diff --git a/tensorflow/lite/g3doc/microcontrollers/build_convert.md b/tensorflow/lite/g3doc/microcontrollers/build_convert.md index b2bd2ce6ac8..cf18b782765 100644 --- a/tensorflow/lite/g3doc/microcontrollers/build_convert.md +++ b/tensorflow/lite/g3doc/microcontrollers/build_convert.md @@ -71,7 +71,7 @@ important to change the array declaration to `const` for better memory efficiency on embedded platforms. For an example of how to include and use a model in your program, see -[`sine_model_data.cc`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/hello_world/sine_model_data.cc) +[`model.cc`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/hello_world/model.cc) in the *Hello World* example. ## Model architecture and training diff --git a/tensorflow/lite/g3doc/microcontrollers/get_started.md b/tensorflow/lite/g3doc/microcontrollers/get_started.md index 5c46701d1fe..96fa336c2ef 100644 --- a/tensorflow/lite/g3doc/microcontrollers/get_started.md +++ b/tensorflow/lite/g3doc/microcontrollers/get_started.md @@ -86,12 +86,10 @@ World README.md The following section walks through the *Hello World* example's [`hello_world_test.cc`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/hello_world/hello_world_test.cc), -which demonstrates how to run inference using TensorFlow Lite for -Microcontrollers. +unit test which demonstrates how to run inference using TensorFlow Lite for +Microcontrollers. It loads the model and runs inference several times. -The test loads the model and then uses it to run inference several times. - -### Include the library headers +### 1. Include the library headers To use the TensorFlow Lite for Microcontrollers library, we must include the following header files: @@ -116,22 +114,20 @@ following header files: - [`version.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/version.h) provides versioning information for the TensorFlow Lite schema. -### Include the model +### 2. Include the model header The TensorFlow Lite for Microcontrollers interpreter expects the model to be -provided as a C++ array. In the *Hello World* example, the model is defined in -`sine_model_data.h` and `sine_model_data.cc`. The header is included with the -following line: +provided as a C++ array. The model is defined in `model.h` and `model.cc` files. +The header is included with the following line: ```C++ -#include "tensorflow/lite/micro/examples/hello_world/sine_model_data.h" +#include "tensorflow/lite/micro/examples/hello_world/model.h" ``` -### Set up the unit test +### 3. Include the unit test framework header -The code we are walking through is a unit test that uses the TensorFlow Lite for -Microcontrollers unit test framework. To load the framework, we include the -following file: +In order to create a unit test, we include the TensorFlow Lite for +Microcontrollers unit test framework by including the following line: ```C++ #include "tensorflow/lite/micro/testing/micro_test.h" @@ -143,11 +139,16 @@ The test is defined using the following macros: TF_LITE_MICRO_TESTS_BEGIN TF_LITE_MICRO_TEST(LoadModelAndPerformInference) { + . // add code here + . +} + +TF_LITE_MICRO_TESTS_END ``` -The remainder of the code demonstrates how to load the model and run inference. +We now discuss the code included in the macro above. -### Set up logging +### 4. Set up logging To set up logging, a `tflite::ErrorReporter` pointer is created using a pointer to a `tflite::MicroErrorReporter` instance: @@ -162,14 +163,14 @@ logs. Since microcontrollers often have a variety of mechanisms for logging, the implementation of `tflite::MicroErrorReporter` is designed to be customized for your particular device. -### Load a model +### 5. Load a model In the following code, the model is instantiated using data from a `char` array, -`g_sine_model_data`, which is declared in `sine_model_data.h`. We then check the -model to ensure its schema version is compatible with the version we are using: +`g_model`, which is declared in `model.h`. We then check the model to ensure its +schema version is compatible with the version we are using: ```C++ -const tflite::Model* model = ::tflite::GetModel(g_sine_model_data); +const tflite::Model* model = ::tflite::GetModel(g_model); if (model->version() != TFLITE_SCHEMA_VERSION) { TF_LITE_REPORT_ERROR(error_reporter, "Model provided is schema version %d not equal " @@ -178,7 +179,7 @@ if (model->version() != TFLITE_SCHEMA_VERSION) { } ``` -### Instantiate operations resolver +### 6. Instantiate operations resolver An [`AllOpsResolver`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/kernels/all_ops_resolver.h) @@ -198,7 +199,7 @@ This is done using a different class, `MicroMutableOpResolver`. You can see how to use it in the *Micro speech* example's [`micro_speech_test.cc`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/micro_speech/micro_speech_test.cc). -### Allocate memory +### 7. Allocate memory We need to preallocate a certain amount of memory for input, output, and intermediate arrays. This is provided as a `uint8_t` array of size @@ -212,7 +213,7 @@ uint8_t tensor_arena[tensor_arena_size]; The size required will depend on the model you are using, and may need to be determined by experimentation. -### Instantiate interpreter +### 8. Instantiate interpreter We create a `tflite::MicroInterpreter` instance, passing in the variables created earlier: @@ -222,7 +223,7 @@ tflite::MicroInterpreter interpreter(model, resolver, tensor_arena, tensor_arena_size, error_reporter); ``` -### Allocate tensors +### 9. Allocate tensors We tell the interpreter to allocate memory from the `tensor_arena` for the model's tensors: @@ -231,7 +232,7 @@ model's tensors: interpreter.AllocateTensors(); ``` -### Validate input shape +### 10. Validate input shape The `MicroInterpreter` instance can provide us with a pointer to the model's input tensor by calling `.input(0)`, where `0` represents the first (and only) @@ -265,7 +266,7 @@ The enum value `kTfLiteFloat32` is a reference to one of the TensorFlow Lite data types, and is defined in [`common.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/c/common.h). -### Provide an input value +### 11. Provide an input value To provide an input to the model, we set the contents of the input tensor, as follows: @@ -276,7 +277,7 @@ input->data.f[0] = 0.; In this case, we input a floating point value representing `0`. -### Run the model +### 12. Run the model To run the model, we can call `Invoke()` on our `tflite::MicroInterpreter` instance: @@ -300,7 +301,7 @@ successfully run. TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, invoke_status); ``` -### Obtain the output +### 12. Obtain the output The model's output tensor can be obtained by calling `output(0)` on the `tflite::MicroInterpreter`, where `0` represents the first (and only) output @@ -327,7 +328,7 @@ float value = output->data.f[0]; TF_LITE_MICRO_EXPECT_NEAR(0., value, 0.05); ``` -### Run inference again +### 13. Run inference again The remainder of the code runs inference several more times. In each instance, we assign a value to the input tensor, invoke the interpreter, and read the @@ -350,7 +351,7 @@ value = output->data.f[0]; TF_LITE_MICRO_EXPECT_NEAR(-0.959, value, 0.05); ``` -### Read the application code +### 14. Read the application code Once you have walked through this unit test, you should be able to understand the example's application code, located in diff --git a/tensorflow/lite/micro/examples/hello_world/BUILD b/tensorflow/lite/micro/examples/hello_world/BUILD index c03069e4ecc..155aaafd98c 100644 --- a/tensorflow/lite/micro/examples/hello_world/BUILD +++ b/tensorflow/lite/micro/examples/hello_world/BUILD @@ -16,12 +16,12 @@ package(default_visibility = ["//visibility:public"]) licenses(["notice"]) # Apache 2.0 cc_library( - name = "sine_model_data", + name = "model", srcs = [ - "sine_model_data.cc", + "model.cc", ], hdrs = [ - "sine_model_data.h", + "model.h", ], build_for_embedded = True, copts = micro_copts(), @@ -33,9 +33,9 @@ tflite_micro_cc_test( "hello_world_test.cc", ], deps = [ + ":model", "//tensorflow/lite:schema_fbs_version", "//tensorflow/lite/micro:micro_framework", - "//tensorflow/lite/micro/examples/hello_world:sine_model_data", "//tensorflow/lite/micro/kernels:all_ops_resolver", "//tensorflow/lite/micro/kernels:micro_ops", "//tensorflow/lite/micro/testing:micro_test", @@ -83,10 +83,10 @@ cc_binary( ], deps = [ ":constants", + ":model", ":output_handler", "//tensorflow/lite:schema_fbs_version", "//tensorflow/lite/micro:micro_framework", - "//tensorflow/lite/micro/examples/hello_world:sine_model_data", "//tensorflow/lite/micro/kernels:all_ops_resolver", "//tensorflow/lite/schema:schema_fbs", ], diff --git a/tensorflow/lite/micro/examples/hello_world/Makefile.inc b/tensorflow/lite/micro/examples/hello_world/Makefile.inc index a4d2da7d891..f1c8859be80 100644 --- a/tensorflow/lite/micro/examples/hello_world/Makefile.inc +++ b/tensorflow/lite/micro/examples/hello_world/Makefile.inc @@ -1,9 +1,9 @@ HELLO_WORLD_TEST_SRCS := \ tensorflow/lite/micro/examples/hello_world/hello_world_test.cc \ -tensorflow/lite/micro/examples/hello_world/sine_model_data.cc +tensorflow/lite/micro/examples/hello_world/model.cc HELLO_WORLD_TEST_HDRS := \ -tensorflow/lite/micro/examples/hello_world/sine_model_data.h +tensorflow/lite/micro/examples/hello_world/model.h OUTPUT_HANDLER_TEST_SRCS := \ tensorflow/lite/micro/examples/hello_world/output_handler_test.cc \ @@ -16,12 +16,12 @@ tensorflow/lite/micro/examples/hello_world/constants.h HELLO_WORLD_SRCS := \ tensorflow/lite/micro/examples/hello_world/main.cc \ tensorflow/lite/micro/examples/hello_world/main_functions.cc \ -tensorflow/lite/micro/examples/hello_world/sine_model_data.cc \ +tensorflow/lite/micro/examples/hello_world/model.cc \ tensorflow/lite/micro/examples/hello_world/output_handler.cc \ tensorflow/lite/micro/examples/hello_world/constants.cc HELLO_WORLD_HDRS := \ -tensorflow/lite/micro/examples/hello_world/sine_model_data.h \ +tensorflow/lite/micro/examples/hello_world/model.h \ tensorflow/lite/micro/examples/hello_world/output_handler.h \ tensorflow/lite/micro/examples/hello_world/constants.h \ tensorflow/lite/micro/examples/hello_world/main_functions.h diff --git a/tensorflow/lite/micro/examples/hello_world/README.md b/tensorflow/lite/micro/examples/hello_world/README.md index 3f3fef67f28..020a7d49e88 100644 --- a/tensorflow/lite/micro/examples/hello_world/README.md +++ b/tensorflow/lite/micro/examples/hello_world/README.md @@ -1,41 +1,32 @@ -# Hello World example +# Hello World Example This example is designed to demonstrate the absolute basics of using [TensorFlow Lite for Microcontrollers](https://www.tensorflow.org/lite/microcontrollers). It includes the full end-to-end workflow of training a model, converting it for -use with TensorFlow Lite, and running inference on a microcontroller. +use with TensorFlow Lite for Microcontrollers for running inference on a +microcontroller. -The sample is built around a model trained to replicate a `sine` function. It -contains implementations for several platforms. In each case, the model is used -to generate a pattern of data that is used to either blink LEDs or control an -animation. +The model is trained to replicate a `sine` function and generates a pattern of +data to either blink LEDs or control an animation, depending on the capabilities +of the device. -![Animation of example running on STM32F746](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/hello_world/images/STM32F746.gif) +![Animation on STM32F746](images/animation_on_STM32F746.gif) ## Table of contents -- [Understand the model](#understand-the-model) - [Deploy to Arduino](#deploy-to-arduino) - [Deploy to ESP32](#deploy-to-esp32) - [Deploy to SparkFun Edge](#deploy-to-sparkfun-edge) - [Deploy to STM32F746](#deploy-to-STM32F746) - [Run the tests on a development machine](#run-the-tests-on-a-development-machine) - -## Understand the model - -The sample comes with a pre-trained model. The code used to train and convert -the model is available as a tutorial in [create_sine_model.ipynb](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/hello_world/create_sine_model.ipynb). - -Walk through this tutorial to understand what the model does, -how it works, and how it was converted for use with TensorFlow Lite for -Microcontrollers. +- [Train your own model](#train-your-own-model) ## Deploy to Arduino The following instructions will help you build and deploy this sample to [Arduino](https://www.arduino.cc/) devices. -![Animation of example running on Arduino MKRZERO](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/hello_world/images/arduino_mkrzero.gif) +![Animation on Arduino MKRZERO](images/animation_on_arduino_mkrzero.gif) The sample has been tested with the following devices: @@ -132,7 +123,7 @@ idf.py --port /dev/ttyUSB0 flash monitor The following instructions will help you build and deploy this sample on the [SparkFun Edge development board](https://sparkfun.com/products/15170). -![Animation of example running on SparkFun Edge](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/hello_world/images/sparkfun_edge.gif) +![Animation on SparkFun Edge](images/animation_on_sparkfun_edge.gif) If you're new to using this board, we recommend walking through the [AI on a microcontroller with TensorFlow Lite and SparkFun Edge](https://codelabs.developers.google.com/codelabs/sparkfun-tensorflow) @@ -272,7 +263,7 @@ The following instructions will help you build and deploy the sample to the [STM32F7 discovery kit](https://os.mbed.com/platforms/ST-Discovery-F746NG/) using [ARM Mbed](https://github.com/ARMmbed/mbed-cli). -![Animation of example running on STM32F746](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/hello_world/images/STM32F746.gif) +![Animation on STM32F746](images/animation_on_STM32F746.gif) Before we begin, you'll need the following: @@ -400,7 +391,14 @@ the trained TensorFlow model, runs some example inputs through it, and got the expected outputs. To understand how TensorFlow Lite does this, you can look at the source in -[hello_world_test.cc](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/hello_world/hello_world_test.cc). +[hello_world_test.cc](hello_world_test.cc). It's a fairly small amount of code that creates an interpreter, gets a handle to a model that's been compiled into the program, and then invokes the interpreter with the model and sample inputs. + +### Train your own model + +So far you have used an existing trained model to run inference on +microcontrollers. If you wish to train your own model, follow the instructions +given in the [train/](train/) directory. + diff --git a/tensorflow/lite/micro/examples/hello_world/create_sine_model.ipynb b/tensorflow/lite/micro/examples/hello_world/create_sine_model.ipynb deleted file mode 100644 index 614cb80b47e..00000000000 --- a/tensorflow/lite/micro/examples/hello_world/create_sine_model.ipynb +++ /dev/null @@ -1,1333 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "create_sine_model.ipynb", - "version": "0.3.2", - "provenance": [], - "collapsed_sections": [], - "toc_visible": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - } - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "sblS7n3zWCWV", - "colab_type": "text" - }, - "source": [ - "**Copyright 2019 The TensorFlow Authors.**" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "0rvUzWmoWMH5", - "colab_type": "code", - "colab": {} - }, - "source": [ - "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "aCZBFzjClURz", - "colab_type": "text" - }, - "source": [ - "# Create and convert a TensorFlow model\n", - "This notebook is designed to demonstrate the process of creating a TensorFlow model and converting it to use with TensorFlow Lite. The model created in this notebook is used in the [hello_world](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/hello_world) sample for [TensorFlow Lite for Microcontrollers](https://www.tensorflow.org/lite/microcontrollers/overview).\n", - "\n", - "\n", - " \n", - " \n", - "
\n", - " Run in Google Colab\n", - " \n", - " View source on GitHub\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dh4AXGuHWeu1", - "colab_type": "text" - }, - "source": [ - "## Import dependencies\n", - "Our first task is to import the dependencies we need. Run the following cell to do so:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "53PBJBv1jEtJ", - "colab_type": "code", - "outputId": "9b035753-60e5-43db-a78d-284ea9de9513", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 479 - } - }, - "source": [ - "# TensorFlow is an open source machine learning library\n", - "import tensorflow as tf\n", - "# Numpy is a math library\n", - "import numpy as np\n", - "# Matplotlib is a graphing library\n", - "import matplotlib.pyplot as plt\n", - "# math is Python's math library\n", - "import math" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "p-PuBEb6CMeo", - "colab_type": "text" - }, - "source": [ - "## Generate data\n", - "Deep learning networks learn to model patterns in underlying data. In this notebook, we're going to train a network to model data generated by a [sine](https://en.wikipedia.org/wiki/Sine) function. This will result in a model that can take a value, `x`, and predict its sine, `y`.\n", - "\n", - "In a real world application, if you needed the sine of `x`, you could just calculate it directly. However, by training a model to do this, we can demonstrate the basic principles of machine learning.\n", - "\n", - "In the [hello_world](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/hello_world) sample for [TensorFlow Lite for Microcontrollers](https://www.tensorflow.org/lite/microcontrollers/overview), we'll use this model to control LEDs that light up in a sequence.\n", - "\n", - "The code in the following cell will generate a set of random `x` values, calculate their sine values, and display them on a graph:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "uKjg7QeMDsDx", - "colab_type": "code", - "outputId": "b17a43c6-eba1-4cc7-8807-14fcf5918d01", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 269 - } - }, - "source": [ - "# We'll generate this many sample datapoints\n", - "SAMPLES = 1000\n", - "\n", - "# Set a \"seed\" value, so we get the same random numbers each time we run this\n", - "# notebook\n", - "np.random.seed(1337)\n", - "\n", - "# Generate a uniformly distributed set of random numbers in the range from\n", - "# 0 to 2π, which covers a complete sine wave oscillation\n", - "x_values = np.random.uniform(low=0, high=2*math.pi, size=SAMPLES)\n", - "\n", - "# Shuffle the values to guarantee they're not in order\n", - "np.random.shuffle(x_values)\n", - "\n", - "# Calculate the corresponding sine values\n", - "y_values = np.sin(x_values)\n", - "\n", - "# Plot our data. The 'b.' argument tells the library to print blue dots.\n", - "plt.plot(x_values, y_values, 'b.')\n", - "plt.show()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iWOlC7W_FYvA", - "colab_type": "text" - }, - "source": [ - "## Add some noise\n", - "Since it was generated directly by the sine function, our data fits a nice, smooth curve.\n", - "\n", - "However, machine learning models are good at extracting underlying meaning from messy, real world data. To demonstrate this, we can add some noise to our data to approximate something more life-like.\n", - "\n", - "In the following cell, we'll add some random noise to each value, then draw a new graph:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "i0FJe3Y-Gkac", - "colab_type": "code", - "outputId": "60b19cdd-c69c-469e-9446-b738a79c1f51", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 269 - } - }, - "source": [ - "# Add a small random number to each y value\n", - "y_values += 0.1 * np.random.randn(*y_values.shape)\n", - "\n", - "# Plot our data\n", - "plt.plot(x_values, y_values, 'b.')\n", - "plt.show()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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WLAiPHSlKKQQnDrpu6rEg1pa/tPZNJPh+Pg/s2FHY60dpPNyMrv5+ntr1uc9x\nkPf66/0jHSUDqLeX17quxOPHC8dAKspoEGNVjApJNx4rYi3+AFtu7qV7Pq/NuRR/mqfn8SYgG0GY\nmEvLENeNKLgBYEUpB/n7Cvs7qzSxdPu4qVLd3f5+PkTanEvxF2tJvx5p9hbM5nFdRMF/ymSS12sA\nWCmXnh6bTTYeGYaxE/+gL/fss/3Pv//9/I++dClvCk1NmrLXqLgxHfHtr1/PVwAi5tksx4iCvYAA\nvmL4xCeAX/8aaGvTvyGlPILdY8famKiI+BPRxwF8DUACwAPGmC8Fnk8B6AYwE0AvgCuMMT+rxHsH\ncYd39/UBL7/sf/7884F9+2wO9/Hj/E+v/7iNjWwE7e32qvHAAWDxYt4MomohH3mErbSdO3kDOXAA\n2LSJx4VOmqRXlsrIdHXx30xbG1+NjldHgbLFn4gSAO4B8DEAhwE8RURbjDEHnWULAfw/Y8zpRHQl\ngC8DuKLc9w4jnbYWmjHA4cP+51taWPwVxcV1Fd56K99fssRmioWRz/PVo8SRVq3i1uGAZgMpxdHV\nxbPCAf6bWb6c+0kdP863tV7kNRvAi8aYl40xxwF8C8BlgTWXAdg49P0/AriAaGxCGsHcfhfP4+fb\n27llAxHfjnVUXaltwqa+dXcPL/xCImGLvl57zf+cZgMpI7Fpk//+hg3AsWPjU+RVCbfPKQBece4f\nBvDhqDXGmEEi+k8AaQC/cBcRUQeADgCYOnXqqE4mmNtvX5uFXi7Dn3hCG3QpTLBHv/j+g64ezwNO\nPZWzfozhYO9nPmPdOwcOhM+N0HYQShjZLLumXY4csd97XgMVeRljugB0AdzYbTSvIbn911/vn8/6\nsY/5i7u0gEsRgoE2wBbbAPz3I8bDN7/Jrp077uA1q1fbS3P5exKf/9tvsyU3OKjZQIqfYPPIMFpa\naj/b51UApzr3pww9FrbmMBElAfwWOPA7JnR08O2SJfwPmkoVX9WrNB7BHv2A7eMvBkQiwVXAAHDn\nnfbx/n5/Smhvr/9vzQ0g699f4xHVobOnJ3qkrLBw4RieGCoj/k8BeC8RvRss8lcC+FRgzRYAVwPI\nAvifAH5gxriXtJTm6z+eUgzBK8HHH2cR37GDhT6fZ2Hv6fGnfBKxG6irK3y2r15hNi7DdehsbeVk\nANfyb27mv6H9+znzR4zYsaJs8R/y4S8BsB2c6rneGPMTIvpbAHuMMVsArAPwd0T0IoC3wBvEmKP/\neMpoyWRY/HfuLMy7TqVsn39jWPg9z24S6t9XgOHnPWcy7DJct467B5955vhnhFXE52+M2QZgW+Cx\nzzvf9wH400q8l6KMF1EjGyUV6Q70AAAfCUlEQVQX+/77bWwgn+cNwPPUv68wwVhSOs2xSID9+W6h\n6Ze+ZF2H4+WtqKmAr6LUGu7Vo/uPOXVqYTaQZABJbCCsfch4/nMr1SXYQmTpUuvmkStFwNaITJ7s\nrzAf61byKv6KUgRB/+3SpfwP7Hb4NIb/cfftC/f/j+eUJqU2EONh5Up/gDfYKmTLFv9j4+E6VPFX\nlCIItoC+6y7r6hHhB/ixgwft2r4+tupmz+bAcJQPWIk3YQFegahwMxgP12HsWzorSiVwW0ATce6+\nBHiD/7g/+pFtI24M1wV89rN8Sa9jIOPLSJPhenqAefMKOxCceGLh+qVLx94wUPFXlCIQ/+2iRXzf\ndfcEMQY4/XT/Y/k8W/zz5+to0DgS1iIkSCbDV4BBLryw8LH9+yt/jkFi6fbRoJoyFmQynOXjVv8G\nIWKr//nn/Y9LFpA2eIsPrs4Ml9bprk2n/e1nPA/4zd/0B4ABzvMfa2In/hpUU6rJyScDr7/uby1y\n2WVs8aXTvHl0d+smUO+EzYCO6sUfXHvFFcBDD/EVoucBTz/tf+0zz+QC1bEmduI/0g6sKOXQ3s6+\n+4EBO/7R5bXX/K6gZJLb9AL+Xi4bNnBzQf3brE+COtPbG14TElzb38/9oeRvZHAQeOopvk/Et889\nx5uFpnqWyHhPw1EaCwncueMf3WpfV/gTCeCP/5i/D/ZyUcOkvgnTmaiOAu5aYwoTBOQK4D3v4eFT\n41UlHruArwTmNKimjBWZDA986ejgv7HbbgPWrOEyfcnkmTePrf6tW9mKS6c51U9Qw6S+KUVnZO0l\nlxQKP8DCn0oBN9/Mt+OVDRY7yx/Qnj7K+OH+rb30EvCd7wCXX849/rdutbn++/axJScj+tTnX/+U\nqjPf+17hYzNmAL/zO7aR23g2o4yl+CvKeNPVxcVcAN8uX86Wv8z/Xb+eBX/NGnuMZqU1DsFusABb\n+M8+y0OAZAb0eBqusXP7KEo1CI7j27+fc/qloGdw0D+Sr5i8cKV+CRZ8tbayMSB4HruBBgZsIHgs\nRzaGoZa/olSAtjYewO3eB/xtH9Jp+7xmpcWXsDTQ3l7g4ouBhx+2mT2AvRoI/n2MByr+ilIBZPDG\nunWc6y++Wyne8TwWALfYR7PS4kkwtVPaOCeT/LuWsZ6TJxf+fYwnKv6KUiGmT2f/7d69wPbtbPGl\nUv5+7mIRJpPA3LksABr8jRfptG345/r5BwfZSJg6ldc88oitCE+lxt8AUPFXlAoRVfgjGT779tnn\nczl2AUyYwOKv1D/ZrH/IT7CBWz4PnHACd3f9m7+xdR8yH3q8DQAVf0WpEBLUy+f5Np3mzJ+tW+2g\nF3leCsLcAfBKfSKiv369v2WzW7Ur3HUXXwG4j8l86PFGxV9RKogb4F282DbwAvj7WbPY2n/ySbtu\nvAN9SuWQ4G5fX3iH16lTueVHLmfbgQTXNTVVJ+aj4q8oFaKnx/5zu60chHwe2LOHRUAsQiJ2Byn1\nibj6RNCDlv5f/ZUN/h89ypY/wIJf7ZiPir+iVAjp4dLfX1jQ46b2BSeArVunQd9ao9gCvKCrb+FC\n9uvv32+rdoULLrBXAF//uv+5aqDirygVQnq4rFgB7NhhN4AzzwRuvNE/wNu1DgcGbFBY2z9Un1Lb\nwrtWf9TvTa4Q8nleVw0ffxCt8FWUCpLJsPi7TdxefJEv/RcssFcAQb/vk08C558P3Hcff7W2atVv\ntQgrwBturbj6crnwtdksZ/jU2ghPFX9FqTCZjL+1g4hCezsHe72Q/7pnn/XHCQYGxr/cX2Hcec0i\n1FHzecPWushVxP338waxaFHtdBtWt4+ijAHt7cDGjYX93sUt9Nhjfuu/VjJAFPt7Ep8/EO0GCq4N\nirp7FQFw9k8tCD+g4q8oY0KUKIhb6Ac/8KeBErGwVDsDRGHc7prXXw8cO8bfHzvGcZlMxt+qA+Dq\nbrkvGVwtLbXbxkPFX1HGiKj2vJkMcNNNwB138P1kkuMBKvi1RzYLPPCA/7F161jUZYqbm9kVTPVs\nbuZ1kv1TS79f9fkryjiTzXI5v2R+rF5t+/yH+ZWj/M3K2CMBXZfBQd4A+vrCRzK6HD/Ouf2PP86b\nQC39DtXyV5RxprvbpnzmciwkgK0ITiY5+0dcCxdcwBam5wH33FP9/PBGorXVVuYKngc8/XR4RW8Q\nIj52vObyloKKv6JUmd27ufJXrMjBQeCWW4CPf5xTBMW1kM8DS5bYiU9KdZg8GXj9dXvfdfUE3T6f\n+hSP9lSfv6IoaG9na99N7Qy6D3bu5C8i/3OSNqriPz709BRa+K++6r/v1m64az0P+P3f5yu6WhzX\nqeKvKONMJsNtAO67zz4mQz0EEZGgmEjfd53/Oz60tvLPvL+f7wc3aSHMNSS/q/Gcy1sKZQV8iei3\niegxInph6PadEetyRLR/6GtLOe+pKHGgvR2YOJFFIpnkgO/atcCUKeHriYA5czhwCOj83/FCUnZv\nu41/R2EFevk8u+IEz7O/q1oUfaHcbJ9bADxujHkvgMeH7odxzBgzY+jr0jLfU1HqHhGVSy8FzjqL\nH5s+HXjzzfD1nmdTBbu7OdOkmPYDSvlkMmzB9/YCn/xk+JpnnrHfex7XctSy8APlu30uA9A69P1G\nAD0A/rLM11SUhuDAAWDzZv5+927gvPMK0woFYzhV8KWXbKsAgK8aaimIGEe6ujjQnstx5XXQRQcM\nX61dq5Rr+f+uMUbi3m8A+N2IdROIaA8R/QsRzYt6MSLqGFq358iRI2WemqLUNps2+e9LgDeMfJ79\nznfc4d8g5s+vfQuzXgirp+jq4grfgQGbrulm9pxySuHvzJj6uBob0fInoh0AJoc89dfuHWOMIaKo\nPe/3jDGvEtF7APyAiA4YY14KLjLGdAHoAoBZs2bVyf6pKKOjrQ149FF73xjgne8EwuwezysMKiYS\nXGm6cqUGfsslrI0zwBZ/sII3meTfQ3Mz8PnP8xWZTPKq1jD20TCi+Btj5kQ9R0T/QUTvMsa8TkTv\nAhDqsTTGvDp0+zIR9QBoAVAg/orSSHR0sBvnjjuswIQJvwR729qAT3/aZp7kcsANN/D3UX3n454V\nVKnPF2zj3N0NvPyyv/8SwOK+ejX7/9Npvu3s9N+vm5+1MWbUXwC+AuCWoe9vAbAqZM07AaSGvj8R\nwAsAzhzptWfOnGkUpRHYtcuYCy80xvMkU9z/lUrxGmOMue668DWJhDG33174us3NxhDx7a5d/HX7\n7fb16pldu4yZOJE/+8SJ5X0m97VSKWOSyfCfM2DM7NnGrF1bufeuNAD2mCL0u9yA75cAfJuIFgL4\nOYA/AwAimgXgOmPMNQA+AGAtEeXBMYYvGWMOlvm+ihIbpNPnzp1sdSYSwDnn8FXASSfxJDDpGNnS\nUlhFCoRXj7ptJI4fB1atArZvL35CVa0TNnRltJ8nk2ELft064D/+A/j5z+1z06YBP/uZvb97N7B3\nL/8OarFtQ7GUJf7GmF4AF4Q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" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Up8Xk_pMH4Rt", - "colab_type": "text" - }, - "source": [ - "## Split our data\n", - "We now have a noisy dataset that approximates real world data. We'll be using this to train our model.\n", - "\n", - "To evaluate the accuracy of the model we train, we'll need to compare its predictions to real data and check how well they match up. This evaluation happens during training (where it is referred to as validation) and after training (referred to as testing) It's important in both cases that we use fresh data that was not already used to train the model.\n", - "\n", - "To ensure we have data to use for evaluation, we'll set some aside before we begin training. We'll reserve 20% of our data for validation, and another 20% for testing. The remaining 60% will be used to train the model. This is a typical split used when training models.\n", - "\n", - "The following code will split our data and then plot each set as a different color:\n" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "nNYko5L1keqZ", - "colab_type": "code", - "outputId": "b9f9c57b-b6aa-4817-8ab4-4a2201732b9a", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 269 - } - }, - "source": [ - "# We'll use 60% of our data for training and 20% for testing. The remaining 20%\n", - "# will be used for validation. Calculate the indices of each section.\n", - "TRAIN_SPLIT = int(0.6 * SAMPLES)\n", - "TEST_SPLIT = int(0.2 * SAMPLES + TRAIN_SPLIT)\n", - "\n", - "# Use np.split to chop our data into three parts.\n", - "# The second argument to np.split is an array of indices where the data will be\n", - "# split. We provide two indices, so the data will be divided into three chunks.\n", - "x_train, x_test, x_validate = np.split(x_values, [TRAIN_SPLIT, TEST_SPLIT])\n", - "y_train, y_test, y_validate = np.split(y_values, [TRAIN_SPLIT, TEST_SPLIT])\n", - "\n", - "# Double check that our splits add up correctly\n", - "assert (x_train.size + x_validate.size + x_test.size) == SAMPLES\n", - "\n", - "# Plot the data in each partition in different colors:\n", - "plt.plot(x_train, y_train, 'b.', label=\"Train\")\n", - "plt.plot(x_test, y_test, 'r.', label=\"Test\")\n", - "plt.plot(x_validate, y_validate, 'y.', label=\"Validate\")\n", - "plt.legend()\n", - "plt.show()\n" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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jCIfDLFy4kC1btnzre5x33nk899xzXHDBBaxZs4ZVq1YBsGPHDjp27EheXh7/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\nYdtguy6kfAgDIvhsus/lZ1JlybxYWcaIxxYjkz5JlBX01FZ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" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "t5McVnHmNiDw", - "colab_type": "text" - }, - "source": [ - "## Design a model\n", - "We're going to build a model that will take an input value (in this case, `x`) and use it to predict a numeric output value (the sine of `x`). This type of problem is called a _regression_.\n", - "\n", - "To achieve this, we're going to create a simple neural network. It will use _layers_ of _neurons_ to attempt to learn any patterns underlying the training data, so it can make predictions.\n", - "\n", - "To begin with, we'll define two layers. The first layer takes a single input (our `x` value) and runs it through 16 neurons. Based on this input, each neuron will become _activated_ to a certain degree based on its internal state (its _weight_ and _bias_ values). A neuron's degree of activation is expressed as a number.\n", - "\n", - "The activation numbers from our first layer will be fed as inputs to our second layer, which is a single neuron. It will apply its own weights and bias to these inputs and calculate its own activation, which will be output as our `y` value.\n", - "\n", - "**Note:** To learn more about how neural networks function, you can explore the [Learn TensorFlow](https://codelabs.developers.google.com/codelabs/tensorflow-lab1-helloworld) codelabs.\n", - "\n", - "The code in the following cell defines our model using [Keras](https://www.tensorflow.org/guide/keras), TensorFlow's high-level API for creating deep learning networks. Once the network is defined, we _compile_ it, specifying parameters that determine how it will be trained:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "gD60bE8cXQId", - "colab_type": "code", - "colab": {} - }, - "source": [ - "# We'll use Keras to create a simple model architecture\n", - "from tensorflow.keras import layers\n", - "model_1 = tf.keras.Sequential()\n", - "\n", - "# First layer takes a scalar input and feeds it through 16 \"neurons\". The\n", - "# neurons decide whether to activate based on the 'relu' activation function.\n", - "model_1.add(layers.Dense(16, activation='relu', input_shape=(1,)))\n", - "\n", - "# Final layer is a single neuron, since we want to output a single value\n", - "model_1.add(layers.Dense(1))\n", - "\n", - "# Compile the model using a standard optimizer and loss function for regression\n", - "model_1.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "O0idLyRLQeGj", - "colab_type": "text" - }, - "source": [ - "## Train the model\n", - "Once we've defined the model, we can use our data to _train_ it. Training involves passing an `x` value into the neural network, checking how far the network's output deviates from the expected `y` value, and adjusting the neurons' weights and biases so that the output is more likely to be correct the next time.\n", - "\n", - "Training runs this process on the full dataset multiple times, and each full run-through is known as an _epoch_. The number of epochs to run during training is a parameter we can set.\n", - "\n", - "During each epoch, data is run through the network in multiple _batches_. Each batch, several pieces of data are passed into the network, producing output values. These outputs' correctness is measured in aggregate and the network's weights and biases are adjusted accordingly, once per batch. The _batch size_ is also a parameter we can set.\n", - "\n", - "The code in the following cell uses the `x` and `y` values from our training data to train the model. It runs for 1000 _epochs_, with 16 pieces of data in each _batch_. We also pass in some data to use for _validation_. As you will see when you run the cell, training can take a while to complete:\n", - "\n" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "p8hQKr4cVOdE", - "colab_type": "code", - "outputId": "3f1a7904-ffcd-4bb7-8bbb-bcd85a132128", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "source": [ - "# Train the model on our training data while validating on our validation set\n", - "history_1 = model_1.fit(x_train, y_train, epochs=1000, batch_size=16,\n", - " validation_data=(x_validate, y_validate))" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Train on 600 samples, validate on 200 samples\n", - "Epoch 1/1000\n", - "600/600 [==============================] - 0s 412us/sample - loss: 0.5016 - mae: 0.6297 - val_loss: 0.4922 - val_mae: 0.6235\n", - "Epoch 2/1000\n", - "600/600 [==============================] - 0s 105us/sample - loss: 0.3905 - mae: 0.5436 - val_loss: 0.4262 - val_mae: 0.5641\n", - "...\n", - "Epoch 998/1000\n", - "600/600 [==============================] - 0s 109us/sample - loss: 0.1535 - mae: 0.3068 - val_loss: 0.1507 - val_mae: 0.3113\n", - "Epoch 999/1000\n", - "600/600 [==============================] - 0s 100us/sample - loss: 0.1545 - mae: 0.3077 - val_loss: 0.1499 - val_mae: 0.3103\n", - "Epoch 1000/1000\n", - "600/600 [==============================] - 0s 132us/sample - loss: 0.1530 - mae: 0.3045 - val_loss: 0.1542 - val_mae: 0.3143\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cRE8KpEqVfaS", - "colab_type": "text" - }, - "source": [ - "## Check the training metrics\n", - "During training, the model's performance is constantly being measured against both our training data and the validation data that we set aside earlier. Training produces a log of data that tells us how the model's performance changed over the course of the training process.\n", - "\n", - "The following cells will display some of that data in a graphical form:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "CmvA-ksoln8r", - "colab_type": "code", - "outputId": "1b834831-81e8-4548-dd8c-f5edf2c3ff43", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 295 - } - }, - "source": [ - "# Draw a graph of the loss, which is the distance between\n", - "# the predicted and actual values during training and validation.\n", - "loss = history_1.history['loss']\n", - "val_loss = history_1.history['val_loss']\n", - "\n", - "epochs = range(1, len(loss) + 1)\n", - "\n", - "plt.plot(epochs, loss, 'g.', label='Training loss')\n", - "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n", - "plt.title('Training and validation loss')\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('Loss')\n", - "plt.legend()\n", - "plt.show()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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TPTOISEuP2SsoeEbGdGCoiFQVkVigJbAwiGW1YGGMMUUIWp+FqmaLyF04D00K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\nXT0QcBrMqzxRnarhQka223i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- "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iOFBSbPcYCN4", - "colab_type": "text" - }, - "source": [ - "## Look closer at the data\n", - "The graph shows the _loss_ (or the difference between the model's predictions and the actual data) for each epoch. There are several ways to calculate loss, and the method we have used is _mean squared error_. There is a distinct loss value given for the training and the validation data.\n", - "\n", - "As we can see, the amount of loss rapidly decreases over the first 25 epochs, before flattening out. This means that the model is improving and producing more accurate predictions!\n", - "\n", - "Our goal is to stop training when either the model is no longer improving, or when the _training loss_ is less than the _validation loss_, which would mean that the model has learned to predict the training data so well that it can no longer generalize to new data.\n", - "\n", - "To make the flatter part of the graph more readable, let's skip the first 50 epochs:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Zo0RYroFZYIV", - "colab_type": "code", - "outputId": "e6841332-0541-44bb-a186-ae5b46781e51", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 295 - } - }, - "source": [ - "# Exclude the first few epochs so the graph is easier to read\n", - "SKIP = 50\n", - "\n", - "plt.plot(epochs[SKIP:], loss[SKIP:], 'g.', label='Training loss')\n", - "plt.plot(epochs[SKIP:], val_loss[SKIP:], 'b.', label='Validation loss')\n", - "plt.title('Training and validation loss')\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('Loss')\n", - "plt.legend()\n", - "plt.show()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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cp4rATDVSKd0zUS6m1iYAmprq3qHm/hxam8yIN2urADg3sP8q4FsRWaKqN9dw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" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "W4EQD-Bb8hLM", - "colab_type": "text" - }, - "source": [ - "## Further metrics\n", - "From the plot, we can see that loss continues to reduce until around 600 epochs, at which point it is mostly stable. This means that there's no need to train our network beyond 600 epochs.\n", - "\n", - "However, we can also see that the lowest loss value is still around 0.155. This means that our network's predictions are off by an average of ~15%. In addition, the validation loss values jump around a lot, and is sometimes even higher.\n", - "\n", - "To gain more insight into our model's performance we can plot some more data. This time, we'll plot the _mean absolute error_, which is another way of measuring how far the network's predictions are from the actual numbers:\n" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Md9E_azmpkZU", - "colab_type": "code", - "outputId": "39b97561-b01d-49f2-c35c-fbd8db663806", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 295 - } - }, - "source": [ - "plt.clf()\n", - "\n", - "# Draw a graph of mean absolute error, which is another way of\n", - "# measuring the amount of error in the prediction.\n", - "mae = history_1.history['mae']\n", - "val_mae = history_1.history['val_mae']\n", - "\n", - "plt.plot(epochs[SKIP:], mae[SKIP:], 'g.', label='Training MAE')\n", - "plt.plot(epochs[SKIP:], val_mae[SKIP:], 'b.', label='Validation MAE')\n", - "plt.title('Training and validation mean absolute error')\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('MAE')\n", - "plt.legend()\n", - "plt.show()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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SYUzCYOTIkQwbNowDDzyQwYMHc+SRRxb9GhdeeCE1NTUMGzYs9dljjz0C24dCIS699FKA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\n2XrB8/Rr3Q/Gt9H90S/4TOee+Dksbl7Ku/cQzO/QY0ghCC17buTnP/w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" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ctawd0CXAVEw", - "colab_type": "text" - }, - "source": [ - "This graph of _mean absolute error_ tells another story. We can see that training data shows consistently lower error than validation data, which means that the network may have _overfit_, or learned the training data so rigidly that it can't make effective predictions about new data.\n", - "\n", - "In addition, the mean absolute error values are quite high, ~0.305 at best, which means some of the model's predictions are at least 30% off. A 30% error means we are very far from accurately modelling the sine wave function.\n", - "\n", - "To get more insight into what is happening, we can plot our network's predictions for the training data against the expected values:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "i13eVIT3B9Mj", - "colab_type": "code", - "outputId": "afc103e2-0beb-4a26-fe18-c0cccc6d3d2a", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 281 - } - }, - "source": [ - "# Use the model to make predictions from our validation data\n", - "predictions = model_1.predict(x_train)\n", - "\n", - "# Plot the predictions along with to the test data\n", - "plt.clf()\n", - "plt.title('Training data predicted vs actual values')\n", - "plt.plot(x_test, y_test, 'b.', label='Actual')\n", - "plt.plot(x_train, predictions, 'r.', label='Predicted')\n", - "plt.legend()\n", - "plt.show()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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QHjo0KwOAFY0xjAhUNa0NCAJLIl5PA6YlaF8BbOvouGeffbYWJA0NuqeihzYh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\nM+9s2wZjx7bft2oVnH8+nH6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- "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Wokallj1D21L", - "colab_type": "text" - }, - "source": [ - "Oh dear! The graph makes it clear that our network has learned to approximate the sine function in a very limited way. From `0 <= x <= 1.1` the line mostly fits, but for the rest of our `x` values it is a rough approximation at best.\n", - "\n", - "The rigidity of this fit suggests that the model does not have enough capacity to learn the full complexity of the sine wave function, so it's only able to approximate it in an overly simplistic way. By making our model bigger, we should be able to improve its performance.\n", - "\n", - "## Change our model\n", - "To make our model bigger, let's add an additional layer of neurons. The following cell redefines our model in the same way as earlier, but with an additional layer of 16 neurons in the middle:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "oW0xus6AF-4o", - "colab_type": "code", - "colab": {} - }, - "source": [ - "model_2 = tf.keras.Sequential()\n", - "\n", - "# First layer takes a scalar input and feeds it through 16 \"neurons\". The\n", - "# neurons decide whether to activate based on the 'relu' activation function.\n", - "model_2.add(layers.Dense(16, activation='relu', input_shape=(1,)))\n", - "\n", - "# The new second layer may help the network learn more complex representations\n", - "model_2.add(layers.Dense(16, activation='relu'))\n", - "\n", - "# Final layer is a single neuron, since we want to output a single value\n", - "model_2.add(layers.Dense(1))\n", - "\n", - "# Compile the model using a standard optimizer and loss function for regression\n", - "model_2.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Dv2SC409Grap", - "colab_type": "text" - }, - "source": [ - "We'll now train the new model. To save time, we'll train for only 600 epochs:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "DPAUrdkmGq1M", - "colab_type": "code", - "outputId": "34ad91e0-229b-479c-bd65-12ad1ed1c660", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "source": [ - "history_2 = model_2.fit(x_train, y_train, epochs=600, batch_size=16,\n", - " validation_data=(x_validate, y_validate))" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Train on 600 samples, validate on 200 samples\n", - "Epoch 1/600\n", - "600/600 [==============================] - 0s 422us/sample - loss: 0.5655 - mae: 0.6259 - val_loss: 0.4104 - val_mae: 0.5509\n", - "Epoch 2/600\n", - "600/600 [==============================] - 0s 111us/sample - loss: 0.3195 - mae: 0.4902 - val_loss: 0.3341 - val_mae: 0.4927\n", - "...\n", - "Epoch 598/600\n", - "600/600 [==============================] - 0s 116us/sample - loss: 0.0124 - mae: 0.0886 - val_loss: 0.0096 - val_mae: 0.0771\n", - "Epoch 599/600\n", - "600/600 [==============================] - 0s 130us/sample - loss: 0.0125 - mae: 0.0900 - val_loss: 0.0107 - val_mae: 0.0824\n", - "Epoch 600/600\n", - "600/600 [==============================] - 0s 109us/sample - loss: 0.0124 - mae: 0.0892 - val_loss: 0.0116 - val_mae: 0.0845\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Mc_CQu2_IvOP", - "colab_type": "text" - }, - "source": [ - "## Evaluate our new model\n", - "Each training epoch, the model prints out its loss and mean absolute error for training and validation. You can read this in the output above (note that your exact numbers may differ): \n", - "\n", - "```\n", - "Epoch 600/600\n", - "600/600 [==============================] - 0s 109us/sample - loss: 0.0124 - mae: 0.0892 - val_loss: 0.0116 - val_mae: 0.0845\n", - "```\n", - "\n", - "You can see that we've already got a huge improvement - validation loss has dropped from 0.15 to 0.015, and validation MAE has dropped from 0.31 to 0.1.\n", - "\n", - "The following cell will print the same graphs we used to evaluate our original model, but showing our new training history:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "SYHGswAJJgrC", - "colab_type": "code", - "outputId": "efcc51f6-f1f1-490a-ffba-ed283586f83e", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 851 - } - }, - "source": [ - "# Draw a graph of the loss, which is the distance between\n", - "# the predicted and actual values during training and validation.\n", - "loss = history_2.history['loss']\n", - "val_loss = history_2.history['val_loss']\n", - "\n", - "epochs = range(1, len(loss) + 1)\n", - "\n", - "plt.plot(epochs, loss, 'g.', label='Training loss')\n", - "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n", - "plt.title('Training and validation loss')\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('Loss')\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "# Exclude the first few epochs so the graph is easier to read\n", - "SKIP = 100\n", - "\n", - "plt.clf()\n", - "\n", - "plt.plot(epochs[SKIP:], loss[SKIP:], 'g.', label='Training loss')\n", - "plt.plot(epochs[SKIP:], val_loss[SKIP:], 'b.', label='Validation loss')\n", - "plt.title('Training and validation loss')\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('Loss')\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "plt.clf()\n", - "\n", - "# Draw a graph of mean absolute error, which is another way of\n", - "# measuring the amount of error in the prediction.\n", - "mae = history_2.history['mae']\n", - "val_mae = history_2.history['val_mae']\n", - "\n", - "plt.plot(epochs[SKIP:], mae[SKIP:], 'g.', label='Training MAE')\n", - "plt.plot(epochs[SKIP:], val_mae[SKIP:], 'b.', label='Validation MAE')\n", - "plt.title('Training and validation mean absolute error')\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('MAE')\n", - "plt.legend()\n", - "plt.show()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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QQpchH8OetrC/NQArf/nJTmYzxgSloE8KAC890hXOLu4daF5du/SFMSYoWVLA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\nJb5/GXWceuKXLao0XuPEUtp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wd0XJtDJx1CHLzqIwr7D5KmdoVpoz8qs5O/OWYNYz2lfLI9kUKZuB8yO3ichE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\nbK98oo69MR6qWJIRXJEviGVV1zlRm8Vrg7oETqI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GnIwcUowU/EE/KUYKORk5cbvWUUcd5f7esmULTz75JKtXr6ZLly786le/ijjOICUlxf1t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\nmnWfRReAMQBvBnAYgIeIaD0z75QTEVEvgF4AmDFjRt2JjIJ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- "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "f86dWOyZKmN9", - "colab_type": "text" - }, - "source": [ - "Great results! From these graphs, we can see several exciting things:\n", - "\n", - "* Our network has reached its peak accuracy much more quickly (within 200 epochs instead of 600)\n", - "* The overall loss and MAE are much better than our previous network\n", - "* Metrics are better for validation than training, which means the network is not overfitting\n", - "\n", - "The reason the metrics for validation are better than those for training is that validation metrics are calculated at the end of each epoch, while training metrics are calculated throughout the epoch, so validation happens on a model that has been trained slightly longer.\n", - "\n", - "This all means our network seems to be performing well! To confirm, let's check its predictions against the test dataset we set aside earlier:\n" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "lZfztKKyhLxX", - "colab_type": "code", - "outputId": "b792a12e-713d-4b07-9f8e-de0d059d5cdb", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 298 - } - }, - "source": [ - "# Calculate and print the loss on our test dataset\n", - "loss = model_2.evaluate(x_test, y_test)\n", - "\n", - "# Make predictions based on our test dataset\n", - "predictions = model_2.predict(x_test)\n", - "\n", - "# Graph the predictions against the actual values\n", - "plt.clf()\n", - "plt.title('Comparison of predictions and actual values')\n", - "plt.plot(x_test, y_test, 'b.', label='Actual')\n", - "plt.plot(x_test, predictions, 'r.', label='Predicted')\n", - "plt.legend()\n", - "plt.show()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "200/200 [==============================] - 0s 146us/sample - loss: 0.0124 - mae: 0.0907\n" - ], - "name": "stdout" - }, - { - "output_type": "display_data", - "data": { - "image/png": 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dUQEbN6qtTVH6yGB1+D4ALDHGtInIZGAx8K3EnUQkCAQBRowYMUhJ6511TWGW\ntR9JJR1goG3WQn7/rxDt7U5XTb+1taf/vjJAxE+YvHChnTdz8eKUtjaNnaQoPcmF+L8FxNfkh7vr\nujDGtMYt3gbMSnYiY0wj0Ag2tk8O0pYTvrN2FpV0dM3IVUkH+70doiphknH11x9EHCfW8R6J2Gkg\nm5p6/AFlP12moqQgF2af54FRIrKPiFQBZwL3x+8gIrvHLZ4EvJyD6w4O4TA1K2LZMUAUH/tOCpTt\nZCwFQyAAFTbuvzGGyIKFPcw/2hejKMnJuuZvjOkUkSnAI4AfWGiMeUlE/gs7ndj9wCUichLQCXwI\nnJvtdQeKHiaCpiZM1M7Da4VfeOasmzkyaNVeRT+POA7vHD+RXZfOx48h2hFhU1OIveP+lMRBYtoX\noyiWnNj8jTEPAQ8lrPtV3O8GoCEX18oVyezASU0ECcfdz8m88rUgRw5yepXk/G23er7HYippp4Mq\nniBAfdz2ZLGUFEUp06ieqeblTWoiqK8nWllFBKGNKm6smqa1xwJiVL3DCVXLmS5XcULVckbVW3XX\nAXeKkp6yDO+QTOQdJ4WJwHHwPxHijaYQTxBgRr2jtccCwnFgRsghFHKYEYjNjOa14Px+Ow98ZydU\nVmoUDkXxKEvxT2UHTmkicBz2dpxu5gSlcEj0soov3OOjcLS3J3UIUpSypCzFP50d2LEz7gIBelr8\nlUIjWd9NfOEO3QsARVEsZSf+8WIRCMRc/xzHboweeRTS0Y6prML3hM4PWMik8uF3HHhudpjW5hCb\nRwc480anW0hoRVHKTPwTbcEi1hbsTaT+lRubGNfRZgdzdbTx7qwmdrtXxb9QSdV3QzhM7VT3j36q\niud/v5wHWx319lGUOEpa/BNNAvFiEY3afYyxwTn/56Iw10de6Hb822/DbknOoxQGKX34E0qF2tYQ\ntQ36xylKPCUr/slMAvFiEe8F4hDmkch4qmgDIAJ0UEXlpHoND1DApOy7STOySwtyRbGUrPgnMwk0\nNMTEoqYGfvITu++RhKiinQqidOLj1eFHE71yOrVBhxkzUpgWlIIgaTylJKVCOGw9fRYtsgV+RQVM\nnGj7APT/VMqRkh3k5VX+EkMse7NCtbZaQTcGniBA1F9FRPxIdTVfu8sKf7rzKAVO3PRfm86+nOHf\n3IszbjmSMW1hIhFr6ps/v/sgP0UpJ8SYggme2Y26ujrT0tKS1TnSNfHDYVhxxOWcHLmH+/yncdzN\np1DbmnxnNRUUMZdfjpkVCyLbgZ8jeYpnXTdevx/OPx9GjND/VykNRGSVMaau1/1KWfzTkiAKMm0a\nzJw5cNdT8sOoUZj167vCcUe7+QmrAAAdUklEQVSBPxxwLT9e30Ak0tPrS/t0lGInU/EvWbNPr/zx\njwBdosA99+QtKcoActppXRFZDYDPz49uCxAK2XDc551nhV9DPivlRsl2+MbTw2wTDhN9+50uUQDY\ndMhpDM9bCpUBw23NyR//CF/6EnLddeA4OMTiAC1erCGflfKj5M0+ia6az80OU9s8nejfHsVnokSB\npxjHM9c+QUNBBZ1WBoQkHTjap6OUEpmafUq+5h/v8jm2Lcz+U8ZDpA0x1q2znWp+XXUdMwL5Tqky\n4KQYtJFu+k0tGJRSpeTFPxCwnXrRKAQkREWkHaJRxOfjk7qjeWDsdA3TXC6kjAdhSRR6HeCnlDIl\nL/5gvTkAnvIFiPqq8Hfat3mn2dOp17e5fIgf+VtRARs3QjhMGIemJli40JYLntD3UlYoSlFT8t4+\noRDUdYS53MwgEoE/TNRZ18sWb+Tv+efb0X233krkqPE0BMLMn99T6HWAn1LKlGTNP775/s2XGpkW\nvRAfUTqjFbwy5kkIas9u2eJF+PNmeom2cxghnjC2IiASE3qd/1cpZUpO/OPttIf5wizvuAAfBgEq\n6KTm+isg+ES+k6nkkzjzj4iPkyNL+UBqWFwV7BHvJ11nsKIUMyVn9om3057Z0dQl/B4VG1/LV9KU\nQsGr0n/3u/g6O/i6WcktZjIbjzybefNU7JXyoOTE36vUHeYLM5FF3Ud3Am8Fzspf4pTCwXHg888B\nO8pbgF2X/QEaG/OaLEUZLEpO/L1K3dVHh6j2dXbV+j/1fYHV357GmEc0fo/iMmFCz3XNzYOfDkXJ\nAyUn/gDOmkYCm5cifh/4/cjQoWy/4q8q/Ep3gkE4K6EluM02GuNZKQtKT/wbG2HyZFi5Ejo64Lvf\nVbdOJTV33GED+3/jG9b3/4EHNMi/UhaUnvg3N3fZ9w1Yu64Kv5KOYBBOOcX6/mt4T6VMKDnxf220\nteOahGVFSYuO6FLKjJLz879rxyAbBE41zdwrExi5YxAd0qWkIjYg0MGJn+DZq/lrq1EpUUpO/AMB\nGD8kyIL2oI3REsh3ipRCpWfgNgcngEZzU/LKYEWSLTnx1yH5SqYkDdxG3MqtW6GpSR8iZdAYzEiy\nJWfzB3uzGhr0nVXSk9TMHwhYrx+wHcALF6rnjzJoJKuQDBQlKf6KkgleK7FbkFfHgYkTMW4ccNMZ\ngVCIxkY49tjYAOBwGGbM0HJByS2D6XeQE7OPiBwH3Aj4gduMMdclbK8GmoCDgVbgDGPMhlxcW1Gy\nIVngtqVfqOfbZjGVtNMRrWLRSwGm/MFuW7YMXnsNbrpJuwWU3JBo4x8ss3XW4i8ifmAucAywCXhe\nRO43xqyN220S8JEx5ssiciYwEzgj22srSrYkm73r+//tcDDLCRCilRr2ezjEocCz2Dfxnnt6n+RF\np39UMiGVjX8wnplc1Py/Aaw3xrwOICJ3AicD8eJ/MjDd/X03MEdExBTq7PFKWZDsxfNC/XtCv5zx\nVH/UzkVUMZ7lPIvDaad1r/knNs11+kclU5LZ+LdbE6a1OUTNhAC1wYF7cHIh/nsCb8YtbwIOSbWP\nMaZTRD4GaoAP4ncSkSAQBBgxYkQOkqYoqUn24gUCUF0NbW0wnhBDTDs+E2GIr53zvxRi4s+drgHB\nqWr2Ov2jkimejb+tDXw+GBVq5KvLLsJHlPZlVazh8QErAAqqw9cY02iMqTPG1O2yyy75To5S4iTr\nXOuKCns1nDEvgG+I3cHnE84btpQgtsc3nUeZDhZWMsVxYPZsK/xf7wxz8rKL8BPBh6GaNjoWNA3Y\ntXNR838L2Ctuebi7Ltk+m0SkAtgB2/GrKHkjVedazObqQO1ymDULli61wQJXrrQ9vjNTR4jVsSZK\nX2httV7F40wIH5Fuk0/tvsfAXTcX4v88MEpE9sGK/JnADxP2uR84BwgD3wMeU3u/Ugj02rkWN+lL\nFzfcYO0+aQ7U6R+VTAkE4HB/mL2jG+k0lfjoACDqr2D3afUDdt2sxd+14U8BHsG6ei40xrwkIv8F\ntBhj7gcWAP8jIuuBD7EFhKIUBxMmWB9PD2PUkK/kDGdNI491XoSYCKaiEjnxFNhtN/zxk0kPADnx\n8zfGPAQ8lLDuV3G/twKn5+JaijLoBIPW1HPDDVb4Kyth40br1qMFgJIN4TBceCG+aNQud3bwwtu7\n0TZt3oA/WgXV4asoBcvMmbBihZ0oSARuvVUnfVGyp6kJPOF3WblycB4tFX9FyRTHgREjMB2dEIlg\n2tp5oymkYR6UnGCACD4WUz8o8wmp+CtKH1hTE2BLtIpOfHREfVx3aw1XXqmNAKWf1NcTrawmitCJ\nnwuZx0qfMyguwir+ipIh4TBc1uwwldlE8eEjwm8jU/l6JKwzPyr9w3G4Y9LjXCnXMI6nWOgLcvTR\ngzMqvOTi+SvKQOCFbGhrg2m04sNQQRRDO9+SEH+vcnQwl5IZCYGfRtU7XLDYob0dqqtg+vTiie2j\nKCWPF7IhGoUnJUCnVOGnHb+/ggljNnLmpDC16vmj9EY4bEW/o8N6jYVCOI6Tl0GBavZRlAyID9nw\n4hCHdfOWI8Hz8Ylh7KpbqZ2qRn8lA2bNsrUIY+x3kw3fkI8JqFT8FSUDEid+qQ1azx8ikcGZdkkp\nfsJheOCBfKeiCzX7KEqG9AjZ4DUHUsV2VpR4QiFb4/fw+6F+4MI39IaKv6L0F43gpvQFN164aWsj\nip8N/zGHffP4zEihxlerq6szLS0t+U6GoihKzljTGObPF4d4LBrghWpnQFw6RWSVMaaut/205q8o\nijJIPNjqcE3UIRoFX1usmygfjUcVf0UZIHQeXyWRmppYKJ9oFDZvzt+Unyr+ipJrwmHeaArRsDDA\nioij8/gqXbS22lm7olH7vXp1/qb8VFdPRckl7lDgveZfyUPt4zX0g9INb45ov99+T5iQvyk/teav\nKLnEHQrsMxGq2cJspnK5fzY1NQ4zZqgJqNxJ5iBWW5sf86B6+yhKLgmH4aijoK0N782KVFRztO9x\nVkQcKipg4kTr3q2FQGlRKH08mXr7qNlHUXKJ41h1B8T9+DrbOawjRCRiA8PNn68hoEsNL/DfX34Z\n5uFxM1jTWPh/roq/ouSa+nprwPWorOLpygAidtEL66L9AKVDUxOcvaWRx6JH8qvOX7L/lMIv3VX8\nFSXXOI5V9gsugAsuwPfE48wIOUyenL/OPWXgCIdh7YIwc7iYSjqoIEpFpK3gS3ft8FWUgSAxEFDY\nxoG76Sbr7ldTE9MGtf0XN6EQ/KCjCT8RBDsdo/j9SUv3QukXABV/RRlwwmH4n3GNnNzZzH0VExg9\nN8gll8QG9jz+eP6FQOk/J9aEGcUifBgMYHx+ZM6cHn+q1y+QjwFdyVCzj6LkmHCYbpO6fzSrkbmd\nk/k2y5jbOZkPrm2krc3a/tvaukK6K0VKbWuIal8nAiCCL3g+BIM99vMmBCqUCOBa81eUHJK0dvd2\nM0CXSWDcB81AT3FQipRAAKm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" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3h7IcvuOOS4J", - "colab_type": "text" - }, - "source": [ - "Much better! The evaluation metrics we printed show that the model has a low loss and MAE on the test data, and the predictions line up visually with our data fairly well.\n", - "\n", - "The model isn't perfect; its predictions don't form a smooth sine curve. For instance, the line is almost straight when `x` is between 4.2 and 5.2. If we wanted to go further, we could try further increasing the capacity of the model, perhaps using some techniques to defend from overfitting.\n", - "\n", - "However, an important part of machine learning is knowing when to quit, and this model is good enough for our use case - which is to make some LEDs blink in a pleasing pattern.\n", - "\n", - "## Convert to TensorFlow Lite\n", - "We now have an acceptably accurate model in-memory. However, to use this with TensorFlow Lite for Microcontrollers, we'll need to convert it into the correct format and download it as a file. To do this, we'll use the [TensorFlow Lite Converter](https://www.tensorflow.org/lite/convert). The converter outputs a file in a special, space-efficient format for use on memory-constrained devices.\n", - "\n", - "Since this model is going to be deployed on a microcontroller, we want it to be as tiny as possible! One technique for reducing the size of models is called [quantization](https://www.tensorflow.org/lite/performance/post_training_quantization). It reduces the precision of the model's weights, which saves memory, often without much impact on accuracy. Quantized models also run faster, since the calculations required are simpler.\n", - "\n", - "The TensorFlow Lite Converter can apply quantization while it converts the model. In the following cell, we'll convert the model twice: once with quantization, once without:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "1muAoUm8lSXL", - "colab_type": "code", - "colab": {} - }, - "source": [ - "# Convert the model to the TensorFlow Lite format without quantization\n", - "converter = tf.lite.TFLiteConverter.from_keras_model(model_2)\n", - "tflite_model = converter.convert()\n", - "\n", - "# Save the model to disk\n", - "open(\"sine_model.tflite\", \"wb\").write(tflite_model)\n", - "\n", - "# Convert the model to the TensorFlow Lite format with quantization\n", - "converter = tf.lite.TFLiteConverter.from_keras_model(model_2)\n", - "converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]\n", - "tflite_model = converter.convert()\n", - "\n", - "# Save the model to disk\n", - "open(\"sine_model_quantized.tflite\", \"wb\").write(tflite_model)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "L_vE-ZDkHVxe", - "colab_type": "text" - }, - "source": [ - "## Test the converted models\n", - "To prove these models are still accurate after conversion and quantization, we'll use both of them to make predictions and compare these against our test results:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "-J7IKlXiYVPz", - "colab_type": "code", - "outputId": "0c10f56c-dbd7-4cc3-e332-30ad673769e5", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 281 - } - }, - "source": [ - "# Instantiate an interpreter for each model\n", - "sine_model = tf.lite.Interpreter('sine_model.tflite')\n", - "sine_model_quantized = tf.lite.Interpreter('sine_model_quantized.tflite')\n", - "\n", - "# Allocate memory for each model\n", - "sine_model.allocate_tensors()\n", - "sine_model_quantized.allocate_tensors()\n", - "\n", - "# Get the input and output tensors so we can feed in values and get the results\n", - "sine_model_input = sine_model.tensor(sine_model.get_input_details()[0][\"index\"])\n", - "sine_model_output = sine_model.tensor(sine_model.get_output_details()[0][\"index\"])\n", - "sine_model_quantized_input = sine_model_quantized.tensor(sine_model_quantized.get_input_details()[0][\"index\"])\n", - "sine_model_quantized_output = sine_model_quantized.tensor(sine_model_quantized.get_output_details()[0][\"index\"])\n", - "\n", - "# Create arrays to store the results\n", - "sine_model_predictions = np.empty(x_test.size)\n", - "sine_model_quantized_predictions = np.empty(x_test.size)\n", - "\n", - "# Run each model's interpreter for each value and store the results in arrays\n", - "for i in range(x_test.size):\n", - " sine_model_input().fill(x_test[i])\n", - " sine_model.invoke()\n", - " sine_model_predictions[i] = sine_model_output()[0]\n", - "\n", - " sine_model_quantized_input().fill(x_test[i])\n", - " sine_model_quantized.invoke()\n", - " sine_model_quantized_predictions[i] = sine_model_quantized_output()[0]\n", - "\n", - "# See how they line up with the data\n", - "plt.clf()\n", - "plt.title('Comparison of various models against actual values')\n", - "plt.plot(x_test, y_test, 'bo', label='Actual')\n", - "plt.plot(x_test, predictions, 'ro', label='Original predictions')\n", - "plt.plot(x_test, sine_model_predictions, 'bx', label='Lite predictions')\n", - "plt.plot(x_test, sine_model_quantized_predictions, 'gx', label='Lite quantized predictions')\n", - "plt.legend()\n", - "plt.show()\n" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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JGHCbOB06dGDv3r31tj+VDrJpoEb+9YQ9G6DnyW5kDf6WAen9iQo/TZepA0kf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\n6q8/GFJf/kBVSSLw8suxsXqX9vNXKk75u9JOM1b7h2Gf3sy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- "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jWxvLGexKv0D", - "colab_type": "text" - }, - "source": [ - "We can see from the graph that the predictions for the original model, the converted model, and the quantized model are all close enough to be indistinguishable. This means that our quantized model is ready to use!\n", - "\n", - "We can print the difference in file size:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "6r42iBnULP4X", - "colab_type": "code", - "outputId": "afe526c9-498d-498e-d768-1edfbf21e870", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 68 - } - }, - "source": [ - "import os\n", - "basic_model_size = os.path.getsize(\"sine_model.tflite\")\n", - "print(\"Basic model is %d bytes\" % basic_model_size)\n", - "quantized_model_size = os.path.getsize(\"sine_model_quantized.tflite\")\n", - "print(\"Quantized model is %d bytes\" % quantized_model_size)\n", - "difference = basic_model_size - quantized_model_size\n", - "print(\"Difference is %d bytes\" % difference)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Basic model is 2656 bytes\n", - "Quantized model is 2640 bytes\n", - "Difference is 16 bytes\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "C2vpZE9ZshVH", - "colab_type": "text" - }, - "source": [ - "Our quantized model is only 16 bytes smaller than the original version, which only a tiny reduction in size! At around 2.6 kilobytes, this model is already so small that the weights make up only a small fraction of the overall size, meaning quantization has little effect.\n", - "\n", - "More complex models have many more weights, meaning the space saving from quantization will be much higher, approaching 4x for most sophisticated models.\n", - "\n", - "Regardless, our quantized model will take less time to execute than the original version, which is important on a tiny microcontroller!\n", - "\n", - "## Write to a C file\n", - "The final step in preparing our model for use with TensorFlow Lite for Microcontrollers is to convert it into a C source file. You can see an example of this format in [`hello_world/sine_model_data.cc`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/hello_world/sine_model_data.cc).\n", - "\n", - "To do so, we can use a command line utility named [`xxd`](https://linux.die.net/man/1/xxd). The following cell runs `xxd` on our quantized model and prints the output:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "l4-WhtGpvb-E", - "colab_type": "code", - "outputId": "f975721f-bdd1-440a-93af-55f13c4c8690", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 3808 - } - }, - "source": [ - "# Install xxd if it is not available\n", - "!apt-get -qq install xxd\n", - "# Save the file as a C source file\n", - "!xxd -i sine_model_quantized.tflite > sine_model_quantized.cc\n", - "# Print the source file\n", - "!cat sine_model_quantized.cc" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "unsigned char sine_model_quantized_tflite[] = {\n", - " 0x18, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x0e, 0x00,\n", - " 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x14, 0x00,\n", - " 0x0e, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x0a, 0x00, 0x00,\n", - " 0xb8, 0x05, 0x00, 0x00, 0xa0, 0x05, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,\n", - 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"\n" - ] - } - ] -} \ No newline at end of file diff --git a/tensorflow/lite/micro/examples/hello_world/hello_world_test.cc b/tensorflow/lite/micro/examples/hello_world/hello_world_test.cc index 3d1155ef41e..46976f30ecb 100644 --- a/tensorflow/lite/micro/examples/hello_world/hello_world_test.cc +++ b/tensorflow/lite/micro/examples/hello_world/hello_world_test.cc @@ -14,7 +14,7 @@ limitations under the License. ==============================================================================*/ // #include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/examples/hello_world/sine_model_data.h" +#include "tensorflow/lite/micro/examples/hello_world/model.h" #include "tensorflow/lite/micro/kernels/all_ops_resolver.h" #include "tensorflow/lite/micro/micro_error_reporter.h" #include "tensorflow/lite/micro/micro_interpreter.h" @@ -31,7 +31,7 @@ TF_LITE_MICRO_TEST(LoadModelAndPerformInference) { // Map the model into a usable data structure. This doesn't involve any // copying or parsing, it's a very lightweight operation. - const tflite::Model* model = ::tflite::GetModel(g_sine_model_data); + const tflite::Model* model = ::tflite::GetModel(g_model); if (model->version() != TFLITE_SCHEMA_VERSION) { TF_LITE_REPORT_ERROR(error_reporter, "Model provided is schema version %d not equal " @@ -43,8 +43,13 @@ TF_LITE_MICRO_TEST(LoadModelAndPerformInference) { tflite::ops::micro::AllOpsResolver resolver; // Create an area of memory to use for input, output, and intermediate arrays. - // `arena_used_bytes` can be used to retrieve the optimal size. - const int tensor_arena_size = 2208 + 16 + 100 /* some reserved space */; + + // Minimum arena size, at the time of writing. After allocating tensors + // you can retrieve this value by invoking interpreter.arena_used_bytes(). + const int model_arena_size = 2352; + /* Extra headroom for model + alignment + future interpreter changes */ + const int extra_arena_size = 560 + 16 + 100; + const int tensor_arena_size = model_arena_size + extra_arena_size; uint8_t tensor_arena[tensor_arena_size]; // Build an interpreter to run the model with @@ -53,11 +58,10 @@ TF_LITE_MICRO_TEST(LoadModelAndPerformInference) { // Allocate memory from the tensor_arena for the model's tensors TF_LITE_MICRO_EXPECT_EQ(interpreter.AllocateTensors(), kTfLiteOk); - // At the time of writing, the hello world model uses 2208 bytes, we leave - // 100 bytes head room here to make the test less fragile and in the same - // time, alert for substantial increase. - TF_LITE_MICRO_EXPECT_LE(interpreter.arena_used_bytes(), 2208 + 100); + // Alert for substantial increase in arena size usage. + TF_LITE_MICRO_EXPECT_LE(interpreter.arena_used_bytes(), + model_arena_size + 100); // Obtain a pointer to the model's input tensor TfLiteTensor* input = interpreter.input(0); diff --git a/tensorflow/lite/micro/examples/hello_world/images/STM32F746.gif b/tensorflow/lite/micro/examples/hello_world/images/animation_on_STM32F746.gif similarity index 100% rename from tensorflow/lite/micro/examples/hello_world/images/STM32F746.gif rename to tensorflow/lite/micro/examples/hello_world/images/animation_on_STM32F746.gif diff --git a/tensorflow/lite/micro/examples/hello_world/images/arduino_mkrzero.gif b/tensorflow/lite/micro/examples/hello_world/images/animation_on_arduino_mkrzero.gif similarity index 100% rename from tensorflow/lite/micro/examples/hello_world/images/arduino_mkrzero.gif rename to tensorflow/lite/micro/examples/hello_world/images/animation_on_arduino_mkrzero.gif diff --git a/tensorflow/lite/micro/examples/hello_world/images/sparkfun_edge.gif b/tensorflow/lite/micro/examples/hello_world/images/animation_on_sparkfun_edge.gif similarity index 100% rename from tensorflow/lite/micro/examples/hello_world/images/sparkfun_edge.gif rename to tensorflow/lite/micro/examples/hello_world/images/animation_on_sparkfun_edge.gif diff --git a/tensorflow/lite/micro/examples/hello_world/images/model_architecture.png b/tensorflow/lite/micro/examples/hello_world/images/model_architecture.png new file mode 100644 index 00000000000..792d18fab4b Binary files /dev/null and b/tensorflow/lite/micro/examples/hello_world/images/model_architecture.png differ diff --git a/tensorflow/lite/micro/examples/hello_world/main_functions.cc b/tensorflow/lite/micro/examples/hello_world/main_functions.cc index 33180d4554c..404c8542432 100644 --- a/tensorflow/lite/micro/examples/hello_world/main_functions.cc +++ b/tensorflow/lite/micro/examples/hello_world/main_functions.cc @@ -1,4 +1,4 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -16,8 +16,8 @@ limitations under the License. #include "tensorflow/lite/micro/examples/hello_world/main_functions.h" #include "tensorflow/lite/micro/examples/hello_world/constants.h" +#include "tensorflow/lite/micro/examples/hello_world/model.h" #include "tensorflow/lite/micro/examples/hello_world/output_handler.h" -#include "tensorflow/lite/micro/examples/hello_world/sine_model_data.h" #include "tensorflow/lite/micro/kernels/all_ops_resolver.h" #include "tensorflow/lite/micro/micro_error_reporter.h" #include "tensorflow/lite/micro/micro_interpreter.h" @@ -49,7 +49,7 @@ void setup() { // Map the model into a usable data structure. This doesn't involve any // copying or parsing, it's a very lightweight operation. - model = tflite::GetModel(g_sine_model_data); + model = tflite::GetModel(g_model); if (model->version() != TFLITE_SCHEMA_VERSION) { TF_LITE_REPORT_ERROR(error_reporter, "Model provided is schema version %d not equal " diff --git a/tensorflow/lite/micro/examples/hello_world/model.cc b/tensorflow/lite/micro/examples/hello_world/model.cc new file mode 100644 index 00000000000..232e4a14115 --- /dev/null +++ b/tensorflow/lite/micro/examples/hello_world/model.cc @@ -0,0 +1,250 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// Automatically created from a TensorFlow Lite flatbuffer using the command: +// xxd -i model.tflite > model.cc + +// This is a standard TensorFlow Lite model file that has been converted into a +// C data array, so it can be easily compiled into a binary for devices that +// don't have a file system. + +// See train/README.md for a full description of the creation process. + +#include "tensorflow/lite/micro/examples/hello_world/model.h" + +// We need to keep the data array aligned on some architectures. +#ifdef __has_attribute +#define HAVE_ATTRIBUTE(x) __has_attribute(x) +#else +#define HAVE_ATTRIBUTE(x) 0 +#endif +#if HAVE_ATTRIBUTE(aligned) || (defined(__GNUC__) && !defined(__clang__)) +#define DATA_ALIGN_ATTRIBUTE __attribute__((aligned(4))) +#else +#define DATA_ALIGN_ATTRIBUTE +#endif + +const unsigned char g_model[] DATA_ALIGN_ATTRIBUTE = { + 0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x12, 0x00, + 0x1c, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x14, 0x00, + 0x00, 0x00, 0x18, 0x00, 0x12, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, + 0x60, 0x09, 0x00, 0x00, 0xa8, 0x02, 0x00, 0x00, 0x90, 0x02, 0x00, 0x00, + 0x3c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x04, 0x00, 0x08, 0x00, + 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x0b, 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0x00, 0x00, 0x00, + 0x24, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x1a, 0xde, 0x0a, 0x3c, + 0x01, 0x00, 0x00, 0x00, 0x66, 0x64, 0x87, 0x3f, 0x01, 0x00, 0x00, 0x00, + 0x13, 0x42, 0x8d, 0xbf, 0x0d, 0x00, 0x00, 0x00, 0x49, 0x64, 0x65, 0x6e, + 0x74, 0x69, 0x74, 0x79, 0x5f, 0x69, 0x6e, 0x74, 0x38, 0x00, 0x00, 0x00, + 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x03, 0x00, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, + 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0a, 0x00, 0x0e, 0x00, 0x07, 0x00, + 0x00, 0x00, 0x08, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, + 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x06, 0x00, 0x05, 0x00, + 0x06, 0x00, 0x00, 0x00, 0x00, 0x72, 0x0a, 0x00, 0x0c, 0x00, 0x07, 0x00, + 0x00, 0x00, 0x08, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, + 0x04, 0x00, 0x00, 0x00}; +const int g_model_len = 2512; diff --git a/tensorflow/lite/micro/examples/hello_world/sine_model_data.h b/tensorflow/lite/micro/examples/hello_world/model.h similarity index 59% rename from tensorflow/lite/micro/examples/hello_world/sine_model_data.h rename to tensorflow/lite/micro/examples/hello_world/model.h index b7087c6bd9e..488f47b3afd 100644 --- a/tensorflow/lite/micro/examples/hello_world/sine_model_data.h +++ b/tensorflow/lite/micro/examples/hello_world/model.h @@ -1,4 +1,4 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -13,15 +13,19 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +// Automatically created from a TensorFlow Lite flatbuffer using the command: +// xxd -i model.tflite > model.cc + // This is a standard TensorFlow Lite model file that has been converted into a // C data array, so it can be easily compiled into a binary for devices that -// don't have a file system. It was created using the command: -// xxd -i sine_model.tflite > sine_model_data.cc +// don't have a file system. -#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_SINE_MODEL_DATA_H_ -#define TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_SINE_MODEL_DATA_H_ +// See train/README.md for a full description of the creation process. -extern const unsigned char g_sine_model_data[]; -extern const int g_sine_model_data_len; +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_MODEL_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_MODEL_H_ -#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_SINE_MODEL_DATA_H_ +extern const unsigned char g_model[]; +extern const int g_model_len; + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_MODEL_H_ diff --git a/tensorflow/lite/micro/examples/hello_world/sine_model_data.cc b/tensorflow/lite/micro/examples/hello_world/sine_model_data.cc deleted file mode 100644 index 7252479fecd..00000000000 --- a/tensorflow/lite/micro/examples/hello_world/sine_model_data.cc +++ /dev/null @@ -1,255 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// Automatically created from a TensorFlow Lite flatbuffer using the command: -// xxd -i sine_model.tflite > sine_model_data.cc -// See the README for a full description of the creation process. - -#include "tensorflow/lite/micro/examples/hello_world/sine_model_data.h" - -// We need to keep the data array aligned on some architectures. -#ifdef __has_attribute -#define HAVE_ATTRIBUTE(x) __has_attribute(x) -#else -#define HAVE_ATTRIBUTE(x) 0 -#endif -#if HAVE_ATTRIBUTE(aligned) || (defined(__GNUC__) && !defined(__clang__)) -#define DATA_ALIGN_ATTRIBUTE __attribute__((aligned(4))) -#else -#define DATA_ALIGN_ATTRIBUTE -#endif - -const unsigned char g_sine_model_data[] DATA_ALIGN_ATTRIBUTE = { - 0x18, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x0e, 0x00, - 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x14, 0x00, - 0x0e, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x0a, 0x00, 0x00, - 0xb8, 0x05, 0x00, 0x00, 0xa0, 0x05, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, - 0x0b, 0x00, 0x00, 0x00, 0x90, 0x05, 0x00, 0x00, 0x7c, 0x05, 0x00, 0x00, - 0x24, 0x05, 0x00, 0x00, 0xd4, 0x04, 0x00, 0x00, 0xc4, 0x00, 0x00, 0x00, - 0x74, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, - 0x14, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, - 0x54, 0xf6, 0xff, 0xff, 0x58, 0xf6, 0xff, 0xff, 0x5c, 0xf6, 0xff, 0xff, - 0x60, 0xf6, 0xff, 0xff, 0xc2, 0xfa, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, - 0x40, 0x00, 0x00, 0x00, 0x7c, 0x19, 0xa7, 0x3e, 0x99, 0x81, 0xb9, 0x3e, - 0x56, 0x8b, 0x9f, 0x3e, 0x88, 0xd8, 0x12, 0xbf, 0x74, 0x10, 0x56, 0x3e, - 0xfe, 0xc6, 0xdf, 0xbe, 0xf2, 0x10, 0x5a, 0xbe, 0xf0, 0xe2, 0x0a, 0xbe, - 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Dataset: Data is generated locally in the Jupyter Notebook. +2. Dataset Type: **Structured Data** +3. Deep Learning Framework: **TensorFlow 2** +4. Language: **Python 3.7** +5. Model Size: **2.5 kB** +6. Model Category: **Regression** + +## Training + +Train the model in the cloud using Google Colaboratory or locally using a +Jupyter Notebook. + + + + +
+ Google Colaboratory + + Jupyter Notebook +
+ +*Estimated Training Time: 10 minutes.* + + +## Trained Models + +| Download Link | [hello_world.zip](https://storage.googleapis.com/download.tensorflow.org/models/tflite/micro/hello_world_2020_04_13.zip) | +| ------------- |-------------| + + +The `models` directory in the above zip file can be generated by following the +instructions in the [Training](#training) section above. It +includes the following 3 model files: + +| Name | Format | Target Framework | Target Device | +| :------------- |:-------------|:-------------|-----| +| `model.pb` | Keras SavedModel | TensorFlow | Large-Scale/Cloud/Servers | +| `model.tflite` *(2.5 kB)* | Fully Quantized* TFLite Model | TensorFlow Lite | Mobile Devices| +| `model.cc` | C Source File | TensorFlow Lite for Microcontrollers | Microcontrollers | + +**Fully quantized implies that the model is **strictly int8** quantized +**excluding** the input(s) and output(s).* + + + +## Model Architecture + +The final model used to simulate a sine wave is displayed below. It is a +simple feed forward deep neural network with 2 fully connected layers with +ReLu activations and a final fully connected output layer with as shown below. + +![model_architecture.png](../images/model_architecture.png) + +*This image was derived from visualizing the 'model.tflite' file in [Netron](https://github.com/lutzroeder/netron)* + diff --git a/tensorflow/lite/micro/examples/hello_world/train/train_hello_world_model.ipynb b/tensorflow/lite/micro/examples/hello_world/train/train_hello_world_model.ipynb new file mode 100644 index 00000000000..129e278f540 --- /dev/null +++ b/tensorflow/lite/micro/examples/hello_world/train/train_hello_world_model.ipynb @@ -0,0 +1,3530 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "train_hello_world_model.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "aCZBFzjClURz", + "colab_type": "text" + }, + "source": [ + "# Train a basic TensorFlow Lite for Microcontrollers model\n", + "\n", + "This notebook demonstrates the process of training a 2.5 kB model using TensorFlow and converting it for use with TensorFlow Lite for Microcontrollers. \n", + "\n", + "Deep learning networks learn to model patterns in underlying data. Here, we're going to train a network to model data generated by a [sine](https://en.wikipedia.org/wiki/Sine) function. This will result in a model that can take a value, `x`, and predict its sine, `y`.\n", + "\n", + "The model created in this notebook is used in the [hello_world](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/hello_world) example for [TensorFlow Lite for MicroControllers](https://www.tensorflow.org/lite/microcontrollers/overview).\n", + "\n", + "\n", + " \n", + " \n", + "
\n", + " Run in Google Colab\n", + " \n", + " View source on GitHub\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0Cz6uV1zU_hV", + "colab_type": "text" + }, + "source": [ + "**Training is much faster using GPU acceleration.** Before you proceed, ensure you are using a GPU runtime by going to **Runtime -> Change runtime type** and set **Hardware accelerator: GPU**." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_UQblnrLd_ET", + "colab_type": "text" + }, + "source": [ + "## Configure Defaults" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5PYwRFppd-WB", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Define paths to model files\n", + "import os\n", + "MODELS_DIR = 'models/'\n", + "os.mkdir(MODELS_DIR)\n", + "MODEL_TF = MODELS_DIR + 'model.pb'\n", + "MODEL_NO_QUANT_TFLITE = MODELS_DIR + 'model_no_quant.tflite'\n", + "MODEL_TFLITE = MODELS_DIR + 'model.tflite'\n", + "MODEL_TFLITE_MICRO = MODELS_DIR + 'model.cc'" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dh4AXGuHWeu1", + "colab_type": "text" + }, + "source": [ + "## Setup Environment\n", + "\n", + "Install Dependencies" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "outputId": "e5cbcfca-b6a5-4a61-ac95-1a8d3fd5411b", + "id": "cr1VLfotanf6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + } + }, + "source": [ + "! pip install -q tensorflow==2" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\u001b[K |████████████████████████████████| 86.3MB 52kB/s \n", + "\u001b[K |████████████████████████████████| 450kB 46.2MB/s \n", + "\u001b[K |████████████████████████████████| 3.8MB 50.3MB/s \n", + "\u001b[?25h Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6rLYpvtg9P4o", + "colab_type": "text" + }, + "source": [ + "Set Seed for Repeatable Results" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EIH9NN1c9PJn", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Set a \"seed\" value, so we get the same random numbers each time we run this\n", + "# notebook for reproducible results.\n", + "# Numpy is a math library\n", + "import numpy as np\n", + "np.random.seed(1) # numpy seed\n", + "# TensorFlow is an open source machine learning library\n", + "import tensorflow as tf\n", + "tf.random.set_seed(1) # tensorflow global random seed" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tx9lOPWh9grN", + "colab_type": "text" + }, + "source": [ + "Import Dependencies" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "53PBJBv1jEtJ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Keras is TensorFlow's high-level API for deep learning\n", + "from tensorflow import keras\n", + "# Matplotlib is a graphing library\n", + "import matplotlib.pyplot as plt\n", + "# Math is Python's math library\n", + "import math" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "p-PuBEb6CMeo", + "colab_type": "text" + }, + "source": [ + "## Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7gB0-dlNmLT-", + "colab_type": "text" + }, + "source": [ + "### 1. Generate Data\n", + "\n", + "The code in the following cell will generate a set of random `x` values, calculate their sine values, and display them on a graph." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uKjg7QeMDsDx", + "colab_type": "code", + "outputId": "0afa45df-3766-467c-c92f-2428aa04f22b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + } + }, + "source": [ + "# Number of sample datapoints\n", + "SAMPLES = 1000\n", + "\n", + "# Generate a uniformly distributed set of random numbers in the range from\n", + "# 0 to 2π, which covers a complete sine wave oscillation\n", + "x_values = np.random.uniform(\n", + " low=0, high=2*math.pi, size=SAMPLES).astype(np.float32)\n", + "\n", + "# Shuffle the values to guarantee they're not in order\n", + "np.random.shuffle(x_values)\n", + "\n", + "# Calculate the corresponding sine values\n", + "y_values = np.sin(x_values).astype(np.float32)\n", + "\n", + "# Plot our data. The 'b.' argument tells the library to print blue dots.\n", + "plt.plot(x_values, y_values, 'b.')\n", + "plt.show()" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iWOlC7W_FYvA", + "colab_type": "text" + }, + "source": [ + "### 2. Add Noise\n", + "Since it was generated directly by the sine function, our data fits a nice, smooth curve.\n", + "\n", + "However, machine learning models are good at extracting underlying meaning from messy, real world data. To demonstrate this, we can add some noise to our data to approximate something more life-like.\n", + "\n", + "In the following cell, we'll add some random noise to each value, then draw a new graph:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "i0FJe3Y-Gkac", + "colab_type": "code", + "outputId": "38886dba-5757-4c7e-bcd6-32c1eb82863e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + } + }, + "source": [ + "# Add a small random number to each y value\n", + "y_values += 0.1 * np.random.randn(*y_values.shape)\n", + "\n", + "# Plot our data\n", + "plt.plot(x_values, y_values, 'b.')\n", + "plt.show()" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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vIaLvDt//LBFNLcfzRrF5c+n7yoATpbmR2KnbUZ3JcFgwzNifc07wdi7HQnsL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\nvEAmk8Cf/imHe4px+un2s9q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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Up8Xk_pMH4Rt", + "colab_type": "text" + }, + "source": [ + "### 3. Split the Data\n", + "We now have a noisy dataset that approximates real world data. We'll be using this to train our model.\n", + "\n", + "To evaluate the accuracy of the model we train, we'll need to compare its predictions to real data and check how well they match up. This evaluation happens during training (where it is referred to as validation) and after training (referred to as testing) It's important in both cases that we use fresh data that was not already used to train the model.\n", + "\n", + "The data is split as follows:\n", + " 1. Training: 60%\n", + " 2. Validation: 20%\n", + " 3. Testing: 20% \n", + "\n", + "The following code will split our data and then plots each set as a different color:\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nNYko5L1keqZ", + "colab_type": "code", + "outputId": "a016bf4f-60a9-4c3f-9954-71218f7f4a25", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + } + }, + "source": [ + "# We'll use 60% of our data for training and 20% for testing. The remaining 20%\n", + "# will be used for validation. Calculate the indices of each section.\n", + "TRAIN_SPLIT = int(0.6 * SAMPLES)\n", + "TEST_SPLIT = int(0.2 * SAMPLES + TRAIN_SPLIT)\n", + "\n", + "# Use np.split to chop our data into three parts.\n", + "# The second argument to np.split is an array of indices where the data will be\n", + "# split. We provide two indices, so the data will be divided into three chunks.\n", + "x_train, x_test, x_validate = np.split(x_values, [TRAIN_SPLIT, TEST_SPLIT])\n", + "y_train, y_test, y_validate = np.split(y_values, [TRAIN_SPLIT, TEST_SPLIT])\n", + "\n", + "# Double check that our splits add up correctly\n", + "assert (x_train.size + x_validate.size + x_test.size) == SAMPLES\n", + "\n", + "# Plot the data in each partition in different colors:\n", + "plt.plot(x_train, y_train, 'b.', label=\"Train\")\n", + "plt.plot(x_test, y_test, 'r.', label=\"Test\")\n", + "plt.plot(x_validate, y_validate, 'y.', label=\"Validate\")\n", + "plt.legend()\n", + "plt.show()\n" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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Q8+9D3WbogYQOuaU83+ENxveF/MpcPv54Al9//RYQEAp55OU5rFljkZOjmpx7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\nH79/5+a68G61y/k4ytTNyo3RMHIhnZa0DBENHGgTDqt99Vt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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wfdelu1TmgPk", + "colab_type": "text" + }, + "source": [ + "## Training" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "t5McVnHmNiDw", + "colab_type": "text" + }, + "source": [ + "### 1. Design the Model\n", + "We're going to build a simple neural network model that will take an input value (in this case, `x`) and use it to predict a numeric output value (the sine of `x`). This type of problem is called a _regression_. It will use _layers_ of _neurons_ to attempt to learn any patterns underlying the training data, so it can make predictions.\n", + "\n", + "To begin with, we'll define two layers. The first layer takes a single input (our `x` value) and runs it through 8 neurons. Based on this input, each neuron will become _activated_ to a certain degree based on its internal state (its _weight_ and _bias_ values). A neuron's degree of activation is expressed as a number.\n", + "\n", + "The activation numbers from our first layer will be fed as inputs to our second layer, which is a single neuron. It will apply its own weights and bias to these inputs and calculate its own activation, which will be output as our `y` value.\n", + "\n", + "**Note:** To learn more about how neural networks function, you can explore the [Learn TensorFlow](https://codelabs.developers.google.com/codelabs/tensorflow-lab1-helloworld) codelabs.\n", + "\n", + "The code in the following cell defines our model using [Keras](https://www.tensorflow.org/guide/keras), TensorFlow's high-level API for creating deep learning networks. Once the network is defined, we _compile_ it, specifying parameters that determine how it will be trained:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gD60bE8cXQId", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# We'll use Keras to create a simple model architecture\n", + "model_1 = tf.keras.Sequential()\n", + "\n", + "# First layer takes a scalar input and feeds it through 8 \"neurons\". The\n", + "# neurons decide whether to activate based on the 'relu' activation function.\n", + "model_1.add(keras.layers.Dense(8, activation='relu', input_shape=(1,)))\n", + "\n", + "# Final layer is a single neuron, since we want to output a single value\n", + "model_1.add(keras.layers.Dense(1))\n", + "\n", + "# Compile the model using a standard optimizer and loss function for regression\n", + "model_1.compile(optimizer='adam', loss='mse', metrics=['mae'])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "O0idLyRLQeGj", + "colab_type": "text" + }, + "source": [ + "### 2. Train the Model\n", + "Once we've defined the model, we can use our data to _train_ it. Training involves passing an `x` value into the neural network, checking how far the network's output deviates from the expected `y` value, and adjusting the neurons' weights and biases so that the output is more likely to be correct the next time.\n", + "\n", + "Training runs this process on the full dataset multiple times, and each full run-through is known as an _epoch_. The number of epochs to run during training is a parameter we can set.\n", + "\n", + "During each epoch, data is run through the network in multiple _batches_. Each batch, several pieces of data are passed into the network, producing output values. These outputs' correctness is measured in aggregate and the network's weights and biases are adjusted accordingly, once per batch. The _batch size_ is also a parameter we can set.\n", + "\n", + "The code in the following cell uses the `x` and `y` values from our training data to train the model. It runs for 500 _epochs_, with 64 pieces of data in each _batch_. We also pass in some data for _validation_. As you will see when you run the cell, training can take a while to complete:\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "p8hQKr4cVOdE", + "colab_type": "code", + "outputId": "5e9fcc84-1733-4786-8fde-ce47a510cde6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "source": [ + "# Train the model on our training data while validating on our validation set\n", + "history_1 = model_1.fit(x_train, y_train, epochs=500, batch_size=64,\n", + " validation_data=(x_validate, y_validate))" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Train on 600 samples, validate on 200 samples\n", + "Epoch 1/500\n", + "600/600 [==============================] - 1s 971us/sample - loss: 0.6936 - mae: 0.6897 - val_loss: 0.6396 - val_mae: 0.6501\n", + "Epoch 2/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.5965 - mae: 0.6254 - val_loss: 0.5594 - val_mae: 0.6035\n", + "Epoch 3/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.5240 - mae: 0.5830 - val_loss: 0.5021 - val_mae: 0.5765\n", + "Epoch 4/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.4724 - mae: 0.5549 - val_loss: 0.4634 - val_mae: 0.5615\n", + "Epoch 5/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.4392 - mae: 0.5390 - val_loss: 0.4375 - val_mae: 0.5533\n", + "Epoch 6/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.4174 - mae: 0.5305 - val_loss: 0.4215 - val_mae: 0.5487\n", + "Epoch 7/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.4026 - mae: 0.5244 - val_loss: 0.4119 - val_mae: 0.5464\n", + "Epoch 8/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.3939 - mae: 0.5225 - val_loss: 0.4057 - val_mae: 0.5452\n", + "Epoch 9/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.3880 - mae: 0.5216 - val_loss: 0.4015 - val_mae: 0.5439\n", + "Epoch 10/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.3836 - mae: 0.5210 - val_loss: 0.3981 - val_mae: 0.5425\n", + "Epoch 11/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.3802 - mae: 0.5205 - val_loss: 0.3950 - val_mae: 0.5412\n", + "Epoch 12/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.3770 - mae: 0.5200 - val_loss: 0.3922 - val_mae: 0.5400\n", + "Epoch 13/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.3741 - mae: 0.5189 - val_loss: 0.3894 - val_mae: 0.5385\n", + "Epoch 14/500\n", + "600/600 [==============================] - 0s 41us/sample - loss: 0.3712 - mae: 0.5173 - val_loss: 0.3866 - val_mae: 0.5368\n", + "Epoch 15/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.3686 - mae: 0.5162 - val_loss: 0.3837 - val_mae: 0.5354\n", + "Epoch 16/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.3655 - mae: 0.5143 - val_loss: 0.3808 - val_mae: 0.5335\n", + "Epoch 17/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.3627 - mae: 0.5122 - val_loss: 0.3777 - val_mae: 0.5314\n", + "Epoch 18/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.3597 - mae: 0.5101 - val_loss: 0.3748 - val_mae: 0.5296\n", + "Epoch 19/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.3567 - mae: 0.5080 - val_loss: 0.3717 - val_mae: 0.5276\n", + "Epoch 20/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.3538 - mae: 0.5059 - val_loss: 0.3686 - val_mae: 0.5256\n", + "Epoch 21/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.3507 - mae: 0.5037 - val_loss: 0.3654 - val_mae: 0.5234\n", + "Epoch 22/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.3477 - mae: 0.5012 - val_loss: 0.3622 - val_mae: 0.5211\n", + "Epoch 23/500\n", + "600/600 [==============================] - 0s 41us/sample - loss: 0.3447 - mae: 0.4993 - val_loss: 0.3591 - val_mae: 0.5195\n", + "Epoch 24/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.3414 - mae: 0.4970 - val_loss: 0.3558 - val_mae: 0.5172\n", + "Epoch 25/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.3385 - mae: 0.4949 - val_loss: 0.3526 - val_mae: 0.5153\n", + "Epoch 26/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.3352 - mae: 0.4926 - val_loss: 0.3493 - val_mae: 0.5130\n", + "Epoch 27/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.3321 - mae: 0.4904 - val_loss: 0.3461 - val_mae: 0.5110\n", + "Epoch 28/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.3288 - mae: 0.4880 - val_loss: 0.3429 - val_mae: 0.5087\n", + "Epoch 29/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.3257 - mae: 0.4854 - val_loss: 0.3395 - val_mae: 0.5064\n", + "Epoch 30/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.3227 - mae: 0.4831 - val_loss: 0.3362 - val_mae: 0.5041\n", + "Epoch 31/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.3195 - mae: 0.4806 - val_loss: 0.3330 - val_mae: 0.5018\n", + "Epoch 32/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.3165 - mae: 0.4782 - val_loss: 0.3298 - val_mae: 0.4996\n", + "Epoch 33/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.3133 - mae: 0.4760 - val_loss: 0.3267 - val_mae: 0.4976\n", + "Epoch 34/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.3103 - mae: 0.4738 - val_loss: 0.3235 - val_mae: 0.4952\n", + "Epoch 35/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.3072 - mae: 0.4713 - val_loss: 0.3203 - val_mae: 0.4930\n", + "Epoch 36/500\n", + "600/600 [==============================] - 0s 100us/sample - loss: 0.3042 - mae: 0.4694 - val_loss: 0.3173 - val_mae: 0.4913\n", + "Epoch 37/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.3012 - mae: 0.4673 - val_loss: 0.3141 - val_mae: 0.4890\n", + "Epoch 38/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.2981 - mae: 0.4651 - val_loss: 0.3111 - val_mae: 0.4869\n", + "Epoch 39/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.2952 - mae: 0.4625 - val_loss: 0.3078 - val_mae: 0.4841\n", + "Epoch 40/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.2921 - mae: 0.4602 - val_loss: 0.3049 - val_mae: 0.4822\n", + "Epoch 41/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.2891 - mae: 0.4585 - val_loss: 0.3021 - val_mae: 0.4810\n", + "Epoch 42/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.2861 - mae: 0.4568 - val_loss: 0.2991 - val_mae: 0.4790\n", + "Epoch 43/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.2832 - mae: 0.4546 - val_loss: 0.2961 - val_mae: 0.4767\n", + "Epoch 44/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.2803 - mae: 0.4523 - val_loss: 0.2931 - val_mae: 0.4741\n", + "Epoch 45/500\n", + "600/600 [==============================] - 0s 41us/sample - loss: 0.2775 - mae: 0.4503 - val_loss: 0.2902 - val_mae: 0.4723\n", + "Epoch 46/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.2746 - mae: 0.4482 - val_loss: 0.2873 - val_mae: 0.4701\n", + "Epoch 47/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.2719 - mae: 0.4464 - val_loss: 0.2846 - val_mae: 0.4685\n", + "Epoch 48/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.2691 - mae: 0.4444 - val_loss: 0.2818 - val_mae: 0.4666\n", + "Epoch 49/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.2663 - mae: 0.4425 - val_loss: 0.2791 - val_mae: 0.4646\n", + "Epoch 50/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.2636 - mae: 0.4404 - val_loss: 0.2764 - val_mae: 0.4625\n", + "Epoch 51/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.2610 - mae: 0.4382 - val_loss: 0.2736 - val_mae: 0.4599\n", + "Epoch 52/500\n", + "600/600 [==============================] - 0s 41us/sample - loss: 0.2583 - mae: 0.4361 - val_loss: 0.2711 - val_mae: 0.4580\n", + "Epoch 53/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.2558 - mae: 0.4344 - val_loss: 0.2685 - val_mae: 0.4561\n", + "Epoch 54/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.2532 - mae: 0.4326 - val_loss: 0.2659 - val_mae: 0.4539\n", + "Epoch 55/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.2508 - mae: 0.4307 - val_loss: 0.2634 - val_mae: 0.4518\n", + "Epoch 56/500\n", + "600/600 [==============================] - 0s 65us/sample - loss: 0.2483 - mae: 0.4288 - val_loss: 0.2609 - val_mae: 0.4499\n", + "Epoch 57/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.2459 - mae: 0.4271 - val_loss: 0.2586 - val_mae: 0.4485\n", + "Epoch 58/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.2436 - mae: 0.4255 - val_loss: 0.2561 - val_mae: 0.4464\n", + "Epoch 59/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.2411 - mae: 0.4239 - val_loss: 0.2540 - val_mae: 0.4451\n", + "Epoch 60/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.2387 - mae: 0.4220 - val_loss: 0.2516 - val_mae: 0.4431\n", + "Epoch 61/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.2365 - mae: 0.4202 - val_loss: 0.2493 - val_mae: 0.4411\n", + "Epoch 62/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.2343 - mae: 0.4186 - val_loss: 0.2472 - val_mae: 0.4395\n", + "Epoch 63/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.2322 - mae: 0.4169 - val_loss: 0.2450 - val_mae: 0.4375\n", + "Epoch 64/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.2301 - mae: 0.4151 - val_loss: 0.2428 - val_mae: 0.4355\n", + "Epoch 65/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.2280 - mae: 0.4134 - val_loss: 0.2408 - val_mae: 0.4338\n", + "Epoch 66/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.2260 - mae: 0.4118 - val_loss: 0.2388 - val_mae: 0.4323\n", + "Epoch 67/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.2241 - mae: 0.4104 - val_loss: 0.2369 - val_mae: 0.4308\n", + "Epoch 68/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.2222 - mae: 0.4089 - val_loss: 0.2351 - val_mae: 0.4293\n", + "Epoch 69/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.2204 - mae: 0.4076 - val_loss: 0.2334 - val_mae: 0.4280\n", + "Epoch 70/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.2188 - mae: 0.4062 - val_loss: 0.2314 - val_mae: 0.4255\n", + "Epoch 71/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.2168 - mae: 0.4043 - val_loss: 0.2297 - val_mae: 0.4246\n", + "Epoch 72/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.2151 - mae: 0.4031 - val_loss: 0.2280 - val_mae: 0.4231\n", + "Epoch 73/500\n", + "600/600 [==============================] - 0s 40us/sample - loss: 0.2135 - mae: 0.4019 - val_loss: 0.2265 - val_mae: 0.4224\n", + "Epoch 74/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.2120 - mae: 0.4007 - val_loss: 0.2247 - val_mae: 0.4203\n", + "Epoch 75/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.2102 - mae: 0.3992 - val_loss: 0.2233 - val_mae: 0.4194\n", + "Epoch 76/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.2087 - mae: 0.3980 - val_loss: 0.2216 - val_mae: 0.4178\n", + "Epoch 77/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.2071 - mae: 0.3965 - val_loss: 0.2199 - val_mae: 0.4158\n", + "Epoch 78/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.2056 - mae: 0.3951 - val_loss: 0.2185 - val_mae: 0.4144\n", + "Epoch 79/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.2044 - mae: 0.3938 - val_loss: 0.2170 - val_mae: 0.4122\n", + "Epoch 80/500\n", + "600/600 [==============================] - 0s 41us/sample - loss: 0.2029 - mae: 0.3926 - val_loss: 0.2159 - val_mae: 0.4123\n", + "Epoch 81/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.2015 - mae: 0.3915 - val_loss: 0.2145 - val_mae: 0.4108\n", + "Epoch 82/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.2002 - mae: 0.3902 - val_loss: 0.2131 - val_mae: 0.4091\n", + "Epoch 83/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1989 - mae: 0.3890 - val_loss: 0.2119 - val_mae: 0.4081\n", + "Epoch 84/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1977 - mae: 0.3878 - val_loss: 0.2107 - val_mae: 0.4071\n", + "Epoch 85/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.1965 - mae: 0.3867 - val_loss: 0.2095 - val_mae: 0.4057\n", + "Epoch 86/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1953 - mae: 0.3857 - val_loss: 0.2082 - val_mae: 0.4044\n", + "Epoch 87/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1941 - mae: 0.3843 - val_loss: 0.2072 - val_mae: 0.4032\n", + "Epoch 88/500\n", + "600/600 [==============================] - 0s 41us/sample - loss: 0.1930 - mae: 0.3834 - val_loss: 0.2062 - val_mae: 0.4028\n", + "Epoch 89/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.1920 - mae: 0.3825 - val_loss: 0.2053 - val_mae: 0.4018\n", + "Epoch 90/500\n", + "600/600 [==============================] - 0s 60us/sample - loss: 0.1913 - mae: 0.3819 - val_loss: 0.2046 - val_mae: 0.4018\n", + "Epoch 91/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1902 - mae: 0.3808 - val_loss: 0.2033 - val_mae: 0.3994\n", + "Epoch 92/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1892 - mae: 0.3796 - val_loss: 0.2025 - val_mae: 0.3989\n", + "Epoch 93/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1882 - mae: 0.3786 - val_loss: 0.2015 - val_mae: 0.3970\n", + "Epoch 94/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1875 - mae: 0.3776 - val_loss: 0.2006 - val_mae: 0.3959\n", + "Epoch 95/500\n", + "600/600 [==============================] - 0s 58us/sample - loss: 0.1870 - mae: 0.3768 - val_loss: 0.1998 - val_mae: 0.3941\n", + "Epoch 96/500\n", + "600/600 [==============================] - 0s 67us/sample - loss: 0.1861 - mae: 0.3760 - val_loss: 0.1992 - val_mae: 0.3947\n", + "Epoch 97/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.1852 - mae: 0.3751 - val_loss: 0.1984 - val_mae: 0.3937\n", + "Epoch 98/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1843 - mae: 0.3742 - val_loss: 0.1980 - val_mae: 0.3939\n", + "Epoch 99/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1837 - mae: 0.3737 - val_loss: 0.1976 - val_mae: 0.3940\n", + "Epoch 100/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1832 - mae: 0.3733 - val_loss: 0.1970 - val_mae: 0.3936\n", + "Epoch 101/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.1828 - mae: 0.3727 - val_loss: 0.1960 - val_mae: 0.3910\n", + "Epoch 102/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.1820 - mae: 0.3717 - val_loss: 0.1956 - val_mae: 0.3913\n", + "Epoch 103/500\n", + "600/600 [==============================] - 0s 64us/sample - loss: 0.1812 - mae: 0.3708 - val_loss: 0.1950 - val_mae: 0.3903\n", + "Epoch 104/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1806 - mae: 0.3701 - val_loss: 0.1946 - val_mae: 0.3898\n", + "Epoch 105/500\n", + "600/600 [==============================] - 0s 58us/sample - loss: 0.1802 - mae: 0.3695 - val_loss: 0.1939 - val_mae: 0.3886\n", + "Epoch 106/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1795 - mae: 0.3686 - val_loss: 0.1932 - val_mae: 0.3871\n", + "Epoch 107/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1790 - mae: 0.3679 - val_loss: 0.1928 - val_mae: 0.3866\n", + "Epoch 108/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1786 - mae: 0.3674 - val_loss: 0.1924 - val_mae: 0.3864\n", + "Epoch 109/500\n", + "600/600 [==============================] - 0s 40us/sample - loss: 0.1783 - mae: 0.3667 - val_loss: 0.1919 - val_mae: 0.3849\n", + "Epoch 110/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1781 - mae: 0.3666 - val_loss: 0.1919 - val_mae: 0.3861\n", + "Epoch 111/500\n", + "600/600 [==============================] - 0s 68us/sample - loss: 0.1774 - mae: 0.3658 - val_loss: 0.1912 - val_mae: 0.3843\n", + "Epoch 112/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1770 - mae: 0.3653 - val_loss: 0.1911 - val_mae: 0.3846\n", + "Epoch 113/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.1766 - mae: 0.3647 - val_loss: 0.1906 - val_mae: 0.3833\n", + "Epoch 114/500\n", + "600/600 [==============================] - 0s 41us/sample - loss: 0.1763 - mae: 0.3642 - val_loss: 0.1903 - val_mae: 0.3831\n", + "Epoch 115/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1758 - mae: 0.3636 - val_loss: 0.1898 - val_mae: 0.3817\n", + "Epoch 116/500\n", + "600/600 [==============================] - 0s 41us/sample - loss: 0.1755 - mae: 0.3630 - val_loss: 0.1897 - val_mae: 0.3821\n", + "Epoch 117/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.1752 - mae: 0.3627 - val_loss: 0.1893 - val_mae: 0.3810\n", + "Epoch 118/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.1749 - mae: 0.3621 - val_loss: 0.1890 - val_mae: 0.3805\n", + "Epoch 119/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.1747 - mae: 0.3617 - val_loss: 0.1888 - val_mae: 0.3802\n", + "Epoch 120/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1743 - mae: 0.3612 - val_loss: 0.1885 - val_mae: 0.3794\n", + "Epoch 121/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1743 - mae: 0.3610 - val_loss: 0.1885 - val_mae: 0.3803\n", + "Epoch 122/500\n", + "600/600 [==============================] - 0s 60us/sample - loss: 0.1740 - mae: 0.3608 - val_loss: 0.1884 - val_mae: 0.3802\n", + "Epoch 123/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1736 - mae: 0.3602 - val_loss: 0.1879 - val_mae: 0.3786\n", + "Epoch 124/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1737 - mae: 0.3597 - val_loss: 0.1876 - val_mae: 0.3765\n", + "Epoch 125/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1738 - mae: 0.3597 - val_loss: 0.1876 - val_mae: 0.3780\n", + "Epoch 126/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1734 - mae: 0.3591 - val_loss: 0.1872 - val_mae: 0.3762\n", + "Epoch 127/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1727 - mae: 0.3583 - val_loss: 0.1873 - val_mae: 0.3775\n", + "Epoch 128/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1726 - mae: 0.3583 - val_loss: 0.1872 - val_mae: 0.3776\n", + "Epoch 129/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.1724 - mae: 0.3579 - val_loss: 0.1869 - val_mae: 0.3763\n", + "Epoch 130/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1723 - mae: 0.3575 - val_loss: 0.1867 - val_mae: 0.3757\n", + "Epoch 131/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.1722 - mae: 0.3573 - val_loss: 0.1866 - val_mae: 0.3759\n", + "Epoch 132/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1720 - mae: 0.3572 - val_loss: 0.1868 - val_mae: 0.3770\n", + "Epoch 133/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.1721 - mae: 0.3570 - val_loss: 0.1864 - val_mae: 0.3754\n", + "Epoch 134/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1717 - mae: 0.3566 - val_loss: 0.1864 - val_mae: 0.3754\n", + "Epoch 135/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1717 - mae: 0.3563 - val_loss: 0.1861 - val_mae: 0.3741\n", + "Epoch 136/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1715 - mae: 0.3559 - val_loss: 0.1861 - val_mae: 0.3744\n", + "Epoch 137/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1714 - mae: 0.3558 - val_loss: 0.1861 - val_mae: 0.3748\n", + "Epoch 138/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1713 - mae: 0.3555 - val_loss: 0.1859 - val_mae: 0.3737\n", + "Epoch 139/500\n", + "600/600 [==============================] - 0s 41us/sample - loss: 0.1712 - mae: 0.3551 - val_loss: 0.1857 - val_mae: 0.3731\n", + "Epoch 140/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1712 - mae: 0.3551 - val_loss: 0.1857 - val_mae: 0.3732\n", + "Epoch 141/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.1710 - mae: 0.3547 - val_loss: 0.1856 - val_mae: 0.3724\n", + "Epoch 142/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.1713 - mae: 0.3546 - val_loss: 0.1855 - val_mae: 0.3718\n", + "Epoch 143/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1711 - mae: 0.3545 - val_loss: 0.1857 - val_mae: 0.3740\n", + "Epoch 144/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.1708 - mae: 0.3545 - val_loss: 0.1856 - val_mae: 0.3733\n", + "Epoch 145/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.1708 - mae: 0.3541 - val_loss: 0.1854 - val_mae: 0.3717\n", + "Epoch 146/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1707 - mae: 0.3539 - val_loss: 0.1854 - val_mae: 0.3720\n", + "Epoch 147/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.1706 - mae: 0.3539 - val_loss: 0.1854 - val_mae: 0.3725\n", + "Epoch 148/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.1706 - mae: 0.3537 - val_loss: 0.1853 - val_mae: 0.3722\n", + "Epoch 149/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1705 - mae: 0.3536 - val_loss: 0.1853 - val_mae: 0.3725\n", + "Epoch 150/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.1707 - mae: 0.3537 - val_loss: 0.1853 - val_mae: 0.3720\n", + "Epoch 151/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1704 - mae: 0.3532 - val_loss: 0.1851 - val_mae: 0.3704\n", + "Epoch 152/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.1705 - mae: 0.3530 - val_loss: 0.1851 - val_mae: 0.3709\n", + "Epoch 153/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1703 - mae: 0.3529 - val_loss: 0.1851 - val_mae: 0.3714\n", + "Epoch 154/500\n", + "600/600 [==============================] - 0s 63us/sample - loss: 0.1703 - mae: 0.3530 - val_loss: 0.1852 - val_mae: 0.3720\n", + "Epoch 155/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1703 - mae: 0.3529 - val_loss: 0.1851 - val_mae: 0.3713\n", + "Epoch 156/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1702 - mae: 0.3526 - val_loss: 0.1850 - val_mae: 0.3711\n", + "Epoch 157/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.1701 - mae: 0.3526 - val_loss: 0.1852 - val_mae: 0.3719\n", + "Epoch 158/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1701 - mae: 0.3528 - val_loss: 0.1852 - val_mae: 0.3721\n", + "Epoch 159/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1705 - mae: 0.3528 - val_loss: 0.1849 - val_mae: 0.3698\n", + "Epoch 160/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1701 - mae: 0.3525 - val_loss: 0.1852 - val_mae: 0.3723\n", + "Epoch 161/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1701 - mae: 0.3528 - val_loss: 0.1851 - val_mae: 0.3721\n", + "Epoch 162/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1701 - mae: 0.3527 - val_loss: 0.1851 - val_mae: 0.3717\n", + "Epoch 163/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1701 - mae: 0.3527 - val_loss: 0.1852 - val_mae: 0.3722\n", + "Epoch 164/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1704 - mae: 0.3531 - val_loss: 0.1852 - val_mae: 0.3722\n", + "Epoch 165/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1700 - mae: 0.3525 - val_loss: 0.1847 - val_mae: 0.3697\n", + "Epoch 166/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1702 - mae: 0.3518 - val_loss: 0.1847 - val_mae: 0.3694\n", + "Epoch 167/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1704 - mae: 0.3519 - val_loss: 0.1847 - val_mae: 0.3680\n", + "Epoch 168/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1699 - mae: 0.3516 - val_loss: 0.1848 - val_mae: 0.3704\n", + "Epoch 169/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1700 - mae: 0.3522 - val_loss: 0.1851 - val_mae: 0.3718\n", + "Epoch 170/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1700 - mae: 0.3524 - val_loss: 0.1851 - val_mae: 0.3720\n", + "Epoch 171/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1699 - mae: 0.3522 - val_loss: 0.1848 - val_mae: 0.3702\n", + "Epoch 172/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1698 - mae: 0.3518 - val_loss: 0.1849 - val_mae: 0.3711\n", + "Epoch 173/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1699 - mae: 0.3521 - val_loss: 0.1849 - val_mae: 0.3710\n", + "Epoch 174/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1699 - mae: 0.3521 - val_loss: 0.1849 - val_mae: 0.3711\n", + "Epoch 175/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1700 - mae: 0.3518 - val_loss: 0.1847 - val_mae: 0.3699\n", + "Epoch 176/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1699 - mae: 0.3517 - val_loss: 0.1847 - val_mae: 0.3701\n", + "Epoch 177/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1702 - mae: 0.3524 - val_loss: 0.1852 - val_mae: 0.3721\n", + "Epoch 178/500\n", + "600/600 [==============================] - 0s 62us/sample - loss: 0.1700 - mae: 0.3523 - val_loss: 0.1849 - val_mae: 0.3710\n", + "Epoch 179/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1697 - mae: 0.3517 - val_loss: 0.1847 - val_mae: 0.3701\n", + "Epoch 180/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1703 - mae: 0.3515 - val_loss: 0.1846 - val_mae: 0.3681\n", + "Epoch 181/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1697 - mae: 0.3515 - val_loss: 0.1849 - val_mae: 0.3708\n", + "Epoch 182/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1698 - mae: 0.3518 - val_loss: 0.1850 - val_mae: 0.3715\n", + "Epoch 183/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1698 - mae: 0.3520 - val_loss: 0.1848 - val_mae: 0.3708\n", + "Epoch 184/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1698 - mae: 0.3516 - val_loss: 0.1846 - val_mae: 0.3690\n", + "Epoch 185/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1699 - mae: 0.3514 - val_loss: 0.1846 - val_mae: 0.3698\n", + "Epoch 186/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1700 - mae: 0.3517 - val_loss: 0.1848 - val_mae: 0.3706\n", + "Epoch 187/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.1696 - mae: 0.3513 - val_loss: 0.1846 - val_mae: 0.3693\n", + "Epoch 188/500\n", + "600/600 [==============================] - 0s 63us/sample - loss: 0.1697 - mae: 0.3511 - val_loss: 0.1845 - val_mae: 0.3687\n", + "Epoch 189/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1698 - mae: 0.3508 - val_loss: 0.1845 - val_mae: 0.3675\n", + "Epoch 190/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.1699 - mae: 0.3510 - val_loss: 0.1845 - val_mae: 0.3688\n", + "Epoch 191/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1698 - mae: 0.3509 - val_loss: 0.1846 - val_mae: 0.3693\n", + "Epoch 192/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1698 - mae: 0.3512 - val_loss: 0.1848 - val_mae: 0.3706\n", + "Epoch 193/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1700 - mae: 0.3520 - val_loss: 0.1850 - val_mae: 0.3714\n", + "Epoch 194/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.1698 - mae: 0.3513 - val_loss: 0.1845 - val_mae: 0.3684\n", + "Epoch 195/500\n", + "600/600 [==============================] - 0s 59us/sample - loss: 0.1697 - mae: 0.3509 - val_loss: 0.1845 - val_mae: 0.3687\n", + "Epoch 196/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1696 - mae: 0.3509 - val_loss: 0.1846 - val_mae: 0.3691\n", + "Epoch 197/500\n", + "600/600 [==============================] - 0s 76us/sample - loss: 0.1697 - mae: 0.3508 - val_loss: 0.1845 - val_mae: 0.3684\n", + "Epoch 198/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1698 - mae: 0.3506 - val_loss: 0.1845 - val_mae: 0.3683\n", + "Epoch 199/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.1698 - mae: 0.3510 - val_loss: 0.1848 - val_mae: 0.3703\n", + "Epoch 200/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1696 - mae: 0.3511 - val_loss: 0.1846 - val_mae: 0.3690\n", + "Epoch 201/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1697 - mae: 0.3509 - val_loss: 0.1846 - val_mae: 0.3694\n", + "Epoch 202/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1697 - mae: 0.3512 - val_loss: 0.1847 - val_mae: 0.3696\n", + "Epoch 203/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.1697 - mae: 0.3513 - val_loss: 0.1850 - val_mae: 0.3708\n", + "Epoch 204/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1696 - mae: 0.3513 - val_loss: 0.1847 - val_mae: 0.3697\n", + "Epoch 205/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.1696 - mae: 0.3508 - val_loss: 0.1845 - val_mae: 0.3685\n", + "Epoch 206/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.1699 - mae: 0.3504 - val_loss: 0.1845 - val_mae: 0.3669\n", + "Epoch 207/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1697 - mae: 0.3500 - val_loss: 0.1845 - val_mae: 0.3680\n", + "Epoch 208/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1696 - mae: 0.3503 - val_loss: 0.1846 - val_mae: 0.3687\n", + "Epoch 209/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.1696 - mae: 0.3505 - val_loss: 0.1846 - val_mae: 0.3690\n", + "Epoch 210/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1698 - mae: 0.3506 - val_loss: 0.1846 - val_mae: 0.3687\n", + "Epoch 211/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1699 - mae: 0.3513 - val_loss: 0.1849 - val_mae: 0.3703\n", + "Epoch 212/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1697 - mae: 0.3510 - val_loss: 0.1846 - val_mae: 0.3693\n", + "Epoch 213/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1697 - mae: 0.3504 - val_loss: 0.1845 - val_mae: 0.3677\n", + "Epoch 214/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1697 - mae: 0.3502 - val_loss: 0.1845 - val_mae: 0.3681\n", + "Epoch 215/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1695 - mae: 0.3505 - val_loss: 0.1847 - val_mae: 0.3698\n", + "Epoch 216/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1696 - mae: 0.3510 - val_loss: 0.1848 - val_mae: 0.3702\n", + "Epoch 217/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1698 - mae: 0.3512 - val_loss: 0.1846 - val_mae: 0.3694\n", + "Epoch 218/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1696 - mae: 0.3509 - val_loss: 0.1847 - val_mae: 0.3699\n", + "Epoch 219/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.1696 - mae: 0.3511 - val_loss: 0.1847 - val_mae: 0.3700\n", + "Epoch 220/500\n", + "600/600 [==============================] - 0s 60us/sample - loss: 0.1697 - mae: 0.3513 - val_loss: 0.1848 - val_mae: 0.3705\n", + "Epoch 221/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1697 - mae: 0.3513 - val_loss: 0.1847 - val_mae: 0.3699\n", + "Epoch 222/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.1698 - mae: 0.3515 - val_loss: 0.1848 - val_mae: 0.3707\n", + "Epoch 223/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1697 - mae: 0.3514 - val_loss: 0.1845 - val_mae: 0.3695\n", + "Epoch 224/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1696 - mae: 0.3509 - val_loss: 0.1845 - val_mae: 0.3691\n", + "Epoch 225/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1696 - mae: 0.3511 - val_loss: 0.1846 - val_mae: 0.3695\n", + "Epoch 226/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.1697 - mae: 0.3510 - val_loss: 0.1845 - val_mae: 0.3691\n", + "Epoch 227/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.1698 - mae: 0.3513 - val_loss: 0.1846 - val_mae: 0.3699\n", + "Epoch 228/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1699 - mae: 0.3510 - val_loss: 0.1844 - val_mae: 0.3685\n", + "Epoch 229/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1697 - mae: 0.3510 - val_loss: 0.1845 - val_mae: 0.3691\n", + "Epoch 230/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1696 - mae: 0.3510 - val_loss: 0.1846 - val_mae: 0.3696\n", + "Epoch 231/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1696 - mae: 0.3508 - val_loss: 0.1845 - val_mae: 0.3689\n", + "Epoch 232/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1697 - mae: 0.3512 - val_loss: 0.1846 - val_mae: 0.3697\n", + "Epoch 233/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1698 - mae: 0.3509 - val_loss: 0.1845 - val_mae: 0.3689\n", + "Epoch 234/500\n", + "600/600 [==============================] - 0s 41us/sample - loss: 0.1696 - mae: 0.3509 - val_loss: 0.1846 - val_mae: 0.3694\n", + "Epoch 235/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1696 - mae: 0.3511 - val_loss: 0.1846 - val_mae: 0.3693\n", + "Epoch 236/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1700 - mae: 0.3506 - val_loss: 0.1844 - val_mae: 0.3673\n", + "Epoch 237/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1698 - mae: 0.3502 - val_loss: 0.1844 - val_mae: 0.3676\n", + "Epoch 238/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.1696 - mae: 0.3505 - val_loss: 0.1845 - val_mae: 0.3690\n", + "Epoch 239/500\n", + "600/600 [==============================] - 0s 41us/sample - loss: 0.1697 - mae: 0.3508 - val_loss: 0.1845 - val_mae: 0.3691\n", + "Epoch 240/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1697 - mae: 0.3506 - val_loss: 0.1844 - val_mae: 0.3676\n", + "Epoch 241/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1698 - mae: 0.3502 - val_loss: 0.1844 - val_mae: 0.3674\n", + "Epoch 242/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1697 - mae: 0.3507 - val_loss: 0.1847 - val_mae: 0.3696\n", + "Epoch 243/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.1697 - mae: 0.3508 - val_loss: 0.1845 - val_mae: 0.3685\n", + "Epoch 244/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1697 - mae: 0.3505 - val_loss: 0.1846 - val_mae: 0.3689\n", + "Epoch 245/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.1701 - mae: 0.3519 - val_loss: 0.1856 - val_mae: 0.3727\n", + "Epoch 246/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.1701 - mae: 0.3519 - val_loss: 0.1850 - val_mae: 0.3708\n", + "Epoch 247/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1698 - mae: 0.3516 - val_loss: 0.1848 - val_mae: 0.3702\n", + "Epoch 248/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1697 - mae: 0.3508 - val_loss: 0.1844 - val_mae: 0.3671\n", + "Epoch 249/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1700 - mae: 0.3506 - val_loss: 0.1844 - val_mae: 0.3682\n", + "Epoch 250/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1696 - mae: 0.3503 - val_loss: 0.1844 - val_mae: 0.3676\n", + "Epoch 251/500\n", + "600/600 [==============================] - 0s 61us/sample - loss: 0.1697 - mae: 0.3504 - val_loss: 0.1844 - val_mae: 0.3676\n", + "Epoch 252/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1695 - mae: 0.3502 - val_loss: 0.1845 - val_mae: 0.3687\n", + "Epoch 253/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1695 - mae: 0.3507 - val_loss: 0.1847 - val_mae: 0.3698\n", + "Epoch 254/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1697 - mae: 0.3512 - val_loss: 0.1849 - val_mae: 0.3704\n", + "Epoch 255/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1698 - mae: 0.3514 - val_loss: 0.1848 - val_mae: 0.3700\n", + "Epoch 256/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1697 - mae: 0.3509 - val_loss: 0.1845 - val_mae: 0.3680\n", + "Epoch 257/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1696 - mae: 0.3503 - val_loss: 0.1844 - val_mae: 0.3679\n", + "Epoch 258/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1696 - mae: 0.3503 - val_loss: 0.1845 - val_mae: 0.3685\n", + "Epoch 259/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1695 - mae: 0.3505 - val_loss: 0.1846 - val_mae: 0.3689\n", + "Epoch 260/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1696 - mae: 0.3508 - val_loss: 0.1847 - val_mae: 0.3698\n", + "Epoch 261/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.1697 - mae: 0.3511 - val_loss: 0.1847 - val_mae: 0.3698\n", + "Epoch 262/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1699 - mae: 0.3510 - val_loss: 0.1845 - val_mae: 0.3684\n", + "Epoch 263/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1697 - mae: 0.3506 - val_loss: 0.1845 - val_mae: 0.3685\n", + "Epoch 264/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1696 - mae: 0.3506 - val_loss: 0.1846 - val_mae: 0.3692\n", + "Epoch 265/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1698 - mae: 0.3513 - val_loss: 0.1848 - val_mae: 0.3700\n", + "Epoch 266/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1696 - mae: 0.3509 - val_loss: 0.1846 - val_mae: 0.3691\n", + "Epoch 267/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1696 - mae: 0.3508 - val_loss: 0.1847 - val_mae: 0.3696\n", + "Epoch 268/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.1697 - mae: 0.3507 - val_loss: 0.1845 - val_mae: 0.3681\n", + "Epoch 269/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1696 - mae: 0.3504 - val_loss: 0.1846 - val_mae: 0.3686\n", + "Epoch 270/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1697 - mae: 0.3510 - val_loss: 0.1848 - val_mae: 0.3699\n", + "Epoch 271/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.1699 - mae: 0.3516 - val_loss: 0.1848 - val_mae: 0.3701\n", + "Epoch 272/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1698 - mae: 0.3509 - val_loss: 0.1845 - val_mae: 0.3683\n", + "Epoch 273/500\n", + "600/600 [==============================] - 0s 41us/sample - loss: 0.1696 - mae: 0.3508 - val_loss: 0.1848 - val_mae: 0.3699\n", + "Epoch 274/500\n", + "600/600 [==============================] - 0s 41us/sample - loss: 0.1696 - mae: 0.3510 - val_loss: 0.1847 - val_mae: 0.3697\n", + "Epoch 275/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1696 - mae: 0.3508 - val_loss: 0.1846 - val_mae: 0.3690\n", + "Epoch 276/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1696 - mae: 0.3509 - val_loss: 0.1847 - val_mae: 0.3693\n", + "Epoch 277/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1695 - mae: 0.3504 - val_loss: 0.1845 - val_mae: 0.3679\n", + "Epoch 278/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1697 - mae: 0.3502 - val_loss: 0.1845 - val_mae: 0.3678\n", + "Epoch 279/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1697 - mae: 0.3505 - val_loss: 0.1846 - val_mae: 0.3687\n", + "Epoch 280/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1696 - mae: 0.3506 - val_loss: 0.1846 - val_mae: 0.3688\n", + "Epoch 281/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1698 - mae: 0.3510 - val_loss: 0.1848 - val_mae: 0.3700\n", + "Epoch 282/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1696 - mae: 0.3509 - val_loss: 0.1847 - val_mae: 0.3694\n", + "Epoch 283/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1696 - mae: 0.3507 - val_loss: 0.1846 - val_mae: 0.3688\n", + "Epoch 284/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.1696 - mae: 0.3508 - val_loss: 0.1847 - val_mae: 0.3692\n", + "Epoch 285/500\n", + "600/600 [==============================] - 0s 58us/sample - loss: 0.1695 - mae: 0.3505 - val_loss: 0.1846 - val_mae: 0.3682\n", + "Epoch 286/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1699 - mae: 0.3501 - val_loss: 0.1846 - val_mae: 0.3664\n", + "Epoch 287/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1698 - mae: 0.3502 - val_loss: 0.1845 - val_mae: 0.3682\n", + "Epoch 288/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1696 - mae: 0.3504 - val_loss: 0.1846 - val_mae: 0.3683\n", + "Epoch 289/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1695 - mae: 0.3504 - val_loss: 0.1846 - val_mae: 0.3690\n", + "Epoch 290/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1696 - mae: 0.3506 - val_loss: 0.1847 - val_mae: 0.3689\n", + "Epoch 291/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1696 - mae: 0.3506 - val_loss: 0.1847 - val_mae: 0.3694\n", + "Epoch 292/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1698 - mae: 0.3508 - val_loss: 0.1846 - val_mae: 0.3683\n", + "Epoch 293/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1701 - mae: 0.3513 - val_loss: 0.1850 - val_mae: 0.3705\n", + "Epoch 294/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1702 - mae: 0.3509 - val_loss: 0.1845 - val_mae: 0.3678\n", + "Epoch 295/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1696 - mae: 0.3505 - val_loss: 0.1849 - val_mae: 0.3702\n", + "Epoch 296/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1697 - mae: 0.3510 - val_loss: 0.1848 - val_mae: 0.3699\n", + "Epoch 297/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1697 - mae: 0.3509 - val_loss: 0.1847 - val_mae: 0.3691\n", + "Epoch 298/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1696 - mae: 0.3507 - val_loss: 0.1848 - val_mae: 0.3695\n", + "Epoch 299/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.1696 - mae: 0.3507 - val_loss: 0.1847 - val_mae: 0.3690\n", + "Epoch 300/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1696 - mae: 0.3505 - val_loss: 0.1846 - val_mae: 0.3684\n", + "Epoch 301/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1696 - mae: 0.3504 - val_loss: 0.1846 - val_mae: 0.3685\n", + "Epoch 302/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1698 - mae: 0.3507 - val_loss: 0.1848 - val_mae: 0.3696\n", + "Epoch 303/500\n", + "600/600 [==============================] - 0s 41us/sample - loss: 0.1695 - mae: 0.3506 - val_loss: 0.1846 - val_mae: 0.3684\n", + "Epoch 304/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.1700 - mae: 0.3502 - val_loss: 0.1845 - val_mae: 0.3667\n", + "Epoch 305/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1696 - mae: 0.3498 - val_loss: 0.1845 - val_mae: 0.3679\n", + "Epoch 306/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.1699 - mae: 0.3509 - val_loss: 0.1850 - val_mae: 0.3706\n", + "Epoch 307/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1697 - mae: 0.3513 - val_loss: 0.1847 - val_mae: 0.3694\n", + "Epoch 308/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1697 - mae: 0.3506 - val_loss: 0.1845 - val_mae: 0.3682\n", + "Epoch 309/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1697 - mae: 0.3506 - val_loss: 0.1847 - val_mae: 0.3691\n", + "Epoch 310/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1696 - mae: 0.3504 - val_loss: 0.1845 - val_mae: 0.3680\n", + "Epoch 311/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1699 - mae: 0.3503 - val_loss: 0.1845 - val_mae: 0.3677\n", + "Epoch 312/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1694 - mae: 0.3502 - val_loss: 0.1847 - val_mae: 0.3692\n", + "Epoch 313/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1698 - mae: 0.3512 - val_loss: 0.1850 - val_mae: 0.3706\n", + "Epoch 314/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1698 - mae: 0.3509 - val_loss: 0.1845 - val_mae: 0.3678\n", + "Epoch 315/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1697 - mae: 0.3503 - val_loss: 0.1845 - val_mae: 0.3674\n", + "Epoch 316/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1697 - mae: 0.3503 - val_loss: 0.1845 - val_mae: 0.3680\n", + "Epoch 317/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1696 - mae: 0.3501 - val_loss: 0.1845 - val_mae: 0.3675\n", + "Epoch 318/500\n", + "600/600 [==============================] - 0s 59us/sample - loss: 0.1697 - mae: 0.3500 - val_loss: 0.1845 - val_mae: 0.3674\n", + "Epoch 319/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1697 - mae: 0.3499 - val_loss: 0.1845 - val_mae: 0.3672\n", + "Epoch 320/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1697 - mae: 0.3503 - val_loss: 0.1846 - val_mae: 0.3685\n", + "Epoch 321/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1697 - mae: 0.3507 - val_loss: 0.1847 - val_mae: 0.3695\n", + "Epoch 322/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1696 - mae: 0.3505 - val_loss: 0.1845 - val_mae: 0.3677\n", + "Epoch 323/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1697 - mae: 0.3501 - val_loss: 0.1845 - val_mae: 0.3676\n", + "Epoch 324/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1696 - mae: 0.3502 - val_loss: 0.1845 - val_mae: 0.3680\n", + "Epoch 325/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.1696 - mae: 0.3505 - val_loss: 0.1846 - val_mae: 0.3690\n", + "Epoch 326/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1696 - mae: 0.3504 - val_loss: 0.1845 - val_mae: 0.3682\n", + "Epoch 327/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1697 - mae: 0.3503 - val_loss: 0.1845 - val_mae: 0.3682\n", + "Epoch 328/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1696 - mae: 0.3504 - val_loss: 0.1845 - val_mae: 0.3682\n", + "Epoch 329/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1695 - mae: 0.3503 - val_loss: 0.1846 - val_mae: 0.3684\n", + "Epoch 330/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1696 - mae: 0.3506 - val_loss: 0.1846 - val_mae: 0.3691\n", + "Epoch 331/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1699 - mae: 0.3512 - val_loss: 0.1847 - val_mae: 0.3697\n", + "Epoch 332/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1696 - mae: 0.3507 - val_loss: 0.1846 - val_mae: 0.3688\n", + "Epoch 333/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1702 - mae: 0.3514 - val_loss: 0.1847 - val_mae: 0.3696\n", + "Epoch 334/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1695 - mae: 0.3505 - val_loss: 0.1845 - val_mae: 0.3678\n", + "Epoch 335/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1696 - mae: 0.3503 - val_loss: 0.1845 - val_mae: 0.3680\n", + "Epoch 336/500\n", + "600/600 [==============================] - 0s 40us/sample - loss: 0.1697 - mae: 0.3501 - val_loss: 0.1845 - val_mae: 0.3675\n", + "Epoch 337/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1696 - mae: 0.3502 - val_loss: 0.1846 - val_mae: 0.3688\n", + "Epoch 338/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1697 - mae: 0.3506 - val_loss: 0.1846 - val_mae: 0.3688\n", + "Epoch 339/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1700 - mae: 0.3513 - val_loss: 0.1851 - val_mae: 0.3711\n", + "Epoch 340/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1697 - mae: 0.3511 - val_loss: 0.1846 - val_mae: 0.3689\n", + "Epoch 341/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.1696 - mae: 0.3505 - val_loss: 0.1845 - val_mae: 0.3677\n", + "Epoch 342/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1701 - mae: 0.3509 - val_loss: 0.1848 - val_mae: 0.3700\n", + "Epoch 343/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1696 - mae: 0.3510 - val_loss: 0.1847 - val_mae: 0.3692\n", + "Epoch 344/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1697 - mae: 0.3506 - val_loss: 0.1846 - val_mae: 0.3682\n", + "Epoch 345/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1696 - mae: 0.3504 - val_loss: 0.1847 - val_mae: 0.3690\n", + "Epoch 346/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1695 - mae: 0.3511 - val_loss: 0.1851 - val_mae: 0.3711\n", + "Epoch 347/500\n", + "600/600 [==============================] - 0s 65us/sample - loss: 0.1697 - mae: 0.3513 - val_loss: 0.1849 - val_mae: 0.3701\n", + "Epoch 348/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1694 - mae: 0.3507 - val_loss: 0.1846 - val_mae: 0.3683\n", + "Epoch 349/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.1696 - mae: 0.3501 - val_loss: 0.1845 - val_mae: 0.3672\n", + "Epoch 350/500\n", + "600/600 [==============================] - 0s 59us/sample - loss: 0.1698 - mae: 0.3505 - val_loss: 0.1846 - val_mae: 0.3684\n", + "Epoch 351/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1696 - mae: 0.3502 - val_loss: 0.1845 - val_mae: 0.3679\n", + "Epoch 352/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1695 - mae: 0.3504 - val_loss: 0.1847 - val_mae: 0.3692\n", + "Epoch 353/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1697 - mae: 0.3509 - val_loss: 0.1849 - val_mae: 0.3701\n", + "Epoch 354/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1696 - mae: 0.3508 - val_loss: 0.1847 - val_mae: 0.3689\n", + "Epoch 355/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1697 - mae: 0.3508 - val_loss: 0.1846 - val_mae: 0.3685\n", + "Epoch 356/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1701 - mae: 0.3504 - val_loss: 0.1846 - val_mae: 0.3664\n", + "Epoch 357/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1699 - mae: 0.3503 - val_loss: 0.1847 - val_mae: 0.3689\n", + "Epoch 358/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1696 - mae: 0.3504 - val_loss: 0.1846 - val_mae: 0.3684\n", + "Epoch 359/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1695 - mae: 0.3503 - val_loss: 0.1846 - val_mae: 0.3683\n", + "Epoch 360/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1696 - mae: 0.3504 - val_loss: 0.1846 - val_mae: 0.3681\n", + "Epoch 361/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1697 - mae: 0.3504 - val_loss: 0.1846 - val_mae: 0.3685\n", + "Epoch 362/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1698 - mae: 0.3503 - val_loss: 0.1845 - val_mae: 0.3676\n", + "Epoch 363/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1696 - mae: 0.3505 - val_loss: 0.1848 - val_mae: 0.3695\n", + "Epoch 364/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1696 - mae: 0.3506 - val_loss: 0.1847 - val_mae: 0.3688\n", + "Epoch 365/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1696 - mae: 0.3507 - val_loss: 0.1849 - val_mae: 0.3699\n", + "Epoch 366/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1697 - mae: 0.3511 - val_loss: 0.1849 - val_mae: 0.3701\n", + "Epoch 367/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1696 - mae: 0.3508 - val_loss: 0.1846 - val_mae: 0.3688\n", + "Epoch 368/500\n", + "600/600 [==============================] - 0s 39us/sample - loss: 0.1696 - mae: 0.3507 - val_loss: 0.1846 - val_mae: 0.3688\n", + "Epoch 369/500\n", + "600/600 [==============================] - 0s 40us/sample - loss: 0.1698 - mae: 0.3504 - val_loss: 0.1845 - val_mae: 0.3678\n", + "Epoch 370/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1697 - mae: 0.3507 - val_loss: 0.1848 - val_mae: 0.3697\n", + "Epoch 371/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1698 - mae: 0.3508 - val_loss: 0.1846 - val_mae: 0.3683\n", + "Epoch 372/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1696 - mae: 0.3506 - val_loss: 0.1847 - val_mae: 0.3692\n", + "Epoch 373/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1696 - mae: 0.3508 - val_loss: 0.1846 - val_mae: 0.3689\n", + "Epoch 374/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1697 - mae: 0.3504 - val_loss: 0.1845 - val_mae: 0.3677\n", + "Epoch 375/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.1697 - mae: 0.3506 - val_loss: 0.1846 - val_mae: 0.3691\n", + "Epoch 376/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1696 - mae: 0.3505 - val_loss: 0.1846 - val_mae: 0.3684\n", + "Epoch 377/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.1697 - mae: 0.3502 - val_loss: 0.1845 - val_mae: 0.3678\n", + "Epoch 378/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1700 - mae: 0.3507 - val_loss: 0.1847 - val_mae: 0.3690\n", + "Epoch 379/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1695 - mae: 0.3501 - val_loss: 0.1845 - val_mae: 0.3670\n", + "Epoch 380/500\n", + "600/600 [==============================] - 0s 60us/sample - loss: 0.1696 - mae: 0.3501 - val_loss: 0.1846 - val_mae: 0.3683\n", + "Epoch 381/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1695 - mae: 0.3505 - val_loss: 0.1847 - val_mae: 0.3691\n", + "Epoch 382/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1696 - mae: 0.3506 - val_loss: 0.1847 - val_mae: 0.3690\n", + "Epoch 383/500\n", + "600/600 [==============================] - 0s 59us/sample - loss: 0.1696 - mae: 0.3506 - val_loss: 0.1847 - val_mae: 0.3693\n", + "Epoch 384/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1697 - mae: 0.3511 - val_loss: 0.1850 - val_mae: 0.3703\n", + "Epoch 385/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.1699 - mae: 0.3510 - val_loss: 0.1847 - val_mae: 0.3689\n", + "Epoch 386/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1697 - mae: 0.3511 - val_loss: 0.1851 - val_mae: 0.3709\n", + "Epoch 387/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1698 - mae: 0.3512 - val_loss: 0.1846 - val_mae: 0.3688\n", + "Epoch 388/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1696 - mae: 0.3506 - val_loss: 0.1846 - val_mae: 0.3687\n", + "Epoch 389/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.1697 - mae: 0.3510 - val_loss: 0.1848 - val_mae: 0.3700\n", + "Epoch 390/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.1696 - mae: 0.3510 - val_loss: 0.1847 - val_mae: 0.3694\n", + "Epoch 391/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1701 - mae: 0.3505 - val_loss: 0.1846 - val_mae: 0.3666\n", + "Epoch 392/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1697 - mae: 0.3501 - val_loss: 0.1846 - val_mae: 0.3681\n", + "Epoch 393/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1696 - mae: 0.3506 - val_loss: 0.1848 - val_mae: 0.3698\n", + "Epoch 394/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1696 - mae: 0.3510 - val_loss: 0.1847 - val_mae: 0.3693\n", + "Epoch 395/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1699 - mae: 0.3507 - val_loss: 0.1845 - val_mae: 0.3675\n", + "Epoch 396/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1695 - mae: 0.3501 - val_loss: 0.1847 - val_mae: 0.3693\n", + "Epoch 397/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1698 - mae: 0.3510 - val_loss: 0.1848 - val_mae: 0.3698\n", + "Epoch 398/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1696 - mae: 0.3508 - val_loss: 0.1846 - val_mae: 0.3687\n", + "Epoch 399/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1695 - mae: 0.3502 - val_loss: 0.1845 - val_mae: 0.3673\n", + "Epoch 400/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1697 - mae: 0.3498 - val_loss: 0.1845 - val_mae: 0.3667\n", + "Epoch 401/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1696 - mae: 0.3498 - val_loss: 0.1845 - val_mae: 0.3681\n", + "Epoch 402/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1696 - mae: 0.3505 - val_loss: 0.1847 - val_mae: 0.3692\n", + "Epoch 403/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1697 - mae: 0.3506 - val_loss: 0.1846 - val_mae: 0.3687\n", + "Epoch 404/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1699 - mae: 0.3502 - val_loss: 0.1846 - val_mae: 0.3667\n", + "Epoch 405/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1697 - mae: 0.3500 - val_loss: 0.1846 - val_mae: 0.3683\n", + "Epoch 406/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1696 - mae: 0.3503 - val_loss: 0.1847 - val_mae: 0.3689\n", + "Epoch 407/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1695 - mae: 0.3504 - val_loss: 0.1847 - val_mae: 0.3684\n", + "Epoch 408/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1697 - mae: 0.3502 - val_loss: 0.1846 - val_mae: 0.3673\n", + "Epoch 409/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1696 - mae: 0.3499 - val_loss: 0.1846 - val_mae: 0.3678\n", + "Epoch 410/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1696 - mae: 0.3502 - val_loss: 0.1846 - val_mae: 0.3682\n", + "Epoch 411/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1697 - mae: 0.3499 - val_loss: 0.1846 - val_mae: 0.3668\n", + "Epoch 412/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1697 - mae: 0.3496 - val_loss: 0.1846 - val_mae: 0.3673\n", + "Epoch 413/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1698 - mae: 0.3508 - val_loss: 0.1852 - val_mae: 0.3710\n", + "Epoch 414/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1703 - mae: 0.3519 - val_loss: 0.1854 - val_mae: 0.3716\n", + "Epoch 415/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1695 - mae: 0.3511 - val_loss: 0.1846 - val_mae: 0.3686\n", + "Epoch 416/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.1696 - mae: 0.3499 - val_loss: 0.1845 - val_mae: 0.3666\n", + "Epoch 417/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1700 - mae: 0.3496 - val_loss: 0.1846 - val_mae: 0.3665\n", + "Epoch 418/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1694 - mae: 0.3497 - val_loss: 0.1847 - val_mae: 0.3687\n", + "Epoch 419/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1695 - mae: 0.3505 - val_loss: 0.1849 - val_mae: 0.3698\n", + "Epoch 420/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1697 - mae: 0.3509 - val_loss: 0.1850 - val_mae: 0.3702\n", + "Epoch 421/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1697 - mae: 0.3511 - val_loss: 0.1849 - val_mae: 0.3700\n", + "Epoch 422/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1696 - mae: 0.3507 - val_loss: 0.1846 - val_mae: 0.3686\n", + "Epoch 423/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1695 - mae: 0.3502 - val_loss: 0.1845 - val_mae: 0.3677\n", + "Epoch 424/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1696 - mae: 0.3498 - val_loss: 0.1845 - val_mae: 0.3668\n", + "Epoch 425/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1697 - mae: 0.3497 - val_loss: 0.1845 - val_mae: 0.3671\n", + "Epoch 426/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1696 - mae: 0.3497 - val_loss: 0.1846 - val_mae: 0.3676\n", + "Epoch 427/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1696 - mae: 0.3500 - val_loss: 0.1847 - val_mae: 0.3683\n", + "Epoch 428/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1696 - mae: 0.3502 - val_loss: 0.1847 - val_mae: 0.3686\n", + "Epoch 429/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1697 - mae: 0.3506 - val_loss: 0.1848 - val_mae: 0.3694\n", + "Epoch 430/500\n", + "600/600 [==============================] - 0s 40us/sample - loss: 0.1698 - mae: 0.3502 - val_loss: 0.1846 - val_mae: 0.3675\n", + "Epoch 431/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1697 - mae: 0.3498 - val_loss: 0.1846 - val_mae: 0.3675\n", + "Epoch 432/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1696 - mae: 0.3504 - val_loss: 0.1850 - val_mae: 0.3703\n", + "Epoch 433/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1699 - mae: 0.3514 - val_loss: 0.1853 - val_mae: 0.3713\n", + "Epoch 434/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1700 - mae: 0.3510 - val_loss: 0.1846 - val_mae: 0.3686\n", + "Epoch 435/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1699 - mae: 0.3509 - val_loss: 0.1846 - val_mae: 0.3689\n", + "Epoch 436/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1696 - mae: 0.3509 - val_loss: 0.1849 - val_mae: 0.3703\n", + "Epoch 437/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1697 - mae: 0.3511 - val_loss: 0.1847 - val_mae: 0.3696\n", + "Epoch 438/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1696 - mae: 0.3509 - val_loss: 0.1846 - val_mae: 0.3691\n", + "Epoch 439/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1695 - mae: 0.3506 - val_loss: 0.1845 - val_mae: 0.3682\n", + "Epoch 440/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1698 - mae: 0.3506 - val_loss: 0.1845 - val_mae: 0.3683\n", + "Epoch 441/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1696 - mae: 0.3501 - val_loss: 0.1845 - val_mae: 0.3670\n", + "Epoch 442/500\n", + "600/600 [==============================] - 0s 59us/sample - loss: 0.1697 - mae: 0.3502 - val_loss: 0.1846 - val_mae: 0.3690\n", + "Epoch 443/500\n", + "600/600 [==============================] - 0s 82us/sample - loss: 0.1704 - mae: 0.3519 - val_loss: 0.1849 - val_mae: 0.3702\n", + "Epoch 444/500\n", + "600/600 [==============================] - 0s 62us/sample - loss: 0.1696 - mae: 0.3507 - val_loss: 0.1846 - val_mae: 0.3685\n", + "Epoch 445/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.1697 - mae: 0.3504 - val_loss: 0.1845 - val_mae: 0.3679\n", + "Epoch 446/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1697 - mae: 0.3501 - val_loss: 0.1845 - val_mae: 0.3673\n", + "Epoch 447/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1700 - mae: 0.3501 - val_loss: 0.1845 - val_mae: 0.3671\n", + "Epoch 448/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.1705 - mae: 0.3515 - val_loss: 0.1852 - val_mae: 0.3713\n", + "Epoch 449/500\n", + "600/600 [==============================] - 0s 59us/sample - loss: 0.1698 - mae: 0.3512 - val_loss: 0.1846 - val_mae: 0.3687\n", + "Epoch 450/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1696 - mae: 0.3504 - val_loss: 0.1845 - val_mae: 0.3682\n", + "Epoch 451/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1695 - mae: 0.3504 - val_loss: 0.1846 - val_mae: 0.3687\n", + "Epoch 452/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1696 - mae: 0.3504 - val_loss: 0.1845 - val_mae: 0.3681\n", + "Epoch 453/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1696 - mae: 0.3502 - val_loss: 0.1846 - val_mae: 0.3683\n", + "Epoch 454/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1696 - mae: 0.3504 - val_loss: 0.1846 - val_mae: 0.3686\n", + "Epoch 455/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1698 - mae: 0.3506 - val_loss: 0.1846 - val_mae: 0.3682\n", + "Epoch 456/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1698 - mae: 0.3508 - val_loss: 0.1847 - val_mae: 0.3695\n", + "Epoch 457/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1697 - mae: 0.3511 - val_loss: 0.1847 - val_mae: 0.3697\n", + "Epoch 458/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1695 - mae: 0.3507 - val_loss: 0.1845 - val_mae: 0.3684\n", + "Epoch 459/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1698 - mae: 0.3504 - val_loss: 0.1845 - val_mae: 0.3677\n", + "Epoch 460/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1696 - mae: 0.3506 - val_loss: 0.1846 - val_mae: 0.3692\n", + "Epoch 461/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1696 - mae: 0.3509 - val_loss: 0.1847 - val_mae: 0.3696\n", + "Epoch 462/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.1696 - mae: 0.3510 - val_loss: 0.1846 - val_mae: 0.3692\n", + "Epoch 463/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1698 - mae: 0.3506 - val_loss: 0.1845 - val_mae: 0.3674\n", + "Epoch 464/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1697 - mae: 0.3502 - val_loss: 0.1845 - val_mae: 0.3685\n", + "Epoch 465/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1695 - mae: 0.3506 - val_loss: 0.1847 - val_mae: 0.3695\n", + "Epoch 466/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1698 - mae: 0.3513 - val_loss: 0.1850 - val_mae: 0.3706\n", + "Epoch 467/500\n", + "600/600 [==============================] - 0s 40us/sample - loss: 0.1698 - mae: 0.3512 - val_loss: 0.1847 - val_mae: 0.3698\n", + "Epoch 468/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1700 - mae: 0.3519 - val_loss: 0.1850 - val_mae: 0.3712\n", + "Epoch 469/500\n", + "600/600 [==============================] - 0s 41us/sample - loss: 0.1697 - mae: 0.3515 - val_loss: 0.1847 - val_mae: 0.3700\n", + "Epoch 470/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1695 - mae: 0.3508 - val_loss: 0.1845 - val_mae: 0.3683\n", + "Epoch 471/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.1697 - mae: 0.3503 - val_loss: 0.1845 - val_mae: 0.3675\n", + "Epoch 472/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1697 - mae: 0.3502 - val_loss: 0.1845 - val_mae: 0.3682\n", + "Epoch 473/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1697 - mae: 0.3506 - val_loss: 0.1846 - val_mae: 0.3689\n", + "Epoch 474/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1696 - mae: 0.3505 - val_loss: 0.1845 - val_mae: 0.3682\n", + "Epoch 475/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.1697 - mae: 0.3506 - val_loss: 0.1845 - val_mae: 0.3683\n", + "Epoch 476/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.1695 - mae: 0.3506 - val_loss: 0.1847 - val_mae: 0.3697\n", + "Epoch 477/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1696 - mae: 0.3511 - val_loss: 0.1848 - val_mae: 0.3701\n", + "Epoch 478/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1697 - mae: 0.3512 - val_loss: 0.1848 - val_mae: 0.3702\n", + "Epoch 479/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1695 - mae: 0.3507 - val_loss: 0.1845 - val_mae: 0.3676\n", + "Epoch 480/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1699 - mae: 0.3502 - val_loss: 0.1845 - val_mae: 0.3669\n", + "Epoch 481/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1697 - mae: 0.3500 - val_loss: 0.1845 - val_mae: 0.3676\n", + "Epoch 482/500\n", + "600/600 [==============================] - 0s 59us/sample - loss: 0.1695 - mae: 0.3506 - val_loss: 0.1850 - val_mae: 0.3706\n", + "Epoch 483/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1698 - mae: 0.3516 - val_loss: 0.1853 - val_mae: 0.3716\n", + "Epoch 484/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1699 - mae: 0.3515 - val_loss: 0.1847 - val_mae: 0.3692\n", + "Epoch 485/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1696 - mae: 0.3507 - val_loss: 0.1846 - val_mae: 0.3687\n", + "Epoch 486/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1699 - mae: 0.3505 - val_loss: 0.1845 - val_mae: 0.3679\n", + "Epoch 487/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1695 - mae: 0.3506 - val_loss: 0.1848 - val_mae: 0.3698\n", + "Epoch 488/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1701 - mae: 0.3517 - val_loss: 0.1851 - val_mae: 0.3709\n", + "Epoch 489/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1698 - mae: 0.3509 - val_loss: 0.1845 - val_mae: 0.3678\n", + "Epoch 490/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1696 - mae: 0.3502 - val_loss: 0.1846 - val_mae: 0.3680\n", + "Epoch 491/500\n", + "600/600 [==============================] - 0s 42us/sample - loss: 0.1696 - mae: 0.3502 - val_loss: 0.1846 - val_mae: 0.3683\n", + "Epoch 492/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1699 - mae: 0.3512 - val_loss: 0.1853 - val_mae: 0.3714\n", + "Epoch 493/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1698 - mae: 0.3513 - val_loss: 0.1848 - val_mae: 0.3697\n", + "Epoch 494/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.1696 - mae: 0.3509 - val_loss: 0.1847 - val_mae: 0.3691\n", + "Epoch 495/500\n", + "600/600 [==============================] - 0s 41us/sample - loss: 0.1695 - mae: 0.3504 - val_loss: 0.1845 - val_mae: 0.3679\n", + "Epoch 496/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1696 - mae: 0.3503 - val_loss: 0.1846 - val_mae: 0.3684\n", + "Epoch 497/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1695 - mae: 0.3505 - val_loss: 0.1847 - val_mae: 0.3693\n", + "Epoch 498/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1696 - mae: 0.3510 - val_loss: 0.1848 - val_mae: 0.3699\n", + "Epoch 499/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1695 - mae: 0.3508 - val_loss: 0.1846 - val_mae: 0.3690\n", + "Epoch 500/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1697 - mae: 0.3503 - val_loss: 0.1845 - val_mae: 0.3681\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cRE8KpEqVfaS", + "colab_type": "text" + }, + "source": [ + "### 3. Plot Metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SDsjqfjFm7Fz", + "colab_type": "text" + }, + "source": [ + "**1. Mean Squared Error**\n", + "\n", + "During training, the model's performance is constantly being measured against both our training data and the validation data that we set aside earlier. Training produces a log of data that tells us how the model's performance changed over the course of the training process.\n", + "\n", + "The following cells will display some of that data in a graphical form:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "CmvA-ksoln8r", + "colab_type": "code", + "outputId": "2796d3ca-deb7-4cf9-cc01-78df3cacf12a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + } + }, + "source": [ + "# Draw a graph of the loss, which is the distance between\n", + "# the predicted and actual values during training and validation.\n", + "loss = history_1.history['loss']\n", + "val_loss = history_1.history['val_loss']\n", + "\n", + "epochs = range(1, len(loss) + 1)\n", + "\n", + "plt.plot(epochs, loss, 'g.', label='Training loss')\n", + "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n", + "plt.title('Training and validation loss')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Loss')\n", + "plt.legend()\n", + "plt.show()" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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58+ezZs0aKisrOfbYYxk7diwA55xzDpdeeikA3/3ud/n1r3/N17/+daZMmcIZZ5zBueee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\n1pQImtHgJLPjfgp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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iOFBSbPcYCN4", + "colab_type": "text" + }, + "source": [ + "The graph shows the _loss_ (or the difference between the model's predictions and the actual data) for each epoch. There are several ways to calculate loss, and the method we have used is _mean squared error_. There is a distinct loss value given for the training and the validation data.\n", + "\n", + "As we can see, the amount of loss rapidly decreases over the first 25 epochs, before flattening out. This means that the model is improving and producing more accurate predictions!\n", + "\n", + "Our goal is to stop training when either the model is no longer improving, or when the _training loss_ is less than the _validation loss_, which would mean that the model has learned to predict the training data so well that it can no longer generalize to new data.\n", + "\n", + "To make the flatter part of the graph more readable, let's skip the first 50 epochs:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Zo0RYroFZYIV", + "colab_type": "code", + "outputId": "5844429f-cb52-41e0-c41c-52485efcd0ac", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + } + }, + "source": [ + "# Exclude the first few epochs so the graph is easier to read\n", + "SKIP = 50\n", + "\n", + "plt.plot(epochs[SKIP:], loss[SKIP:], 'g.', label='Training loss')\n", + "plt.plot(epochs[SKIP:], val_loss[SKIP:], 'b.', label='Validation loss')\n", + "plt.title('Training and validation loss')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Loss')\n", + "plt.legend()\n", + "plt.show()" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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8nOXLl3PEEUfw6KOP8uUvf5ny8nLWrl3bLrO+KkCE2vsHV0QOl4xcX2wzU2zz0uOPP86Z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\nztnDzq675kTHRO9vZu/Mus+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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "W4EQD-Bb8hLM", + "colab_type": "text" + }, + "source": [ + "From the plot, we can see that loss continues to reduce until around 500 epochs, at which point it is mostly stable. This means that there's no need to train our network beyond 500 epochs.\n", + "\n", + "However, we can also see that the lowest loss value is still around 0.155. This means that our network's predictions are off by an average of ~15%. In addition, the validation loss values jump around a lot, and is sometimes even higher.\n", + "\n", + "**2. Mean Absolute Error**\n", + "\n", + "To gain more insight into our model's performance we can plot some more data. This time, we'll plot the _mean absolute error_, which is another way of measuring how far the network's predictions are from the actual numbers:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Md9E_azmpkZU", + "colab_type": "code", + "outputId": "90fff6f3-8dc1-42ec-a0e2-f2434c790a3d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + } + }, + "source": [ + "plt.clf()\n", + "\n", + "# Draw a graph of mean absolute error, which is another way of\n", + "# measuring the amount of error in the prediction.\n", + "mae = history_1.history['mae']\n", + "val_mae = history_1.history['val_mae']\n", + "\n", + "plt.plot(epochs[SKIP:], mae[SKIP:], 'g.', label='Training MAE')\n", + "plt.plot(epochs[SKIP:], val_mae[SKIP:], 'b.', label='Validation MAE')\n", + "plt.title('Training and validation mean absolute error')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('MAE')\n", + "plt.legend()\n", + "plt.show()" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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EKClpWK+vt05zhmF0XExBZEgmHeYAysqge3frNGcYRsfHFESGZDr0t3WaMwyj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\n6uudn/+++5wbyicadfKMHet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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ctawd0CXAVEw", + "colab_type": "text" + }, + "source": [ + "This graph of _mean absolute error_ tells another story. We can see that training data shows consistently lower error than validation data, which means that the network may have _overfit_, or learned the training data so rigidly that it can't make effective predictions about new data.\n", + "\n", + "In addition, the mean absolute error values are quite high, ~0.305 at best, which means some of the model's predictions are at least 30% off. A 30% error means we are very far from accurately modelling the sine wave function.\n", + "\n", + "**3. Actual vs Predicted Outputs**\n", + "\n", + "To get more insight into what is happening, we can plot our network's predictions for the training data against the expected values:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "i13eVIT3B9Mj", + "colab_type": "code", + "outputId": "372e169f-f97d-47ee-e64c-162b8ba4e38c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 281 + } + }, + "source": [ + "# Use the model to make predictions from our validation data\n", + "predictions = model_1.predict(x_train)\n", + "\n", + "# Plot the predictions along with to the test data\n", + "plt.clf()\n", + "plt.title('Training data predicted vs actual values')\n", + "plt.plot(x_test, y_test, 'b.', label='Actual')\n", + "plt.plot(x_train, predictions, 'r.', label='Predicted')\n", + "plt.legend()\n", + "plt.show()" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wokallj1D21L", + "colab_type": "text" + }, + "source": [ + "Oh dear! The graph makes it clear that our network has learned to approximate the sine function in a very limited way. From `0 <= x <= 1.1` the line mostly fits, but for the rest of our `x` values it is a rough approximation at best.\n", + "\n", + "The rigidity of this fit suggests that the model does not have enough capacity to learn the full complexity of the sine wave function, so it's only able to approximate it in an overly simplistic way. By making our model bigger, we should be able to improve its performance." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "T7sL-hWtoAZC", + "colab_type": "text" + }, + "source": [ + "## Training a Larger Model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aQd0JSdOoAbw", + "colab_type": "text" + }, + "source": [ + "### 1. Design the Model\n", + "To make our model bigger, let's add an additional layer of neurons. The following cell redefines our model in the same way as earlier, but with 16 neurons in the first layer and an additional layer of 16 neurons in the middle:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "oW0xus6AF-4o", + "colab_type": "code", + "colab": {} + }, + "source": [ + "model_2 = tf.keras.Sequential()\n", + "\n", + "# First layer takes a scalar input and feeds it through 16 \"neurons\". The\n", + "# neurons decide whether to activate based on the 'relu' activation function.\n", + "model_2.add(keras.layers.Dense(16, activation='relu', input_shape=(1,)))\n", + "\n", + "# The new second layer may help the network learn more complex representations\n", + "model_2.add(keras.layers.Dense(16, activation='relu'))\n", + "\n", + "# Final layer is a single neuron, since we want to output a single value\n", + "model_2.add(keras.layers.Dense(1))\n", + "\n", + "# Compile the model using a standard optimizer and loss function for regression\n", + "model_2.compile(optimizer='adam', loss='mse', metrics=['mae'])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Dv2SC409Grap", + "colab_type": "text" + }, + "source": [ + "### 2. Train the Model ###\n", + "\n", + "We'll now train the new model." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DPAUrdkmGq1M", + "colab_type": "code", + "outputId": "64730ff7-488e-4b74-d5a1-49a1b733e9e5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "source": [ + "history_2 = model_2.fit(x_train, y_train, epochs=500, batch_size=64,\n", + " validation_data=(x_validate, y_validate))" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Train on 600 samples, validate on 200 samples\n", + "Epoch 1/500\n", + "600/600 [==============================] - 0s 736us/sample - loss: 0.4245 - mae: 0.5529 - val_loss: 0.4310 - val_mae: 0.5678\n", + "Epoch 2/500\n", + "600/600 [==============================] - 0s 64us/sample - loss: 0.4056 - mae: 0.5462 - val_loss: 0.4138 - val_mae: 0.5548\n", + "Epoch 3/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.3897 - mae: 0.5302 - val_loss: 0.3974 - val_mae: 0.5437\n", + "Epoch 4/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.3743 - mae: 0.5181 - val_loss: 0.3815 - val_mae: 0.5336\n", + "Epoch 5/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.3602 - mae: 0.5128 - val_loss: 0.3677 - val_mae: 0.5276\n", + "Epoch 6/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.3436 - mae: 0.5010 - val_loss: 0.3504 - val_mae: 0.5140\n", + "Epoch 7/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.3281 - mae: 0.4859 - val_loss: 0.3340 - val_mae: 0.5021\n", + "Epoch 8/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.3127 - mae: 0.4748 - val_loss: 0.3177 - val_mae: 0.4921\n", + "Epoch 9/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.2961 - mae: 0.4626 - val_loss: 0.3012 - val_mae: 0.4794\n", + "Epoch 10/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.2797 - mae: 0.4502 - val_loss: 0.2851 - val_mae: 0.4687\n", + "Epoch 11/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.2635 - mae: 0.4391 - val_loss: 0.2699 - val_mae: 0.4589\n", + "Epoch 12/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.2467 - mae: 0.4251 - val_loss: 0.2523 - val_mae: 0.4414\n", + "Epoch 13/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.2312 - mae: 0.4107 - val_loss: 0.2369 - val_mae: 0.4293\n", + "Epoch 14/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.2149 - mae: 0.3971 - val_loss: 0.2225 - val_mae: 0.4168\n", + "Epoch 15/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.2031 - mae: 0.3861 - val_loss: 0.2085 - val_mae: 0.4023\n", + "Epoch 16/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1908 - mae: 0.3716 - val_loss: 0.1970 - val_mae: 0.3899\n", + "Epoch 17/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1777 - mae: 0.3590 - val_loss: 0.1881 - val_mae: 0.3810\n", + "Epoch 18/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1682 - mae: 0.3475 - val_loss: 0.1789 - val_mae: 0.3677\n", + "Epoch 19/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.1603 - mae: 0.3367 - val_loss: 0.1723 - val_mae: 0.3586\n", + "Epoch 20/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1536 - mae: 0.3276 - val_loss: 0.1668 - val_mae: 0.3500\n", + "Epoch 21/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1487 - mae: 0.3181 - val_loss: 0.1619 - val_mae: 0.3403\n", + "Epoch 22/500\n", + "600/600 [==============================] - 0s 74us/sample - loss: 0.1433 - mae: 0.3108 - val_loss: 0.1598 - val_mae: 0.3358\n", + "Epoch 23/500\n", + "600/600 [==============================] - 0s 58us/sample - loss: 0.1418 - mae: 0.3072 - val_loss: 0.1558 - val_mae: 0.3248\n", + "Epoch 24/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.1389 - mae: 0.2992 - val_loss: 0.1538 - val_mae: 0.3189\n", + "Epoch 25/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.1387 - mae: 0.2978 - val_loss: 0.1524 - val_mae: 0.3161\n", + "Epoch 26/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1346 - mae: 0.2904 - val_loss: 0.1510 - val_mae: 0.3112\n", + "Epoch 27/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1340 - mae: 0.2904 - val_loss: 0.1501 - val_mae: 0.3098\n", + "Epoch 28/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1313 - mae: 0.2849 - val_loss: 0.1489 - val_mae: 0.3042\n", + "Epoch 29/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1303 - mae: 0.2830 - val_loss: 0.1489 - val_mae: 0.3058\n", + "Epoch 30/500\n", + "600/600 [==============================] - 0s 63us/sample - loss: 0.1292 - mae: 0.2804 - val_loss: 0.1474 - val_mae: 0.2997\n", + "Epoch 31/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1286 - mae: 0.2781 - val_loss: 0.1467 - val_mae: 0.2998\n", + "Epoch 32/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.1274 - mae: 0.2774 - val_loss: 0.1463 - val_mae: 0.2990\n", + "Epoch 33/500\n", + "600/600 [==============================] - 0s 62us/sample - loss: 0.1268 - mae: 0.2758 - val_loss: 0.1451 - val_mae: 0.2945\n", + "Epoch 34/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.1295 - mae: 0.2746 - val_loss: 0.1449 - val_mae: 0.2966\n", + "Epoch 35/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.1278 - mae: 0.2760 - val_loss: 0.1438 - val_mae: 0.2937\n", + "Epoch 36/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1246 - mae: 0.2710 - val_loss: 0.1431 - val_mae: 0.2908\n", + "Epoch 37/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1247 - mae: 0.2693 - val_loss: 0.1434 - val_mae: 0.2939\n", + "Epoch 38/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.1237 - mae: 0.2702 - val_loss: 0.1415 - val_mae: 0.2893\n", + "Epoch 39/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1263 - mae: 0.2691 - val_loss: 0.1411 - val_mae: 0.2891\n", + "Epoch 40/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1238 - mae: 0.2693 - val_loss: 0.1408 - val_mae: 0.2906\n", + "Epoch 41/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.1209 - mae: 0.2659 - val_loss: 0.1393 - val_mae: 0.2859\n", + "Epoch 42/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.1216 - mae: 0.2644 - val_loss: 0.1387 - val_mae: 0.2842\n", + "Epoch 43/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1200 - mae: 0.2642 - val_loss: 0.1386 - val_mae: 0.2869\n", + "Epoch 44/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1193 - mae: 0.2626 - val_loss: 0.1370 - val_mae: 0.2814\n", + "Epoch 45/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.1187 - mae: 0.2625 - val_loss: 0.1362 - val_mae: 0.2829\n", + "Epoch 46/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1177 - mae: 0.2593 - val_loss: 0.1353 - val_mae: 0.2796\n", + "Epoch 47/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.1172 - mae: 0.2598 - val_loss: 0.1346 - val_mae: 0.2789\n", + "Epoch 48/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.1158 - mae: 0.2569 - val_loss: 0.1337 - val_mae: 0.2769\n", + "Epoch 49/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1176 - mae: 0.2590 - val_loss: 0.1329 - val_mae: 0.2761\n", + "Epoch 50/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1141 - mae: 0.2544 - val_loss: 0.1320 - val_mae: 0.2759\n", + "Epoch 51/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1138 - mae: 0.2536 - val_loss: 0.1312 - val_mae: 0.2741\n", + "Epoch 52/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1127 - mae: 0.2535 - val_loss: 0.1313 - val_mae: 0.2776\n", + "Epoch 53/500\n", + "600/600 [==============================] - 0s 60us/sample - loss: 0.1124 - mae: 0.2518 - val_loss: 0.1294 - val_mae: 0.2708\n", + "Epoch 54/500\n", + "600/600 [==============================] - 0s 61us/sample - loss: 0.1115 - mae: 0.2508 - val_loss: 0.1287 - val_mae: 0.2722\n", + "Epoch 55/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.1103 - mae: 0.2487 - val_loss: 0.1278 - val_mae: 0.2709\n", + "Epoch 56/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1094 - mae: 0.2485 - val_loss: 0.1267 - val_mae: 0.2687\n", + "Epoch 57/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1090 - mae: 0.2479 - val_loss: 0.1259 - val_mae: 0.2684\n", + "Epoch 58/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1118 - mae: 0.2456 - val_loss: 0.1256 - val_mae: 0.2695\n", + "Epoch 59/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1106 - mae: 0.2500 - val_loss: 0.1243 - val_mae: 0.2670\n", + "Epoch 60/500\n", + "600/600 [==============================] - 0s 59us/sample - loss: 0.1071 - mae: 0.2429 - val_loss: 0.1231 - val_mae: 0.2626\n", + "Epoch 61/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.1059 - mae: 0.2436 - val_loss: 0.1226 - val_mae: 0.2653\n", + "Epoch 62/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.1048 - mae: 0.2419 - val_loss: 0.1213 - val_mae: 0.2607\n", + "Epoch 63/500\n", + "600/600 [==============================] - 0s 65us/sample - loss: 0.1038 - mae: 0.2394 - val_loss: 0.1204 - val_mae: 0.2604\n", + "Epoch 64/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.1029 - mae: 0.2383 - val_loss: 0.1196 - val_mae: 0.2593\n", + "Epoch 65/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.1021 - mae: 0.2376 - val_loss: 0.1186 - val_mae: 0.2576\n", + "Epoch 66/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.1012 - mae: 0.2353 - val_loss: 0.1179 - val_mae: 0.2585\n", + "Epoch 67/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.1006 - mae: 0.2358 - val_loss: 0.1169 - val_mae: 0.2568\n", + "Epoch 68/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0996 - mae: 0.2346 - val_loss: 0.1158 - val_mae: 0.2553\n", + "Epoch 69/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0996 - mae: 0.2349 - val_loss: 0.1148 - val_mae: 0.2534\n", + "Epoch 70/500\n", + "600/600 [==============================] - 0s 62us/sample - loss: 0.0985 - mae: 0.2316 - val_loss: 0.1142 - val_mae: 0.2490\n", + "Epoch 71/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0986 - mae: 0.2327 - val_loss: 0.1144 - val_mae: 0.2559\n", + "Epoch 72/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0981 - mae: 0.2306 - val_loss: 0.1121 - val_mae: 0.2494\n", + "Epoch 73/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0966 - mae: 0.2308 - val_loss: 0.1118 - val_mae: 0.2521\n", + "Epoch 74/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0972 - mae: 0.2281 - val_loss: 0.1104 - val_mae: 0.2456\n", + "Epoch 75/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0960 - mae: 0.2293 - val_loss: 0.1101 - val_mae: 0.2500\n", + "Epoch 76/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0933 - mae: 0.2247 - val_loss: 0.1087 - val_mae: 0.2424\n", + "Epoch 77/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0922 - mae: 0.2221 - val_loss: 0.1080 - val_mae: 0.2453\n", + "Epoch 78/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0917 - mae: 0.2235 - val_loss: 0.1069 - val_mae: 0.2432\n", + "Epoch 79/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0922 - mae: 0.2204 - val_loss: 0.1061 - val_mae: 0.2394\n", + "Epoch 80/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0918 - mae: 0.2239 - val_loss: 0.1062 - val_mae: 0.2456\n", + "Epoch 81/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0908 - mae: 0.2220 - val_loss: 0.1048 - val_mae: 0.2372\n", + "Epoch 82/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0889 - mae: 0.2193 - val_loss: 0.1046 - val_mae: 0.2421\n", + "Epoch 83/500\n", + "600/600 [==============================] - 0s 62us/sample - loss: 0.0883 - mae: 0.2175 - val_loss: 0.1029 - val_mae: 0.2339\n", + "Epoch 84/500\n", + "600/600 [==============================] - 0s 64us/sample - loss: 0.0872 - mae: 0.2143 - val_loss: 0.1022 - val_mae: 0.2372\n", + "Epoch 85/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0865 - mae: 0.2148 - val_loss: 0.1012 - val_mae: 0.2342\n", + "Epoch 86/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0856 - mae: 0.2124 - val_loss: 0.1004 - val_mae: 0.2317\n", + "Epoch 87/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0850 - mae: 0.2122 - val_loss: 0.0998 - val_mae: 0.2340\n", + "Epoch 88/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0843 - mae: 0.2121 - val_loss: 0.0987 - val_mae: 0.2312\n", + "Epoch 89/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0836 - mae: 0.2103 - val_loss: 0.0981 - val_mae: 0.2313\n", + "Epoch 90/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0832 - mae: 0.2113 - val_loss: 0.0971 - val_mae: 0.2288\n", + "Epoch 91/500\n", + "600/600 [==============================] - 0s 58us/sample - loss: 0.0830 - mae: 0.2066 - val_loss: 0.0970 - val_mae: 0.2238\n", + "Epoch 92/500\n", + "600/600 [==============================] - 0s 70us/sample - loss: 0.0829 - mae: 0.2111 - val_loss: 0.0965 - val_mae: 0.2311\n", + "Epoch 93/500\n", + "600/600 [==============================] - 0s 69us/sample - loss: 0.0813 - mae: 0.2068 - val_loss: 0.0959 - val_mae: 0.2234\n", + "Epoch 94/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0816 - mae: 0.2070 - val_loss: 0.0950 - val_mae: 0.2288\n", + "Epoch 95/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0817 - mae: 0.2036 - val_loss: 0.0940 - val_mae: 0.2189\n", + "Epoch 96/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0803 - mae: 0.2064 - val_loss: 0.0929 - val_mae: 0.2243\n", + "Epoch 97/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0795 - mae: 0.2018 - val_loss: 0.0919 - val_mae: 0.2201\n", + "Epoch 98/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0773 - mae: 0.2024 - val_loss: 0.0930 - val_mae: 0.2276\n", + "Epoch 99/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0780 - mae: 0.2015 - val_loss: 0.0905 - val_mae: 0.2205\n", + "Epoch 100/500\n", + "600/600 [==============================] - 0s 59us/sample - loss: 0.0770 - mae: 0.2025 - val_loss: 0.0900 - val_mae: 0.2220\n", + "Epoch 101/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0768 - mae: 0.1993 - val_loss: 0.0892 - val_mae: 0.2146\n", + "Epoch 102/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0783 - mae: 0.2039 - val_loss: 0.0885 - val_mae: 0.2191\n", + "Epoch 103/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0748 - mae: 0.1963 - val_loss: 0.0876 - val_mae: 0.2149\n", + "Epoch 104/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0743 - mae: 0.1978 - val_loss: 0.0873 - val_mae: 0.2179\n", + "Epoch 105/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0733 - mae: 0.1952 - val_loss: 0.0865 - val_mae: 0.2114\n", + "Epoch 106/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0733 - mae: 0.1943 - val_loss: 0.0862 - val_mae: 0.2131\n", + "Epoch 107/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0723 - mae: 0.1936 - val_loss: 0.0848 - val_mae: 0.2112\n", + "Epoch 108/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0715 - mae: 0.1927 - val_loss: 0.0843 - val_mae: 0.2125\n", + "Epoch 109/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.0714 - mae: 0.1903 - val_loss: 0.0836 - val_mae: 0.2100\n", + "Epoch 110/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0719 - mae: 0.1952 - val_loss: 0.0830 - val_mae: 0.2111\n", + "Epoch 111/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0714 - mae: 0.1895 - val_loss: 0.0824 - val_mae: 0.2072\n", + "Epoch 112/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0699 - mae: 0.1929 - val_loss: 0.0823 - val_mae: 0.2110\n", + "Epoch 113/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0699 - mae: 0.1891 - val_loss: 0.0810 - val_mae: 0.2053\n", + "Epoch 114/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0691 - mae: 0.1898 - val_loss: 0.0805 - val_mae: 0.2074\n", + "Epoch 115/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.0678 - mae: 0.1859 - val_loss: 0.0798 - val_mae: 0.2025\n", + "Epoch 116/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0674 - mae: 0.1880 - val_loss: 0.0794 - val_mae: 0.2061\n", + "Epoch 117/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0672 - mae: 0.1844 - val_loss: 0.0785 - val_mae: 0.2008\n", + "Epoch 118/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0663 - mae: 0.1848 - val_loss: 0.0780 - val_mae: 0.2038\n", + "Epoch 119/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.0657 - mae: 0.1830 - val_loss: 0.0772 - val_mae: 0.2003\n", + "Epoch 120/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0649 - mae: 0.1813 - val_loss: 0.0767 - val_mae: 0.2002\n", + "Epoch 121/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0654 - mae: 0.1845 - val_loss: 0.0761 - val_mae: 0.1997\n", + "Epoch 122/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0642 - mae: 0.1815 - val_loss: 0.0755 - val_mae: 0.1991\n", + "Epoch 123/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0635 - mae: 0.1807 - val_loss: 0.0750 - val_mae: 0.1955\n", + "Epoch 124/500\n", + "600/600 [==============================] - 0s 58us/sample - loss: 0.0635 - mae: 0.1779 - val_loss: 0.0744 - val_mae: 0.1981\n", + "Epoch 125/500\n", + "600/600 [==============================] - 0s 60us/sample - loss: 0.0642 - mae: 0.1844 - val_loss: 0.0738 - val_mae: 0.1968\n", + "Epoch 126/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0659 - mae: 0.1780 - val_loss: 0.0739 - val_mae: 0.1973\n", + "Epoch 127/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0622 - mae: 0.1817 - val_loss: 0.0731 - val_mae: 0.1985\n", + "Epoch 128/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0619 - mae: 0.1772 - val_loss: 0.0722 - val_mae: 0.1936\n", + "Epoch 129/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0607 - mae: 0.1764 - val_loss: 0.0718 - val_mae: 0.1946\n", + "Epoch 130/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0602 - mae: 0.1747 - val_loss: 0.0710 - val_mae: 0.1925\n", + "Epoch 131/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0600 - mae: 0.1748 - val_loss: 0.0706 - val_mae: 0.1923\n", + "Epoch 132/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0592 - mae: 0.1743 - val_loss: 0.0699 - val_mae: 0.1913\n", + "Epoch 133/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0594 - mae: 0.1722 - val_loss: 0.0695 - val_mae: 0.1901\n", + "Epoch 134/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0589 - mae: 0.1753 - val_loss: 0.0690 - val_mae: 0.1903\n", + "Epoch 135/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0587 - mae: 0.1702 - val_loss: 0.0684 - val_mae: 0.1886\n", + "Epoch 136/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.0575 - mae: 0.1725 - val_loss: 0.0682 - val_mae: 0.1908\n", + "Epoch 137/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0570 - mae: 0.1704 - val_loss: 0.0676 - val_mae: 0.1871\n", + "Epoch 138/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0567 - mae: 0.1692 - val_loss: 0.0671 - val_mae: 0.1879\n", + "Epoch 139/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0562 - mae: 0.1692 - val_loss: 0.0663 - val_mae: 0.1848\n", + "Epoch 140/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0558 - mae: 0.1676 - val_loss: 0.0658 - val_mae: 0.1847\n", + "Epoch 141/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0553 - mae: 0.1663 - val_loss: 0.0653 - val_mae: 0.1840\n", + "Epoch 142/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0552 - mae: 0.1665 - val_loss: 0.0650 - val_mae: 0.1850\n", + "Epoch 143/500\n", + "600/600 [==============================] - 0s 58us/sample - loss: 0.0550 - mae: 0.1688 - val_loss: 0.0642 - val_mae: 0.1831\n", + "Epoch 144/500\n", + "600/600 [==============================] - 0s 59us/sample - loss: 0.0542 - mae: 0.1647 - val_loss: 0.0640 - val_mae: 0.1820\n", + "Epoch 145/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0536 - mae: 0.1644 - val_loss: 0.0633 - val_mae: 0.1812\n", + "Epoch 146/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0533 - mae: 0.1646 - val_loss: 0.0628 - val_mae: 0.1820\n", + "Epoch 147/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0527 - mae: 0.1630 - val_loss: 0.0623 - val_mae: 0.1803\n", + "Epoch 148/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0524 - mae: 0.1620 - val_loss: 0.0620 - val_mae: 0.1809\n", + "Epoch 149/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0519 - mae: 0.1624 - val_loss: 0.0613 - val_mae: 0.1798\n", + "Epoch 150/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0527 - mae: 0.1629 - val_loss: 0.0610 - val_mae: 0.1798\n", + "Epoch 151/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0515 - mae: 0.1605 - val_loss: 0.0609 - val_mae: 0.1752\n", + "Epoch 152/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0511 - mae: 0.1609 - val_loss: 0.0602 - val_mae: 0.1788\n", + "Epoch 153/500\n", + "600/600 [==============================] - 0s 58us/sample - loss: 0.0506 - mae: 0.1594 - val_loss: 0.0594 - val_mae: 0.1786\n", + "Epoch 154/500\n", + "600/600 [==============================] - 0s 64us/sample - loss: 0.0501 - mae: 0.1607 - val_loss: 0.0589 - val_mae: 0.1763\n", + "Epoch 155/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0497 - mae: 0.1576 - val_loss: 0.0587 - val_mae: 0.1762\n", + "Epoch 156/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0493 - mae: 0.1585 - val_loss: 0.0581 - val_mae: 0.1756\n", + "Epoch 157/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0489 - mae: 0.1575 - val_loss: 0.0581 - val_mae: 0.1780\n", + "Epoch 158/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0486 - mae: 0.1582 - val_loss: 0.0574 - val_mae: 0.1728\n", + "Epoch 159/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0488 - mae: 0.1552 - val_loss: 0.0576 - val_mae: 0.1777\n", + "Epoch 160/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0501 - mae: 0.1633 - val_loss: 0.0567 - val_mae: 0.1750\n", + "Epoch 161/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.0481 - mae: 0.1568 - val_loss: 0.0562 - val_mae: 0.1750\n", + "Epoch 162/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0476 - mae: 0.1569 - val_loss: 0.0553 - val_mae: 0.1706\n", + "Epoch 163/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0464 - mae: 0.1533 - val_loss: 0.0549 - val_mae: 0.1717\n", + "Epoch 164/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0470 - mae: 0.1559 - val_loss: 0.0550 - val_mae: 0.1696\n", + "Epoch 165/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0463 - mae: 0.1526 - val_loss: 0.0543 - val_mae: 0.1669\n", + "Epoch 166/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0467 - mae: 0.1530 - val_loss: 0.0536 - val_mae: 0.1685\n", + "Epoch 167/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0465 - mae: 0.1521 - val_loss: 0.0536 - val_mae: 0.1691\n", + "Epoch 168/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.0462 - mae: 0.1570 - val_loss: 0.0530 - val_mae: 0.1681\n", + "Epoch 169/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0448 - mae: 0.1514 - val_loss: 0.0523 - val_mae: 0.1679\n", + "Epoch 170/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0441 - mae: 0.1509 - val_loss: 0.0518 - val_mae: 0.1668\n", + "Epoch 171/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0438 - mae: 0.1488 - val_loss: 0.0516 - val_mae: 0.1668\n", + "Epoch 172/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.0437 - mae: 0.1509 - val_loss: 0.0510 - val_mae: 0.1649\n", + "Epoch 173/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0431 - mae: 0.1479 - val_loss: 0.0507 - val_mae: 0.1658\n", + "Epoch 174/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0432 - mae: 0.1493 - val_loss: 0.0503 - val_mae: 0.1634\n", + "Epoch 175/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0427 - mae: 0.1467 - val_loss: 0.0502 - val_mae: 0.1667\n", + "Epoch 176/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0425 - mae: 0.1475 - val_loss: 0.0494 - val_mae: 0.1618\n", + "Epoch 177/500\n", + "600/600 [==============================] - 0s 43us/sample - loss: 0.0426 - mae: 0.1497 - val_loss: 0.0491 - val_mae: 0.1618\n", + "Epoch 178/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0416 - mae: 0.1454 - val_loss: 0.0489 - val_mae: 0.1635\n", + "Epoch 179/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0414 - mae: 0.1467 - val_loss: 0.0483 - val_mae: 0.1599\n", + "Epoch 180/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0411 - mae: 0.1439 - val_loss: 0.0489 - val_mae: 0.1651\n", + "Epoch 181/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0418 - mae: 0.1485 - val_loss: 0.0477 - val_mae: 0.1597\n", + "Epoch 182/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0405 - mae: 0.1445 - val_loss: 0.0473 - val_mae: 0.1612\n", + "Epoch 183/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0399 - mae: 0.1435 - val_loss: 0.0466 - val_mae: 0.1579\n", + "Epoch 184/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0399 - mae: 0.1432 - val_loss: 0.0465 - val_mae: 0.1561\n", + "Epoch 185/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0397 - mae: 0.1437 - val_loss: 0.0459 - val_mae: 0.1573\n", + "Epoch 186/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0394 - mae: 0.1424 - val_loss: 0.0455 - val_mae: 0.1582\n", + "Epoch 187/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0385 - mae: 0.1411 - val_loss: 0.0453 - val_mae: 0.1544\n", + "Epoch 188/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0385 - mae: 0.1403 - val_loss: 0.0447 - val_mae: 0.1545\n", + "Epoch 189/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0381 - mae: 0.1392 - val_loss: 0.0444 - val_mae: 0.1549\n", + "Epoch 190/500\n", + "600/600 [==============================] - 0s 61us/sample - loss: 0.0378 - mae: 0.1402 - val_loss: 0.0441 - val_mae: 0.1529\n", + "Epoch 191/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0376 - mae: 0.1390 - val_loss: 0.0441 - val_mae: 0.1574\n", + "Epoch 192/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0378 - mae: 0.1397 - val_loss: 0.0431 - val_mae: 0.1533\n", + "Epoch 193/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0376 - mae: 0.1401 - val_loss: 0.0430 - val_mae: 0.1538\n", + "Epoch 194/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0372 - mae: 0.1376 - val_loss: 0.0433 - val_mae: 0.1548\n", + "Epoch 195/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0376 - mae: 0.1412 - val_loss: 0.0429 - val_mae: 0.1508\n", + "Epoch 196/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0365 - mae: 0.1383 - val_loss: 0.0419 - val_mae: 0.1529\n", + "Epoch 197/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0361 - mae: 0.1353 - val_loss: 0.0416 - val_mae: 0.1485\n", + "Epoch 198/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0354 - mae: 0.1353 - val_loss: 0.0411 - val_mae: 0.1506\n", + "Epoch 199/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0354 - mae: 0.1363 - val_loss: 0.0410 - val_mae: 0.1504\n", + "Epoch 200/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.0354 - mae: 0.1358 - val_loss: 0.0410 - val_mae: 0.1511\n", + "Epoch 201/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.0348 - mae: 0.1349 - val_loss: 0.0399 - val_mae: 0.1475\n", + "Epoch 202/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.0345 - mae: 0.1342 - val_loss: 0.0396 - val_mae: 0.1476\n", + "Epoch 203/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0342 - mae: 0.1345 - val_loss: 0.0395 - val_mae: 0.1455\n", + "Epoch 204/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0337 - mae: 0.1321 - val_loss: 0.0390 - val_mae: 0.1462\n", + "Epoch 205/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0336 - mae: 0.1328 - val_loss: 0.0389 - val_mae: 0.1445\n", + "Epoch 206/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0337 - mae: 0.1317 - val_loss: 0.0392 - val_mae: 0.1497\n", + "Epoch 207/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0335 - mae: 0.1326 - val_loss: 0.0384 - val_mae: 0.1436\n", + "Epoch 208/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0329 - mae: 0.1310 - val_loss: 0.0376 - val_mae: 0.1444\n", + "Epoch 209/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0328 - mae: 0.1298 - val_loss: 0.0375 - val_mae: 0.1454\n", + "Epoch 210/500\n", + "600/600 [==============================] - 0s 44us/sample - loss: 0.0328 - mae: 0.1328 - val_loss: 0.0370 - val_mae: 0.1432\n", + "Epoch 211/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0331 - mae: 0.1310 - val_loss: 0.0369 - val_mae: 0.1413\n", + "Epoch 212/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.0317 - mae: 0.1290 - val_loss: 0.0367 - val_mae: 0.1449\n", + "Epoch 213/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0318 - mae: 0.1291 - val_loss: 0.0360 - val_mae: 0.1425\n", + "Epoch 214/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0312 - mae: 0.1284 - val_loss: 0.0356 - val_mae: 0.1413\n", + "Epoch 215/500\n", + "600/600 [==============================] - 0s 65us/sample - loss: 0.0309 - mae: 0.1273 - val_loss: 0.0356 - val_mae: 0.1423\n", + "Epoch 216/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0310 - mae: 0.1280 - val_loss: 0.0350 - val_mae: 0.1396\n", + "Epoch 217/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0303 - mae: 0.1263 - val_loss: 0.0346 - val_mae: 0.1400\n", + "Epoch 218/500\n", + "600/600 [==============================] - 0s 59us/sample - loss: 0.0302 - mae: 0.1267 - val_loss: 0.0343 - val_mae: 0.1390\n", + "Epoch 219/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0299 - mae: 0.1258 - val_loss: 0.0340 - val_mae: 0.1377\n", + "Epoch 220/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.0299 - mae: 0.1262 - val_loss: 0.0338 - val_mae: 0.1374\n", + "Epoch 221/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0294 - mae: 0.1246 - val_loss: 0.0337 - val_mae: 0.1395\n", + "Epoch 222/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0299 - mae: 0.1275 - val_loss: 0.0340 - val_mae: 0.1394\n", + "Epoch 223/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0295 - mae: 0.1251 - val_loss: 0.0331 - val_mae: 0.1378\n", + "Epoch 224/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0290 - mae: 0.1228 - val_loss: 0.0325 - val_mae: 0.1361\n", + "Epoch 225/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0291 - mae: 0.1254 - val_loss: 0.0321 - val_mae: 0.1344\n", + "Epoch 226/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0286 - mae: 0.1237 - val_loss: 0.0318 - val_mae: 0.1340\n", + "Epoch 227/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0281 - mae: 0.1219 - val_loss: 0.0315 - val_mae: 0.1331\n", + "Epoch 228/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0280 - mae: 0.1221 - val_loss: 0.0313 - val_mae: 0.1345\n", + "Epoch 229/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0276 - mae: 0.1202 - val_loss: 0.0310 - val_mae: 0.1333\n", + "Epoch 230/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.0276 - mae: 0.1215 - val_loss: 0.0308 - val_mae: 0.1313\n", + "Epoch 231/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0274 - mae: 0.1214 - val_loss: 0.0319 - val_mae: 0.1382\n", + "Epoch 232/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0281 - mae: 0.1242 - val_loss: 0.0304 - val_mae: 0.1305\n", + "Epoch 233/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0268 - mae: 0.1195 - val_loss: 0.0299 - val_mae: 0.1320\n", + "Epoch 234/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0264 - mae: 0.1187 - val_loss: 0.0296 - val_mae: 0.1302\n", + "Epoch 235/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0267 - mae: 0.1206 - val_loss: 0.0299 - val_mae: 0.1285\n", + "Epoch 236/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0272 - mae: 0.1182 - val_loss: 0.0309 - val_mae: 0.1363\n", + "Epoch 237/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0273 - mae: 0.1209 - val_loss: 0.0286 - val_mae: 0.1297\n", + "Epoch 238/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0260 - mae: 0.1191 - val_loss: 0.0286 - val_mae: 0.1276\n", + "Epoch 239/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0259 - mae: 0.1173 - val_loss: 0.0283 - val_mae: 0.1279\n", + "Epoch 240/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0255 - mae: 0.1157 - val_loss: 0.0279 - val_mae: 0.1281\n", + "Epoch 241/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0253 - mae: 0.1162 - val_loss: 0.0280 - val_mae: 0.1294\n", + "Epoch 242/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0256 - mae: 0.1178 - val_loss: 0.0273 - val_mae: 0.1259\n", + "Epoch 243/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0245 - mae: 0.1144 - val_loss: 0.0276 - val_mae: 0.1287\n", + "Epoch 244/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0252 - mae: 0.1163 - val_loss: 0.0268 - val_mae: 0.1263\n", + "Epoch 245/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0261 - mae: 0.1201 - val_loss: 0.0295 - val_mae: 0.1333\n", + "Epoch 246/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0268 - mae: 0.1231 - val_loss: 0.0279 - val_mae: 0.1302\n", + "Epoch 247/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0240 - mae: 0.1149 - val_loss: 0.0263 - val_mae: 0.1242\n", + "Epoch 248/500\n", + "600/600 [==============================] - 0s 66us/sample - loss: 0.0242 - mae: 0.1146 - val_loss: 0.0259 - val_mae: 0.1249\n", + "Epoch 249/500\n", + "600/600 [==============================] - 0s 69us/sample - loss: 0.0233 - mae: 0.1129 - val_loss: 0.0277 - val_mae: 0.1258\n", + "Epoch 250/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0246 - mae: 0.1158 - val_loss: 0.0255 - val_mae: 0.1237\n", + "Epoch 251/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0231 - mae: 0.1114 - val_loss: 0.0249 - val_mae: 0.1216\n", + "Epoch 252/500\n", + "600/600 [==============================] - 0s 63us/sample - loss: 0.0230 - mae: 0.1122 - val_loss: 0.0246 - val_mae: 0.1216\n", + "Epoch 253/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0229 - mae: 0.1109 - val_loss: 0.0247 - val_mae: 0.1228\n", + "Epoch 254/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.0230 - mae: 0.1122 - val_loss: 0.0242 - val_mae: 0.1204\n", + "Epoch 255/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.0233 - mae: 0.1139 - val_loss: 0.0252 - val_mae: 0.1209\n", + "Epoch 256/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0225 - mae: 0.1102 - val_loss: 0.0239 - val_mae: 0.1197\n", + "Epoch 257/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0219 - mae: 0.1086 - val_loss: 0.0235 - val_mae: 0.1197\n", + "Epoch 258/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0217 - mae: 0.1091 - val_loss: 0.0234 - val_mae: 0.1188\n", + "Epoch 259/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0215 - mae: 0.1082 - val_loss: 0.0231 - val_mae: 0.1184\n", + "Epoch 260/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0214 - mae: 0.1080 - val_loss: 0.0228 - val_mae: 0.1183\n", + "Epoch 261/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0214 - mae: 0.1081 - val_loss: 0.0226 - val_mae: 0.1175\n", + "Epoch 262/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0211 - mae: 0.1077 - val_loss: 0.0224 - val_mae: 0.1177\n", + "Epoch 263/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0210 - mae: 0.1075 - val_loss: 0.0223 - val_mae: 0.1176\n", + "Epoch 264/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0209 - mae: 0.1079 - val_loss: 0.0223 - val_mae: 0.1164\n", + "Epoch 265/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0208 - mae: 0.1073 - val_loss: 0.0219 - val_mae: 0.1165\n", + "Epoch 266/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0209 - mae: 0.1084 - val_loss: 0.0221 - val_mae: 0.1149\n", + "Epoch 267/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0206 - mae: 0.1075 - val_loss: 0.0215 - val_mae: 0.1148\n", + "Epoch 268/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0203 - mae: 0.1062 - val_loss: 0.0212 - val_mae: 0.1142\n", + "Epoch 269/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0201 - mae: 0.1055 - val_loss: 0.0212 - val_mae: 0.1141\n", + "Epoch 270/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0200 - mae: 0.1063 - val_loss: 0.0213 - val_mae: 0.1137\n", + "Epoch 271/500\n", + "600/600 [==============================] - 0s 58us/sample - loss: 0.0201 - mae: 0.1066 - val_loss: 0.0211 - val_mae: 0.1127\n", + "Epoch 272/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0205 - mae: 0.1074 - val_loss: 0.0203 - val_mae: 0.1131\n", + "Epoch 273/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0197 - mae: 0.1052 - val_loss: 0.0202 - val_mae: 0.1123\n", + "Epoch 274/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0194 - mae: 0.1043 - val_loss: 0.0201 - val_mae: 0.1119\n", + "Epoch 275/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0192 - mae: 0.1038 - val_loss: 0.0199 - val_mae: 0.1118\n", + "Epoch 276/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0191 - mae: 0.1040 - val_loss: 0.0200 - val_mae: 0.1113\n", + "Epoch 277/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0191 - mae: 0.1043 - val_loss: 0.0199 - val_mae: 0.1117\n", + "Epoch 278/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0194 - mae: 0.1051 - val_loss: 0.0195 - val_mae: 0.1111\n", + "Epoch 279/500\n", + "600/600 [==============================] - 0s 65us/sample - loss: 0.0186 - mae: 0.1031 - val_loss: 0.0197 - val_mae: 0.1098\n", + "Epoch 280/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0187 - mae: 0.1031 - val_loss: 0.0192 - val_mae: 0.1103\n", + "Epoch 281/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0186 - mae: 0.1031 - val_loss: 0.0192 - val_mae: 0.1098\n", + "Epoch 282/500\n", + "600/600 [==============================] - 0s 63us/sample - loss: 0.0185 - mae: 0.1031 - val_loss: 0.0190 - val_mae: 0.1092\n", + "Epoch 283/500\n", + "600/600 [==============================] - 0s 60us/sample - loss: 0.0183 - mae: 0.1022 - val_loss: 0.0188 - val_mae: 0.1097\n", + "Epoch 284/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0181 - mae: 0.1020 - val_loss: 0.0186 - val_mae: 0.1086\n", + "Epoch 285/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0183 - mae: 0.1025 - val_loss: 0.0192 - val_mae: 0.1085\n", + "Epoch 286/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0190 - mae: 0.1057 - val_loss: 0.0190 - val_mae: 0.1106\n", + "Epoch 287/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0181 - mae: 0.1022 - val_loss: 0.0181 - val_mae: 0.1077\n", + "Epoch 288/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0177 - mae: 0.1012 - val_loss: 0.0181 - val_mae: 0.1072\n", + "Epoch 289/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0175 - mae: 0.1003 - val_loss: 0.0182 - val_mae: 0.1082\n", + "Epoch 290/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0180 - mae: 0.1028 - val_loss: 0.0179 - val_mae: 0.1064\n", + "Epoch 291/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0175 - mae: 0.1013 - val_loss: 0.0179 - val_mae: 0.1063\n", + "Epoch 292/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0175 - mae: 0.1014 - val_loss: 0.0177 - val_mae: 0.1067\n", + "Epoch 293/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0176 - mae: 0.1018 - val_loss: 0.0171 - val_mae: 0.1051\n", + "Epoch 294/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0175 - mae: 0.1010 - val_loss: 0.0175 - val_mae: 0.1050\n", + "Epoch 295/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0176 - mae: 0.1015 - val_loss: 0.0174 - val_mae: 0.1056\n", + "Epoch 296/500\n", + "600/600 [==============================] - 0s 58us/sample - loss: 0.0173 - mae: 0.1017 - val_loss: 0.0172 - val_mae: 0.1040\n", + "Epoch 297/500\n", + "600/600 [==============================] - 0s 63us/sample - loss: 0.0168 - mae: 0.0999 - val_loss: 0.0169 - val_mae: 0.1046\n", + "Epoch 298/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0169 - mae: 0.1001 - val_loss: 0.0166 - val_mae: 0.1035\n", + "Epoch 299/500\n", + "600/600 [==============================] - 0s 141us/sample - loss: 0.0168 - mae: 0.0994 - val_loss: 0.0168 - val_mae: 0.1035\n", + "Epoch 300/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0166 - mae: 0.0999 - val_loss: 0.0162 - val_mae: 0.1026\n", + "Epoch 301/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0164 - mae: 0.0985 - val_loss: 0.0164 - val_mae: 0.1026\n", + "Epoch 302/500\n", + "600/600 [==============================] - 0s 58us/sample - loss: 0.0162 - mae: 0.0988 - val_loss: 0.0165 - val_mae: 0.1026\n", + "Epoch 303/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0164 - mae: 0.0989 - val_loss: 0.0161 - val_mae: 0.1022\n", + "Epoch 304/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0163 - mae: 0.0988 - val_loss: 0.0161 - val_mae: 0.1026\n", + "Epoch 305/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0163 - mae: 0.0993 - val_loss: 0.0158 - val_mae: 0.1015\n", + "Epoch 306/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0162 - mae: 0.0989 - val_loss: 0.0161 - val_mae: 0.1020\n", + "Epoch 307/500\n", + "600/600 [==============================] - 0s 76us/sample - loss: 0.0166 - mae: 0.1004 - val_loss: 0.0158 - val_mae: 0.1011\n", + "Epoch 308/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0160 - mae: 0.0984 - val_loss: 0.0158 - val_mae: 0.1004\n", + "Epoch 309/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0160 - mae: 0.0983 - val_loss: 0.0160 - val_mae: 0.1012\n", + "Epoch 310/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0170 - mae: 0.1013 - val_loss: 0.0159 - val_mae: 0.1016\n", + "Epoch 311/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0160 - mae: 0.0983 - val_loss: 0.0192 - val_mae: 0.1091\n", + "Epoch 312/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0185 - mae: 0.1053 - val_loss: 0.0153 - val_mae: 0.1004\n", + "Epoch 313/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0161 - mae: 0.0997 - val_loss: 0.0162 - val_mae: 0.1010\n", + "Epoch 314/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0153 - mae: 0.0966 - val_loss: 0.0154 - val_mae: 0.1006\n", + "Epoch 315/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0162 - mae: 0.1002 - val_loss: 0.0152 - val_mae: 0.0999\n", + "Epoch 316/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0162 - mae: 0.0999 - val_loss: 0.0158 - val_mae: 0.0996\n", + "Epoch 317/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0158 - mae: 0.0985 - val_loss: 0.0170 - val_mae: 0.1026\n", + "Epoch 318/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0167 - mae: 0.1021 - val_loss: 0.0148 - val_mae: 0.0981\n", + "Epoch 319/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0161 - mae: 0.0994 - val_loss: 0.0157 - val_mae: 0.1011\n", + "Epoch 320/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0148 - mae: 0.0950 - val_loss: 0.0144 - val_mae: 0.0973\n", + "Epoch 321/500\n", + "600/600 [==============================] - 0s 62us/sample - loss: 0.0147 - mae: 0.0954 - val_loss: 0.0152 - val_mae: 0.0983\n", + "Epoch 322/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0149 - mae: 0.0955 - val_loss: 0.0147 - val_mae: 0.0982\n", + "Epoch 323/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0149 - mae: 0.0956 - val_loss: 0.0145 - val_mae: 0.0977\n", + "Epoch 324/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0147 - mae: 0.0956 - val_loss: 0.0142 - val_mae: 0.0963\n", + "Epoch 325/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0145 - mae: 0.0950 - val_loss: 0.0144 - val_mae: 0.0974\n", + "Epoch 326/500\n", + "600/600 [==============================] - 0s 59us/sample - loss: 0.0147 - mae: 0.0957 - val_loss: 0.0141 - val_mae: 0.0965\n", + "Epoch 327/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0147 - mae: 0.0960 - val_loss: 0.0144 - val_mae: 0.0973\n", + "Epoch 328/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0145 - mae: 0.0944 - val_loss: 0.0141 - val_mae: 0.0959\n", + "Epoch 329/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0145 - mae: 0.0952 - val_loss: 0.0137 - val_mae: 0.0949\n", + "Epoch 330/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0143 - mae: 0.0944 - val_loss: 0.0139 - val_mae: 0.0952\n", + "Epoch 331/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0143 - mae: 0.0941 - val_loss: 0.0139 - val_mae: 0.0947\n", + "Epoch 332/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0141 - mae: 0.0941 - val_loss: 0.0139 - val_mae: 0.0949\n", + "Epoch 333/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0149 - mae: 0.0951 - val_loss: 0.0148 - val_mae: 0.0968\n", + "Epoch 334/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0148 - mae: 0.0957 - val_loss: 0.0151 - val_mae: 0.0979\n", + "Epoch 335/500\n", + "600/600 [==============================] - 0s 58us/sample - loss: 0.0151 - mae: 0.0966 - val_loss: 0.0139 - val_mae: 0.0945\n", + "Epoch 336/500\n", + "600/600 [==============================] - 0s 59us/sample - loss: 0.0141 - mae: 0.0932 - val_loss: 0.0140 - val_mae: 0.0954\n", + "Epoch 337/500\n", + "600/600 [==============================] - 0s 60us/sample - loss: 0.0141 - mae: 0.0936 - val_loss: 0.0133 - val_mae: 0.0934\n", + "Epoch 338/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0141 - mae: 0.0932 - val_loss: 0.0137 - val_mae: 0.0943\n", + "Epoch 339/500\n", + "600/600 [==============================] - 0s 62us/sample - loss: 0.0139 - mae: 0.0931 - val_loss: 0.0132 - val_mae: 0.0929\n", + "Epoch 340/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0136 - mae: 0.0923 - val_loss: 0.0132 - val_mae: 0.0929\n", + "Epoch 341/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0137 - mae: 0.0925 - val_loss: 0.0146 - val_mae: 0.0963\n", + "Epoch 342/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0140 - mae: 0.0947 - val_loss: 0.0139 - val_mae: 0.0946\n", + "Epoch 343/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0139 - mae: 0.0940 - val_loss: 0.0136 - val_mae: 0.0934\n", + "Epoch 344/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0135 - mae: 0.0920 - val_loss: 0.0132 - val_mae: 0.0925\n", + "Epoch 345/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0136 - mae: 0.0923 - val_loss: 0.0134 - val_mae: 0.0932\n", + "Epoch 346/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0134 - mae: 0.0922 - val_loss: 0.0130 - val_mae: 0.0919\n", + "Epoch 347/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0133 - mae: 0.0920 - val_loss: 0.0137 - val_mae: 0.0937\n", + "Epoch 348/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0134 - mae: 0.0926 - val_loss: 0.0133 - val_mae: 0.0926\n", + "Epoch 349/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0139 - mae: 0.0941 - val_loss: 0.0135 - val_mae: 0.0929\n", + "Epoch 350/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0133 - mae: 0.0904 - val_loss: 0.0126 - val_mae: 0.0907\n", + "Epoch 351/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0134 - mae: 0.0916 - val_loss: 0.0128 - val_mae: 0.0912\n", + "Epoch 352/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0137 - mae: 0.0928 - val_loss: 0.0131 - val_mae: 0.0916\n", + "Epoch 353/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0144 - mae: 0.0947 - val_loss: 0.0126 - val_mae: 0.0904\n", + "Epoch 354/500\n", + "600/600 [==============================] - 0s 58us/sample - loss: 0.0131 - mae: 0.0910 - val_loss: 0.0132 - val_mae: 0.0923\n", + "Epoch 355/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0138 - mae: 0.0930 - val_loss: 0.0131 - val_mae: 0.0919\n", + "Epoch 356/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0135 - mae: 0.0926 - val_loss: 0.0126 - val_mae: 0.0904\n", + "Epoch 357/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0131 - mae: 0.0907 - val_loss: 0.0138 - val_mae: 0.0940\n", + "Epoch 358/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0132 - mae: 0.0907 - val_loss: 0.0126 - val_mae: 0.0904\n", + "Epoch 359/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0129 - mae: 0.0903 - val_loss: 0.0127 - val_mae: 0.0907\n", + "Epoch 360/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0128 - mae: 0.0900 - val_loss: 0.0126 - val_mae: 0.0902\n", + "Epoch 361/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0133 - mae: 0.0909 - val_loss: 0.0126 - val_mae: 0.0905\n", + "Epoch 362/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0130 - mae: 0.0907 - val_loss: 0.0125 - val_mae: 0.0898\n", + "Epoch 363/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0129 - mae: 0.0899 - val_loss: 0.0124 - val_mae: 0.0896\n", + "Epoch 364/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0129 - mae: 0.0903 - val_loss: 0.0126 - val_mae: 0.0900\n", + "Epoch 365/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0129 - mae: 0.0898 - val_loss: 0.0125 - val_mae: 0.0901\n", + "Epoch 366/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0129 - mae: 0.0910 - val_loss: 0.0131 - val_mae: 0.0912\n", + "Epoch 367/500\n", + "600/600 [==============================] - 0s 72us/sample - loss: 0.0127 - mae: 0.0895 - val_loss: 0.0122 - val_mae: 0.0890\n", + "Epoch 368/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0129 - mae: 0.0905 - val_loss: 0.0126 - val_mae: 0.0905\n", + "Epoch 369/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0129 - mae: 0.0902 - val_loss: 0.0123 - val_mae: 0.0889\n", + "Epoch 370/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0127 - mae: 0.0899 - val_loss: 0.0125 - val_mae: 0.0894\n", + "Epoch 371/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0134 - mae: 0.0920 - val_loss: 0.0139 - val_mae: 0.0931\n", + "Epoch 372/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0134 - mae: 0.0916 - val_loss: 0.0129 - val_mae: 0.0905\n", + "Epoch 373/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0129 - mae: 0.0907 - val_loss: 0.0126 - val_mae: 0.0897\n", + "Epoch 374/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0128 - mae: 0.0899 - val_loss: 0.0121 - val_mae: 0.0879\n", + "Epoch 375/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0133 - mae: 0.0923 - val_loss: 0.0125 - val_mae: 0.0904\n", + "Epoch 376/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0129 - mae: 0.0908 - val_loss: 0.0130 - val_mae: 0.0915\n", + "Epoch 377/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.0129 - mae: 0.0911 - val_loss: 0.0119 - val_mae: 0.0877\n", + "Epoch 378/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0138 - mae: 0.0941 - val_loss: 0.0121 - val_mae: 0.0881\n", + "Epoch 379/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0127 - mae: 0.0898 - val_loss: 0.0127 - val_mae: 0.0895\n", + "Epoch 380/500\n", + "600/600 [==============================] - 0s 46us/sample - loss: 0.0129 - mae: 0.0903 - val_loss: 0.0120 - val_mae: 0.0876\n", + "Epoch 381/500\n", + "600/600 [==============================] - 0s 45us/sample - loss: 0.0126 - mae: 0.0896 - val_loss: 0.0120 - val_mae: 0.0876\n", + "Epoch 382/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0130 - mae: 0.0917 - val_loss: 0.0121 - val_mae: 0.0880\n", + "Epoch 383/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0126 - mae: 0.0895 - val_loss: 0.0120 - val_mae: 0.0882\n", + "Epoch 384/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0128 - mae: 0.0910 - val_loss: 0.0150 - val_mae: 0.0983\n", + "Epoch 385/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0134 - mae: 0.0912 - val_loss: 0.0118 - val_mae: 0.0876\n", + "Epoch 386/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0124 - mae: 0.0892 - val_loss: 0.0123 - val_mae: 0.0886\n", + "Epoch 387/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0127 - mae: 0.0898 - val_loss: 0.0128 - val_mae: 0.0900\n", + "Epoch 388/500\n", + "600/600 [==============================] - 0s 58us/sample - loss: 0.0128 - mae: 0.0903 - val_loss: 0.0129 - val_mae: 0.0906\n", + "Epoch 389/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0148 - mae: 0.0984 - val_loss: 0.0121 - val_mae: 0.0880\n", + "Epoch 390/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0137 - mae: 0.0939 - val_loss: 0.0118 - val_mae: 0.0874\n", + "Epoch 391/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0127 - mae: 0.0896 - val_loss: 0.0122 - val_mae: 0.0893\n", + "Epoch 392/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0124 - mae: 0.0888 - val_loss: 0.0118 - val_mae: 0.0873\n", + "Epoch 393/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0124 - mae: 0.0887 - val_loss: 0.0119 - val_mae: 0.0879\n", + "Epoch 394/500\n", + "600/600 [==============================] - 0s 62us/sample - loss: 0.0124 - mae: 0.0885 - val_loss: 0.0117 - val_mae: 0.0865\n", + "Epoch 395/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0128 - mae: 0.0904 - val_loss: 0.0121 - val_mae: 0.0880\n", + "Epoch 396/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0126 - mae: 0.0895 - val_loss: 0.0119 - val_mae: 0.0874\n", + "Epoch 397/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0124 - mae: 0.0883 - val_loss: 0.0120 - val_mae: 0.0880\n", + "Epoch 398/500\n", + "600/600 [==============================] - 0s 58us/sample - loss: 0.0130 - mae: 0.0906 - val_loss: 0.0122 - val_mae: 0.0891\n", + "Epoch 399/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0136 - mae: 0.0935 - val_loss: 0.0128 - val_mae: 0.0917\n", + "Epoch 400/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0136 - mae: 0.0923 - val_loss: 0.0128 - val_mae: 0.0910\n", + "Epoch 401/500\n", + "600/600 [==============================] - 0s 59us/sample - loss: 0.0126 - mae: 0.0896 - val_loss: 0.0134 - val_mae: 0.0934\n", + "Epoch 402/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0135 - mae: 0.0925 - val_loss: 0.0127 - val_mae: 0.0910\n", + "Epoch 403/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0129 - mae: 0.0904 - val_loss: 0.0117 - val_mae: 0.0868\n", + "Epoch 404/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0126 - mae: 0.0898 - val_loss: 0.0140 - val_mae: 0.0928\n", + "Epoch 405/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0132 - mae: 0.0928 - val_loss: 0.0117 - val_mae: 0.0869\n", + "Epoch 406/500\n", + "600/600 [==============================] - 0s 47us/sample - loss: 0.0126 - mae: 0.0906 - val_loss: 0.0128 - val_mae: 0.0908\n", + "Epoch 407/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0122 - mae: 0.0880 - val_loss: 0.0117 - val_mae: 0.0870\n", + "Epoch 408/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0125 - mae: 0.0897 - val_loss: 0.0119 - val_mae: 0.0875\n", + "Epoch 409/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0124 - mae: 0.0889 - val_loss: 0.0118 - val_mae: 0.0869\n", + "Epoch 410/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0124 - mae: 0.0888 - val_loss: 0.0117 - val_mae: 0.0868\n", + "Epoch 411/500\n", + "600/600 [==============================] - 0s 59us/sample - loss: 0.0122 - mae: 0.0886 - val_loss: 0.0139 - val_mae: 0.0933\n", + "Epoch 412/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0132 - mae: 0.0923 - val_loss: 0.0125 - val_mae: 0.0891\n", + "Epoch 413/500\n", + "600/600 [==============================] - 0s 62us/sample - loss: 0.0140 - mae: 0.0938 - val_loss: 0.0119 - val_mae: 0.0875\n", + "Epoch 414/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0134 - mae: 0.0917 - val_loss: 0.0125 - val_mae: 0.0897\n", + "Epoch 415/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0131 - mae: 0.0917 - val_loss: 0.0126 - val_mae: 0.0904\n", + "Epoch 416/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0128 - mae: 0.0900 - val_loss: 0.0129 - val_mae: 0.0912\n", + "Epoch 417/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0124 - mae: 0.0890 - val_loss: 0.0118 - val_mae: 0.0874\n", + "Epoch 418/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0128 - mae: 0.0899 - val_loss: 0.0132 - val_mae: 0.0925\n", + "Epoch 419/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0131 - mae: 0.0917 - val_loss: 0.0120 - val_mae: 0.0882\n", + "Epoch 420/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0124 - mae: 0.0884 - val_loss: 0.0130 - val_mae: 0.0919\n", + "Epoch 421/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0131 - mae: 0.0914 - val_loss: 0.0130 - val_mae: 0.0916\n", + "Epoch 422/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0133 - mae: 0.0921 - val_loss: 0.0115 - val_mae: 0.0864\n", + "Epoch 423/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0123 - mae: 0.0886 - val_loss: 0.0120 - val_mae: 0.0876\n", + "Epoch 424/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0122 - mae: 0.0883 - val_loss: 0.0141 - val_mae: 0.0935\n", + "Epoch 425/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0136 - mae: 0.0936 - val_loss: 0.0117 - val_mae: 0.0869\n", + "Epoch 426/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0134 - mae: 0.0922 - val_loss: 0.0116 - val_mae: 0.0868\n", + "Epoch 427/500\n", + "600/600 [==============================] - 0s 66us/sample - loss: 0.0121 - mae: 0.0879 - val_loss: 0.0116 - val_mae: 0.0867\n", + "Epoch 428/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0121 - mae: 0.0882 - val_loss: 0.0121 - val_mae: 0.0881\n", + "Epoch 429/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0125 - mae: 0.0895 - val_loss: 0.0114 - val_mae: 0.0859\n", + "Epoch 430/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0123 - mae: 0.0883 - val_loss: 0.0129 - val_mae: 0.0901\n", + "Epoch 431/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0126 - mae: 0.0900 - val_loss: 0.0120 - val_mae: 0.0877\n", + "Epoch 432/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0123 - mae: 0.0882 - val_loss: 0.0118 - val_mae: 0.0870\n", + "Epoch 433/500\n", + "600/600 [==============================] - 0s 60us/sample - loss: 0.0120 - mae: 0.0879 - val_loss: 0.0120 - val_mae: 0.0878\n", + "Epoch 434/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0122 - mae: 0.0877 - val_loss: 0.0114 - val_mae: 0.0861\n", + "Epoch 435/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0120 - mae: 0.0877 - val_loss: 0.0120 - val_mae: 0.0876\n", + "Epoch 436/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0122 - mae: 0.0885 - val_loss: 0.0115 - val_mae: 0.0862\n", + "Epoch 437/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0120 - mae: 0.0882 - val_loss: 0.0117 - val_mae: 0.0867\n", + "Epoch 438/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0119 - mae: 0.0872 - val_loss: 0.0116 - val_mae: 0.0865\n", + "Epoch 439/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0122 - mae: 0.0885 - val_loss: 0.0116 - val_mae: 0.0864\n", + "Epoch 440/500\n", + "600/600 [==============================] - 0s 65us/sample - loss: 0.0122 - mae: 0.0888 - val_loss: 0.0123 - val_mae: 0.0889\n", + "Epoch 441/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0120 - mae: 0.0886 - val_loss: 0.0116 - val_mae: 0.0864\n", + "Epoch 442/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0124 - mae: 0.0880 - val_loss: 0.0120 - val_mae: 0.0880\n", + "Epoch 443/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0121 - mae: 0.0875 - val_loss: 0.0123 - val_mae: 0.0885\n", + "Epoch 444/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0124 - mae: 0.0895 - val_loss: 0.0118 - val_mae: 0.0875\n", + "Epoch 445/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0126 - mae: 0.0902 - val_loss: 0.0117 - val_mae: 0.0869\n", + "Epoch 446/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0121 - mae: 0.0873 - val_loss: 0.0132 - val_mae: 0.0925\n", + "Epoch 447/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0124 - mae: 0.0883 - val_loss: 0.0124 - val_mae: 0.0890\n", + "Epoch 448/500\n", + "600/600 [==============================] - 0s 69us/sample - loss: 0.0120 - mae: 0.0877 - val_loss: 0.0115 - val_mae: 0.0863\n", + "Epoch 449/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0122 - mae: 0.0885 - val_loss: 0.0115 - val_mae: 0.0865\n", + "Epoch 450/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0125 - mae: 0.0904 - val_loss: 0.0118 - val_mae: 0.0872\n", + "Epoch 451/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0119 - mae: 0.0869 - val_loss: 0.0126 - val_mae: 0.0895\n", + "Epoch 452/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0124 - mae: 0.0890 - val_loss: 0.0116 - val_mae: 0.0867\n", + "Epoch 453/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0119 - mae: 0.0872 - val_loss: 0.0117 - val_mae: 0.0868\n", + "Epoch 454/500\n", + "600/600 [==============================] - 0s 49us/sample - loss: 0.0120 - mae: 0.0878 - val_loss: 0.0116 - val_mae: 0.0863\n", + "Epoch 455/500\n", + "600/600 [==============================] - 0s 61us/sample - loss: 0.0120 - mae: 0.0878 - val_loss: 0.0117 - val_mae: 0.0870\n", + "Epoch 456/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0118 - mae: 0.0869 - val_loss: 0.0115 - val_mae: 0.0862\n", + "Epoch 457/500\n", + "600/600 [==============================] - 0s 66us/sample - loss: 0.0121 - mae: 0.0883 - val_loss: 0.0116 - val_mae: 0.0866\n", + "Epoch 458/500\n", + "600/600 [==============================] - 0s 60us/sample - loss: 0.0121 - mae: 0.0876 - val_loss: 0.0116 - val_mae: 0.0863\n", + "Epoch 459/500\n", + "600/600 [==============================] - 0s 60us/sample - loss: 0.0119 - mae: 0.0872 - val_loss: 0.0116 - val_mae: 0.0864\n", + "Epoch 460/500\n", + "600/600 [==============================] - 0s 48us/sample - loss: 0.0119 - mae: 0.0871 - val_loss: 0.0115 - val_mae: 0.0862\n", + "Epoch 461/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0120 - mae: 0.0880 - val_loss: 0.0120 - val_mae: 0.0881\n", + "Epoch 462/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0119 - mae: 0.0872 - val_loss: 0.0116 - val_mae: 0.0864\n", + "Epoch 463/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0119 - mae: 0.0873 - val_loss: 0.0117 - val_mae: 0.0866\n", + "Epoch 464/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0118 - mae: 0.0868 - val_loss: 0.0115 - val_mae: 0.0862\n", + "Epoch 465/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0120 - mae: 0.0875 - val_loss: 0.0124 - val_mae: 0.0896\n", + "Epoch 466/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0117 - mae: 0.0875 - val_loss: 0.0129 - val_mae: 0.0901\n", + "Epoch 467/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0126 - mae: 0.0907 - val_loss: 0.0127 - val_mae: 0.0898\n", + "Epoch 468/500\n", + "600/600 [==============================] - 0s 58us/sample - loss: 0.0125 - mae: 0.0893 - val_loss: 0.0118 - val_mae: 0.0874\n", + "Epoch 469/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0122 - mae: 0.0887 - val_loss: 0.0115 - val_mae: 0.0864\n", + "Epoch 470/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0119 - mae: 0.0874 - val_loss: 0.0119 - val_mae: 0.0876\n", + "Epoch 471/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0118 - mae: 0.0866 - val_loss: 0.0116 - val_mae: 0.0867\n", + "Epoch 472/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0120 - mae: 0.0873 - val_loss: 0.0118 - val_mae: 0.0872\n", + "Epoch 473/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0121 - mae: 0.0882 - val_loss: 0.0115 - val_mae: 0.0863\n", + "Epoch 474/500\n", + "600/600 [==============================] - 0s 55us/sample - loss: 0.0118 - mae: 0.0871 - val_loss: 0.0117 - val_mae: 0.0867\n", + "Epoch 475/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0120 - mae: 0.0877 - val_loss: 0.0121 - val_mae: 0.0884\n", + "Epoch 476/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0127 - mae: 0.0902 - val_loss: 0.0119 - val_mae: 0.0877\n", + "Epoch 477/500\n", + "600/600 [==============================] - 0s 61us/sample - loss: 0.0122 - mae: 0.0882 - val_loss: 0.0151 - val_mae: 0.0967\n", + "Epoch 478/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0136 - mae: 0.0933 - val_loss: 0.0123 - val_mae: 0.0889\n", + "Epoch 479/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0121 - mae: 0.0884 - val_loss: 0.0116 - val_mae: 0.0869\n", + "Epoch 480/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0121 - mae: 0.0883 - val_loss: 0.0118 - val_mae: 0.0877\n", + "Epoch 481/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0120 - mae: 0.0876 - val_loss: 0.0118 - val_mae: 0.0875\n", + "Epoch 482/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0121 - mae: 0.0887 - val_loss: 0.0116 - val_mae: 0.0865\n", + "Epoch 483/500\n", + "600/600 [==============================] - 0s 70us/sample - loss: 0.0122 - mae: 0.0892 - val_loss: 0.0114 - val_mae: 0.0863\n", + "Epoch 484/500\n", + "600/600 [==============================] - 0s 57us/sample - loss: 0.0132 - mae: 0.0926 - val_loss: 0.0115 - val_mae: 0.0866\n", + "Epoch 485/500\n", + "600/600 [==============================] - 0s 70us/sample - loss: 0.0138 - mae: 0.0948 - val_loss: 0.0118 - val_mae: 0.0874\n", + "Epoch 486/500\n", + "600/600 [==============================] - 0s 59us/sample - loss: 0.0119 - mae: 0.0879 - val_loss: 0.0114 - val_mae: 0.0860\n", + "Epoch 487/500\n", + "600/600 [==============================] - 0s 50us/sample - loss: 0.0118 - mae: 0.0872 - val_loss: 0.0116 - val_mae: 0.0870\n", + "Epoch 488/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0117 - mae: 0.0870 - val_loss: 0.0114 - val_mae: 0.0861\n", + "Epoch 489/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0118 - mae: 0.0869 - val_loss: 0.0120 - val_mae: 0.0879\n", + "Epoch 490/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0119 - mae: 0.0873 - val_loss: 0.0115 - val_mae: 0.0863\n", + "Epoch 491/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0118 - mae: 0.0871 - val_loss: 0.0117 - val_mae: 0.0873\n", + "Epoch 492/500\n", + "600/600 [==============================] - 0s 61us/sample - loss: 0.0122 - mae: 0.0886 - val_loss: 0.0127 - val_mae: 0.0899\n", + "Epoch 493/500\n", + "600/600 [==============================] - 0s 54us/sample - loss: 0.0122 - mae: 0.0881 - val_loss: 0.0113 - val_mae: 0.0857\n", + "Epoch 494/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0125 - mae: 0.0898 - val_loss: 0.0119 - val_mae: 0.0880\n", + "Epoch 495/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0123 - mae: 0.0897 - val_loss: 0.0116 - val_mae: 0.0866\n", + "Epoch 496/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0119 - mae: 0.0875 - val_loss: 0.0115 - val_mae: 0.0866\n", + "Epoch 497/500\n", + "600/600 [==============================] - 0s 56us/sample - loss: 0.0118 - mae: 0.0868 - val_loss: 0.0117 - val_mae: 0.0871\n", + "Epoch 498/500\n", + "600/600 [==============================] - 0s 52us/sample - loss: 0.0124 - mae: 0.0889 - val_loss: 0.0116 - val_mae: 0.0866\n", + "Epoch 499/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0119 - mae: 0.0871 - val_loss: 0.0115 - val_mae: 0.0863\n", + "Epoch 500/500\n", + "600/600 [==============================] - 0s 53us/sample - loss: 0.0118 - mae: 0.0873 - val_loss: 0.0115 - val_mae: 0.0864\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Mc_CQu2_IvOP", + "colab_type": "text" + }, + "source": [ + "### 3. Plot Metrics\n", + "Each training epoch, the model prints out its loss and mean absolute error for training and validation. You can read this in the output above (note that your exact numbers may differ): \n", + "\n", + "```\n", + "Epoch 500/500\n", + "600/600 [==============================] - 0s 51us/sample - loss: 0.0118 - mae: 0.0873 - val_loss: 0.0105 - val_mae: 0.0832\n", + "```\n", + "\n", + "You can see that we've already got a huge improvement - validation loss has dropped from 0.15 to 0.01, and validation MAE has dropped from 0.33 to 0.08.\n", + "\n", + "The following cell will print the same graphs we used to evaluate our original model, but showing our new training history:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "SYHGswAJJgrC", + "colab_type": "code", + "outputId": "bdc6e8f7-480d-4d3e-c20b-94776722360f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 851 + } + }, + "source": [ + "# Draw a graph of the loss, which is the distance between\n", + "# the predicted and actual values during training and validation.\n", + "loss = history_2.history['loss']\n", + "val_loss = history_2.history['val_loss']\n", + "\n", + "epochs = range(1, len(loss) + 1)\n", + "\n", + "plt.plot(epochs, loss, 'g.', label='Training loss')\n", + "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n", + "plt.title('Training and validation loss')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Loss')\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "# Exclude the first few epochs so the graph is easier to read\n", + "SKIP = 100\n", + "\n", + "plt.clf()\n", + "\n", + "plt.plot(epochs[SKIP:], loss[SKIP:], 'g.', label='Training loss')\n", + "plt.plot(epochs[SKIP:], val_loss[SKIP:], 'b.', label='Validation loss')\n", + "plt.title('Training and validation loss')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Loss')\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "plt.clf()\n", + "\n", + "# Draw a graph of mean absolute error, which is another way of\n", + "# measuring the amount of error in the prediction.\n", + "mae = history_2.history['mae']\n", + "val_mae = history_2.history['val_mae']\n", + "\n", + "plt.plot(epochs[SKIP:], mae[SKIP:], 'g.', label='Training MAE')\n", + "plt.plot(epochs[SKIP:], val_mae[SKIP:], 'b.', label='Validation MAE')\n", + "plt.title('Training and validation mean absolute error')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('MAE')\n", + "plt.legend()\n", + "plt.show()" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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Li+Prr79m2rRpvPHGGwCsWrWK+Ph4BgwYwPTp0ykoKCi/3oMPPsjgwYMZMGAAu3fvrvH+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\niN4kfZtEZn4mXx//um4L9uS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Mie2kqnNVtbeq9u7YsWND21gtt/SKDpHec2gPA+YPSHgkEZkSXBWeecaC5wzD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\n3nkbqTtHUXrBGOj9DMfKjrFgywIA8gr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RYAVAz541j5eXw4QJqkkoitK0pE1AiEgQmAoMBM4AhojIGQmn7QCGAX9NuPYc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\nsDs+gf7WO0O9C74WJ/ZTbao1ftU+wN4wXGSCYgs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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f86dWOyZKmN9", + "colab_type": "text" + }, + "source": [ + "Great results! From these graphs, we can see several exciting things:\n", + "\n", + "* Our network has reached its peak accuracy much more quickly (within 200 epochs instead of 500)\n", + "* The overall loss and MAE are much better than our previous network\n", + "* Metrics are better for validation than training, which means the network is not overfitting\n", + "\n", + "The reason the metrics for validation are better than those for training is that validation metrics are calculated at the end of each epoch, while training metrics are calculated throughout the epoch, so validation happens on a model that has been trained slightly longer.\n", + "\n", + "This all means our network seems to be performing well! To confirm, let's check its predictions against the test dataset we set aside earlier:\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lZfztKKyhLxX", + "colab_type": "code", + "outputId": "7ed4e1c5-4d19-4d10-cd65-0cae30486734", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 318 + } + }, + "source": [ + "# Calculate and print the loss on our test dataset\n", + "loss = model_2.evaluate(x_test, y_test)\n", + "\n", + "# Make predictions based on our test dataset\n", + "predictions = model_2.predict(x_test)\n", + "\n", + "# Graph the predictions against the actual values\n", + "plt.clf()\n", + "plt.title('Comparison of predictions and actual values')\n", + "plt.plot(x_test, y_test, 'b.', label='Actual')\n", + "plt.plot(x_test, predictions, 'r.', label='Predicted')\n", + "plt.legend()\n", + "plt.show()" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\r200/1 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- 0s 40us/sample - loss: 0.0082 - mae: 0.0827\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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zvpEbRgmRE8EXkaNE5O8i8qKIXJpm+ZkislFEnvVf5+TiuENNv3rDeh5LL2zh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\ndlOEo+aFqMTV5o97e3/+6sWMTXHLswn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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3h7IcvuOOS4J", + "colab_type": "text" + }, + "source": [ + "Much better! The evaluation metrics we printed show that the model has a low loss and MAE on the test data, and the predictions line up visually with our data fairly well.\n", + "\n", + "The model isn't perfect; its predictions don't form a smooth sine curve. For instance, the line is almost straight when `x` is between 4.2 and 5.2. If we wanted to go further, we could try further increasing the capacity of the model, perhaps using some techniques to defend from overfitting.\n", + "\n", + "However, an important part of machine learning is knowing when to quit, and this model is good enough for our use case - which is to make some LEDs blink in a pleasing pattern.\n", + "\n", + "## Generate a TensorFlow Lite Model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sHe-Wv47rhm8", + "colab_type": "text" + }, + "source": [ + "### 1. Generate Models with or without Quantization\n", + "We now have an acceptably accurate model. We'll use the [TensorFlow Lite Converter](https://www.tensorflow.org/lite/convert) to convert the model into a special, space-efficient format for use on memory-constrained devices.\n", + "\n", + "Since this model is going to be deployed on a microcontroller, we want it to be as tiny as possible! One technique for reducing the size of models is called [quantization](https://www.tensorflow.org/lite/performance/post_training_quantization) while converting the model. It reduces the precision of the model's weights, and possibly the activations (output of each layer) as well, which saves memory, often without much impact on accuracy. Quantized models also run faster, since the calculations required are simpler.\n", + "\n", + "*Note: Currently, TFLite Converter produces TFlite models with float interfaces (input and output ops are always float). This is a blocker for users who require TFlite models with pure int8 or uint8 inputs/outputs. Refer to https://github.com/tensorflow/tensorflow/issues/38285*\n", + "\n", + "In the following cell, we'll convert the model twice: once with quantization, once without." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "1muAoUm8lSXL", + "colab_type": "code", + "outputId": "5ff328ef-73c5-45cd-e339-da52696b00e3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "# Convert the model to the TensorFlow Lite format without quantization\n", + "converter = tf.lite.TFLiteConverter.from_keras_model(model_2)\n", + "model_no_quant_tflite = converter.convert()\n", + "\n", + "# # Save the model to disk\n", + "open(MODEL_NO_QUANT_TFLITE, \"wb\").write(model_no_quant_tflite)\n", + "\n", + "# Convert the model to the TensorFlow Lite format with quantization\n", + "def representative_dataset():\n", + " for i in range(500):\n", + " yield([x_train[i].reshape(1, 1)])\n", + "# Set the optimization flag.\n", + "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n", + "# Enforce full-int8 quantization (except inputs/outputs which are always float)\n", + "converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\n", + "# Provide a representative dataset to ensure we quantize correctly.\n", + "converter.representative_dataset = representative_dataset\n", + "model_tflite = converter.convert()\n", + "\n", + "# Save the model to disk\n", + "open(MODEL_TFLITE, \"wb\").write(model_tflite)" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "2512" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8X1yO3h5pYbt", + "colab_type": "text" + }, + "source": [ + "### 2. Compare Model Sizes" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "jAIe0dK3pXU8", + "colab_type": "code", + "outputId": "ce15b7eb-f857-4cb0-ba70-5a67ce04566b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + } + }, + "source": [ + "import os\n", + "model_no_quant_size = os.path.getsize(MODEL_NO_QUANT_TFLITE)\n", + "print(\"Model is %d bytes\" % model_no_quant_size)\n", + "model_size = os.path.getsize(MODEL_TFLITE)\n", + "print(\"Quantized model is %d bytes\" % model_size)\n", + "difference = model_no_quant_size - model_size\n", + "print(\"Difference is %d bytes\" % difference)" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Model is 2736 bytes\n", + "Quantized model is 2512 bytes\n", + "Difference is 224 bytes\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cR2OuokFpkEM", + "colab_type": "text" + }, + "source": [ + "Our quantized model is only 224 bytes smaller than the original version, which only a tiny reduction in size! At around 2.5 kilobytes, this model is already so small that the weights make up only a small fraction of the overall size, meaning quantization has little effect.\n", + "\n", + "More complex models have many more weights, meaning the space saving from quantization will be much higher, approaching 4x for most sophisticated models.\n", + "\n", + "Regardless, our quantized model will take less time to execute than the original version, which is important on a tiny microcontroller!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L_vE-ZDkHVxe", + "colab_type": "text" + }, + "source": [ + "### 3. Test the Models\n", + "\n", + "To prove these models are still accurate after conversion and quantization, we'll use both of them to make predictions and compare these against our test results:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-J7IKlXiYVPz", + "colab_type": "code", + "outputId": "87d2fd39-4ddc-4f73-e164-e0089a5cfb59", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 281 + } + }, + "source": [ + "# Instantiate an interpreter for each model\n", + "model_no_quant = tf.lite.Interpreter(MODEL_NO_QUANT_TFLITE)\n", + "model = tf.lite.Interpreter(MODEL_TFLITE)\n", + "\n", + "# Allocate memory for each model\n", + "model_no_quant.allocate_tensors()\n", + "model.allocate_tensors()\n", + "\n", + "# Get the input and output tensors so we can feed in values and get the results\n", + "model_no_quant_input = model_no_quant.tensor(model_no_quant.get_input_details()[0][\"index\"])\n", + "model_no_quant_output = model_no_quant.tensor(model_no_quant.get_output_details()[0][\"index\"])\n", + "model_input = model.tensor(model.get_input_details()[0][\"index\"])\n", + "model_output = model.tensor(model.get_output_details()[0][\"index\"])\n", + "\n", + "# Create arrays to store the results\n", + "model_no_quant_predictions = np.empty(x_test.size)\n", + "model_predictions = np.empty(x_test.size)\n", + "\n", + "# Run each model's interpreter for each value and store the results in arrays\n", + "for i in range(x_test.size):\n", + " model_no_quant_input().fill(x_test[i])\n", + " model_no_quant.invoke()\n", + " model_no_quant_predictions[i] = model_no_quant_output()[0]\n", + "\n", + " model_input().fill(x_test[i])\n", + " model.invoke()\n", + " model_predictions[i] = model_output()[0]\n", + "\n", + "# See how they line up with the data\n", + "plt.clf()\n", + "plt.title('Comparison of various models against actual values')\n", + "plt.plot(x_test, y_test, 'bo', label='Actual predictions')\n", + "plt.plot(x_test, predictions, 'ro', label='Original predictions')\n", + "plt.plot(x_test, model_no_quant_predictions, 'bx', label='Lite predictions')\n", + "plt.plot(x_test, model_predictions, 'gx', label='Lite quantized predictions')\n", + "plt.legend()\n", + "plt.show()" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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RZs2aJYMHDxYRkX379jks74EDB6ROnTq28M/WPDP38K3rycnJUqtWLfnzzz9F\nRGTAgAHywQcf2PK0XtPHH38szz//vIiIdO7cWXbs2CEiIteuXbtnX055Re/hFxFsPfDgcM2N0W57\ndHw0aXvfZdZKL26a3aDUdVI99mghD1LKYYh9HnP9/5J0cAAr2ndxmH+/fvDpp1C5csbtFy5oHc3s\nIs3mFM0xQ0Awq+vlqghO/y+CGgdbcK1agjaoV0CpNE71fgnfkG4sCVvPa5H+hKSXIJedQpprsn37\n9g7DCO/YscMWVtk6SYojvLy8CAgIADKGET5w4AAtW7bE19eXpUuX5il8cq9evXBycqJOnTo8/PDD\ntl6zfRl//PFHpk2bRkBAAG3atOHGjRskJCSwbds2+vfvD2gxexyVNyoqirCwMKpUqQLkHj75zz//\nxMvLi7p16wJ5C58cHBzMmDFjmD17NpcvX7aFny6q6AK/kLAJz9NBmuA0RuPmBv0mRdNrVS+Cfr/M\nGNM+vH7tDAaz5g3jZKb9rw1w3zATl43vUrriYa4e/Sxbw2+/fmA3wZEN69d4XqI52ht2J02CgQO1\nGDnUjMXpmwiIDyHZ/33OBH2HSnOh1253Rm58BHFOw6lUIvtbr6V/XFneM21ixMObmN11M6ecjaQr\nJ045G5ndtZgac+9CfOSGDRuya9euDNuuXr1KQkICjz76KJAxNPHtYA0hDBnDCA8aNIi5c+eyf/9+\npkyZUqDhk7/55htb+OSEhIRCi1fkKHzyq6++yueff05ycjLBwcF5mkf4fkYX+IWEtQfu+lcIj0e+\nSJWwtoxppvj0SFvCtr7IjLM/8YKxB/FNv0OllQIB5zQDm5oeIsw4Co+YnqSv2c66dY7VxFZB7Sjw\nGsDFi9r5PT21uDeentq6vcumIzX0V19pMXLc9own/bjmySNem+HwU8jSjaz4LpH5MUepEfsUinSe\nOA4/NDvJa5O+4uxf8NY6f46YvXBCOGL24q11/rjuLIYeFHchPvITTzxBUlKSbcJus9nMK6+8wqBB\ng3DLfK5MBAcHExERAcDvv//O/v3783Xua9euUb16dVJTU1maRztEZGQk6enpHDt2jOPHj1OvXr0s\naUJDQ5kzZw6aJgL27NkDQKtWrVi2bBmgfV04Clvctm1bIiMjuWCJPZJb+OR69ephMpk4evQokLfw\nyceOHcPX15cJEyYQFBSkC3yd26dfP3jimQHsoCWBcc14pzUExjXjk4MtOfrUdD7pvYlShiTKpd5k\n1MZHSEt1x2BI4YveGzhV/xRlyjg2vP7737cEdXZ4eGSdGatfv4w9+oEDHef/6aeZti/bAMvXg0l7\nAWCM5ox3DFV+jGLzV0LE8H1owNAAACAASURBVM28V2E65R5txERje3oRwRtoxtyJxvb8fbEYGnOt\nb/Sc3qj5RCnF6tWriYyMpE6dOtStWxdXV1f+85//5HrsCy+8wPnz52nYsCGvv/463t7eVKhQIc/n\nfvvtt2nWrBnBwcHUr18/T8d4eHjQtGlTOnbsyPz583F1dc2SZvLkyaSmpuLn54e3tzeTJ08GYNSo\nUSQmJtKgQQPeeOMNGjdunOVYb29vJk2aROvWrfH392fMmDGANhPYjBkzCAwM5NixY7b0rq6uLFy4\nkLCwMHx9fXFycmLkyJFZ8rXnww8/tKnAXFxc6NixY56u/b4lO+V+YS/F3WjbsaPIrMciJcqIuE4o\nJbxaQSqGjBZerSCuE0pJUGd/qdG5k/Tp7C6bjUg8nhJonCF0Hi5l+wyXPnOmZ2t4zW3Jzv0yO0Nv\nvpfg6VK6flSGc0Qdj5JhnZEq45ABxkEZjLmbjUXDmJtvt8z7iLS0NElOThYRkaNHj4rRaJSUlJS7\ndr6S5BpZmOTXaFu0LRBFlE7vhrPL+Dk/7BnBKHpQWn3DDScDl703gksipVPNTDuwjydMe1kBrLAe\naNKWRGDTpuwjaeaEp6emkpk0CQYMyDgxhyNXTUcYDGA253COU+MdnCOEN37wpP6BiowNW0/LuDYs\nbhHLqKhHqHcyzXZsdHw0sWditVAPOgVGUlISISEhpKamIiLMmzePUqVKFXaxdO412b0JCnspzj38\nWZNmCBPdNDfLvk9J2eaThckutgFVo4w95L1glWtP2mAQKVUqa++9cmXH6a1jf7IbcJWXLwY3N5FR\no7Lfr1T25xgXuEkqc04GhHgKU5E63ZqJGldZptd9zDZwzH1SBXH3iSqosUoFSlHu4esUT3S3zPuc\n8HAInLODWcvqQKob1P2exPbvglMq7Y+Ce/WdfMIoLv8dkcXmlxmzGcqVy6om/uij7O2FOQ24ys2B\nxJr/vHlZ3T2teHhkf47P4tvR99mv+L7JSV7fCn/X2UOp7f/HlK6/8UYboXurixiWLqTZATNSMGOV\ndHR07NAF/j3m2LVhdHtgIIGmSnj90k1ztzSkQ2I1nlzyMusikykV1p1VnpVtNr+cuHgxq+E1J3th\nTu7h776rpXeEdbpCq80xp5dKdue4XCmaFQHTWTVqM29HCWu2VcK15ZukHu3M263h+t/NmcKbbORJ\nLcwykPRgNKOXF0e/TR2de48u8O8x1hj27Zs/SXyLtdhmpCr7N680r8VcUyQ3I9dy1inW5kWTk9DP\nrlfuyAMnp/RWr52RI7MKfUfehJlfKpUrQ5kyms7eKZtWVbFhpsBsv57jje2p0OBb2DoZVSOON3vv\nY7bRh334E2icCWG9uPx7UPYVoKOjk2d0gX+PCfn1HM9u98Ac+iq4XMcl1YVRGx/R1DuhY1ndPAFM\nISRtumW0tEbNzEypUvl3687NPXzePFi8OG/ehNaXyuLFkJysGZBFHBt03dxgbt/xNmEPEN30Qd5q\n6YLzb71pH59K6orvSFLuvNI7nkbdffnz2XEERk6g4qWQrBnq6OjkG13g3wPsfdtPKg92GfzgWnVQ\n4PzLvwiLqc2bywLgcGd4OOsgpH79YOHCjHrzypXhyy/z79adF/fw7L4OsiM77x6DIeeXxor2XZDI\nlfznwG/sCZtGY+JIW7GeUpdqscn/b4IPVeOkaSBfXu7GMxU207dv/q61OFLWwdDp+fPn2wZjLVq0\niDNnztzrYmVAD2V8H5OdNbewl+LipZPZY6Utm6S88VupMM5FJocgFca5SHnjt9KWTVmCnBUVbicQ\nm4gW/jlq4iYxOxlkltFf1LjKUr3bE1oY6O7PihpXWWYZ/SWKNuLONanC+UL12smPl8706SJRURm3\nRUVp2+8Ed3f3HPfnJ4xwftBDGd+f5NdLp9AFe3ZLcRH4rs93FJrPyhBh0mmiq/g/7yJmlCwzVhOX\ncRW0yJOWNC4u95c7Ym7caWDI7uU1d8323aoJU5EHunUUgqeLah4ubuPcxNX4vSjM8ryxj1R86g4l\n5h2QH4EfFSVSpcotoZ95/XZxJPCtESsjIyPF3d1d6tatK/7+/pKUlCRxcXHSqlUradSokXTo0EHO\nnDmT5fiBAwfKiBEjpHHjxlKnTh1Zv369iIgsXLhQunTpIiEhIdKqVStJTEyUwYMHS1BQkAQEBMia\nNWtERCQpKUl69+4t9evXl27duknTpk2zRLYUEfnqq6/E19dX/Pz8pH///rJz506pVKmSGI1G8ff3\nl6NHj2Z4AWzevFkCAgLEx8dHBg8eLDdu3LDl+cYbb0hgYKD4+PjIoUOHRERky5Yt4u/vL/7+/hIQ\nECBXr169s8ouAugCv5DJPOcFzWcJU5TMaK4kHaRTX8tkIZleAgRPt60XJWEvcnuTqdgDIoHGGeI+\nEU3oT3YSl8GNhHFVtHoKnibevb3EaTKC8Q4l5h2QXz98q5CfPLlghL1IzgJfJGPv+ubNm9KiRQs5\nd+6ciIisWLHCFnLYHj2UcdFF98MvRBwFG+se4wEbZzAuVGg9GDbUBTbO1LZbMYXATs1I6+l5R+FW\nCoU7DRtTrVk0e8KmU3fZDHav2c9jhyuR6rEb/qkDLd+Dhis5WD8ew+FQul+8ZRHObYrIwiYkBEaN\ngrff1n5D7rHt+c8//+TAgQO0b9+egIAA3nnnHU6dOuUwrR7KuGSg10gB4sh4GUEfZsf8i1caPM52\nzx1w4nFmxZziJSbgQlqGtHcYTLFQsfr/3w4h/WNZ/XEEe0whtGUzk1b60r53GdLr/wAp5aHmHjjd\niGkrGzCw7FCMRhMnTmgvFxEtjxMnNJfQnTs1T6P7geho+OQTmDxZ+w0JubdCX0Tw9vbml19+yTVt\nfkIZO4p6ea/JLpTxU089xYYNGwgODmbjxo15DvRWUtB7+AWIowFHBszQ/EPw2AknWmq/zT/EGTNL\nlhRoMMUiy/LR4/ni9RA8PSFateMlPkJWfkfZ62XA9SrcKA8VE4g0VqFSYoItCqhV2FsRgfnz74+e\nfnQ09OoFERHw1lvab69e2va7iX1o4Hr16nH+/HmbwE9NTc124hI9lHHJQBf4BYijQU2zmiteCQU2\nzqDlwndg4wxeCYVZLRSTJmkvCfsAZiUVe1fQg/hRr3ddEt2TIbEKlL6KU0IQcWEzWWl8MMd8RLSw\nzoUt9GNjNSFv7dGHhGjrsbF3lm9SUhK1atWyLe+//36G/da5ZAMCAjCbzaxatYoJEybg7+9PQEAA\nP//8s8N89VDGJYTslPuFvRRFo60j4+VDzwYKzWfKLF4WAZnFy0Lzmdr22zRyFnfK9h4lTEFceodK\nFG1kVCds648ED81TiOa7UZ/FNXia7hpZdNGNtoWII+NltYO7meXkyRjDHADGGObQZ5cnfy3bneFY\nawCzkoYjw2s5vyjUrlG0XDkWL+KZu0HRP64spR48yLGdn+Up35Janzo6OaEbbQsYR8bLpUt7Yjyd\npqlvamU/E9Vdnt/6vsPq1WQ1dFujY3766R/gCZP2w8MJJk3l1RfSvoNLhs3MNw/FgwQS8OB5PieK\ndg7zL2n1ebssWrSosIugc4/QBf5dxpFQs/cusecO5rcukuQUqtlRSIcav2+ml9mfeLwwcoJ4vPgN\nf55gMz85EPolrT51dHJDF/gFRHg4HPvvEfrsGkfI1bUARJfvynspM0hKqZMhrUhWoV+UXTJvl5xC\nNTsiZOlQIvCic3BL6le6xtEDL7HG1IugyvFUqbqQlEdXwKVHYOf4ElmfOjq5oevwC4igK5tZGV2V\nblcXEU0bomlD96uLOJlS1Rbb3R4R3SUzp1DNDklIIIQt9Dh9kt3eR0jp0xeMW4gtfwKnvt1Qfivh\nTFCJrU8dndzQe/gFQPjOcI4df5uWncsRfWABT5m+I924lXSfl+nAj/hcSiZq56UMx1gnFCnJvPtu\nRnUX5PylE96pAs4HPfnWNIf2Kx9iU+95hPa/iSiBG2Zk+Xo8JaTEu7jq6GSH3sMvAI5tC2KpZzLR\nfmdJ6x1GcvMFpPTui9lvJdu9z9L+dMbwsLq6QSO/IRmcAyYxNuwUzxkHsdM0ibqH65HqLKQZwCVm\nBN1NF4r9tIhFITyyI/bu3cuGDRts6+vWrWPatGl3nK99KOa7ibXez5w5Q8+ePXNM++GHH5Jk14vJ\nLUT0vUQX+AVAn0QzhpWRmM2lSHEBQl8BlxTSzaWYstKfZtc8S7z6JjvyE3s/rfxYZpZ6lciwb2jQ\nLYjDfrGoNBe4WQZpNpefjAbasvm+cMkM3xlOdHzGYbXR8dGE7yz46RpHjhzJc889BxQdgf/000/z\n6quvFmKJsIVkyA81atRg1apVOabJLPA3bNhAxYoV832uu4Eu8HMgr8G5QpYOZYppGzd+HQ/OKdo8\ntc4plP51BG+athA74vN8TSii45jx42HMO2PpeBR2BfwB6Qbk0iM8tLcDN8QVc58wrjdfAH27cCK4\nU6GWNahGEL1W9bIJ/ej4aHqt6kVQjYKfrnHq1KnMnDmTVatWERcXR79+/QgICCA5OZldu3bRunVr\nGjduTGhoKGfPns1yfHx8PC1atMDX15fXX3/d1pvdsmULnTt3tqUbPXq0zYXzrbfeIigoCB8fH4YP\nH24Lt9CmTRsmTJhA06ZNqVu3Ltu3b+fmzZu88cYbrFy5koCAAFauXMmiRYsYPXo0AAEBAbalTJky\nbN26levXrzNkyBCaNm1KYGAga9dqjhDJycn06dOHBg0a0L17d5KTkx3WidFoZPz48fj6+tK0aVNb\niAbrSORmzZoxfvx4jh07xpNPPknjxo1p2bKlLRxD5jqxYjKZ8PHxAcBsNjN27Fjb6N45c+Ywe/Zs\nzpw5Q0hICCGWYdZGo5F//vkHgPfffx8fHx98fHz48MMPbXk2aNCAYcOG4e3tTYcOHWzXNXv2bBo2\nbIifnx99+vTJV7twSHYjsgp7KeyRtvkK+auUDDf2FqcJ7sLrpYUpSOnXEfcJBunc/es7nvRC5xZR\nx6PEfZKSOsPLChPdhImlhSlK6nZqKx59vYXXXYQpiGvIrGzzyBzCOq8jcvMdHvl4lFQJryKToyZL\nlfAqEnX8zuMj343wyF26dJGvvvpKRETmzp1rO0d0dLQ89dRTtnQvvviiLFy4UERuhTcWEenfv7+s\nW7fOdv4xY8aIiMj3338vTzzxhIhosfVffPFF2zGZ10VE1q1bJ48//rjcvHnztkIx2+Pp6SnvvPOO\niGhx+K3XMXDgQHnqqackLS1NRETatm0rhw8fFhGRmJgYCQkJybFO4uPjxdvbW0RE5s2bJz169LCF\nYbbWif0cAPbrcXFx4uPjI4mJiXLt2jVp2LCh7N69W+Lj48VgMMiePXtERCQsLMx27dWrV7fNA3Dp\n0qUs16mPtC0gcvIRz0x00wdZ2nsV6QahdCq4bXwT51QDYkjnp4DRBIXd5YhZJQRrL/ktwrn06XFq\n/11O05OlluJwUBQJj/4JhlTU+Tp0+KuLwzwchbC+Wzr/EK8QRjUZxdvb3mZUk1EZ5vO9F+Q1PPLO\nnTvpa5k/csCAAXnKOzo6mmbNmuHr60tUVFSGoGyOQhfnxpEjRxg3bhwRERG4uLjcUShmK9Zr6tu3\nb4aIoWFhYRgMBhITE/n5558JCwsjICCAESNG2L6A8lInmzdvZsSIEbYwzLmFfN6xYwfdu3fH3d2d\nsmXL8swzz7B9+3YAvLy8CAgIADLWm5+fH/369WPJkiUFEu65QAS+UupJpdSfSqmjSqksijml1CCl\n1Hml1F7LMrQgzns3yY+P+Id+XUg7+Cydf6vADytT+C4mGsPKSCr+1hHDoT7EnrnDiFklEEfqtNgz\nsUT0jCCt/Fhee2wrFw6+AM434GptTY1m0HSypXcP5OXT4xzmm58X+Z0SHR/NJ3GfMLnVZD6J+ySL\nTv9uI5bwyHv37mXv3r3s37+fH3/80WHazOGRAZydnUlPT7et37hxw/b7wgsvsGrVKvbv38+wYcNs\n+8Bx6OKcSExMpFevXnz22WdUr17dVvZvvvnGVvaEhAQaNGiQ94vPdE32/60hn9PT06lYsaLtHHv3\n7uXQoUMOj7nbWOsMMtbb999/z4svvsju3bsJCgq6LbuDPXcs8JVSBuBjoCPQEOirlGroIOlKEQmw\nLJ/f6XnvNvnxEd8W+RnB3z3HnO9K0dqk8CKeJqZynPnue66vXMD44PF3t7DFjOx64TVN4wnxCmH8\neEjr2pP1MdF4xz4OlY+CoC3AYBbC1SuEO7CP5new1+1i/RqJ6BnBWyFvEdEzIoNO/25xO+GRg4OD\nWbFiBQBL7T51PD09+f3330lJSeHy5cv89NNPwC3BX6VKFRITE3M1YmYuV2aGDBnC4MGDadmypW3b\nnYRitrJy5Urbb4sWLbLsL1++PF5eXkRGRgLaS2bfvn1A9nViT/v27VmwYIFNCOcW8rlly5asWbOG\npKQkrl+/zurVqzNcc2bS09M5efIkISEhTJ8+nStXrpCYmJht+rxQED38psBRETkuIjeBFUDXAsi3\nUHn3Xc190h6nluF0CZ6Zoev5/uszuewdThTt8MKEgXS8MNniu+jD+/NPXnrh48fDZK+2HPS3BKFT\nwN++cNONT0KP0bl5CM5rV2UR+vke7HWbWL9GrGqcEK8QInpG3PHX3t0Ij/zRRx/x8ccf4+vry+nT\np23ba9euTa9evfDx8aFXr14EBgYCULFiRYYNG4aPjw+hoaEEBeVuiA4JCeH333+3GW2tnDhxglWr\nVvHll1/aDLdxcXF3FIrZyqVLl/Dz8+Ojjz7igw8+cJhm6dKlfPHFF/j7++Pt7W0zDmdXJ/YMHToU\nDw8P/Pz88Pf3t72Ihg8fzpNPPmkz2lpp1KgRgwYNomnTpjRr1oyhQ4fa6tQRZrOZ/v374+vrS2Bg\nIC+99NKde/tkp9zP6wL0BD63Wx8AzM2UZhBwFvgNWAXUziav4UAcEOfh4ZHFGHGvyWzcG/3sDFHj\nKssso78W6tjoL2pcZXnce4bDEL1K6SGPbwelHIc8VipjOoKni+u/q2hzBL9STfvtNELo+5Q8ONRT\nSnceIMNfH5rhmDuZf7e4hkfOjCPDcFEjs+G0uHK/Gm3XA0YR8QM2AV85SiQin4pIExFpUrVq1XtU\ntOzJ7CP+3sa5zIysxdiwU7QKac3YsFPMjKzFmhNzs3wNKAUjR+oumLdDXnvhlco7c6PiBVxih+Du\n8g9OaS4QtACu1OJclUs4ey+jz6b1GY650/l3dXSKMgUh8E8Dte3Wa1m22RCRCyKSYln9HMj+O+w+\nxu1CAmNM+3g8zoftrbfyeJwPY0z7qJSYkEWILF58/8ytWtRwpE7LPDp56VJIrLYZNs5k5YYLvLXC\nBzGXgXQDNPkMJzGzfqWZkF/PZck/P4O9SiJ3qie+HzCZTLYJ03VuURACPxaoo5TyUkqVAvoA6+wT\nKKWq260+DRyiCBAeDtGTNtt09uk48YKxB9ubHKDl1tbsaHKA943+JOChC5ECJLdeuNWom7poA8SM\nYS6jec+0iXb/awAGMzilk/7r/7HH9HKBK+fFUVxrHZ1C4Hba4h0LfBFJA0YDG9EEeYSIHFRKvaWU\netqS7CWl1EGl1D7gJTSd/n1N1Zc78fEfQXRbdo3oE14gQp1O7fhkwFqaHarOtuitNvXOAO/RhV3c\nYkdOL9DMRt0o2lHf+DGbmx2i9E0nnMxOOLeYzkRje+3eWYzrnd7NaMHN60hqK66urly4cEEX+jqF\njohw4cIFh3MP54S6XxtvkyZN5F4ERcqOrn0Hsq7eYgw3nXFfvpKHHvovh0M/RaW68OMyM21NQgIe\nhD08mtQn09n7se56ea9wcso0gYwxGvp0xyBpLF5Zlm0PlWV+6HFcUg0MXtaDevzB2LBTvHj6Vdbv\nHEtCAjzwAFy7Bjdv3srGzS1nfX5qaiqnTp3K4HOuo1NYuLq6UqtWLVxcXDJsV0rtEpEmjo7RBX52\nODvTNegx1oXugHQncDLjdNOVzctv0NqkcFbp2tR7eijee47RmGmayOBwqHQMDvQBUwjxGAlv7swn\nT5zCeLIGJ6pdZWZkLXqeuIynmHLMWw9brVPUyUng6/Hws8NsZm3MdtwbNCLJU/P1Lh3zAph24+QZ\nT7qpcItXkskSR39nxq8rDxKYFyMcKNOa7a230nJra8aYtpJO7iMn9XlwdYozeiwdstHlGgwEN+9O\nksceMBtA4GbzOXQzvkR0v/t+oHCxxt6oC5ph155TyoP3jf7scGBczw19oJxOcabEC/zshvG37diP\nn0PXYLjpTNRiM09vbIm5VBrXn+3LirKGwi52icdq1BXRXGDtPXpm9B1tGyNhb1x/xpizcV2fmEan\nuFPiBX52w/i3VjpP/fOBbFppJsQEa2N/5uk/B1A2xZ9HWunB0O4nMnv0HGuYzsxSrzJGLoNSjJHL\ntEhqxl6/IxmOMzwajVv7cH0Alk6JocQbbbN4fFhQShMgOsWDEe9Fs/R6d75c7krP4+dY9fCDDOl7\ng37uq1nw2r0NW6yjczfJyWhb4nv4pduGE2icSTxGzDgRj5FA40xKty34qeh0Co8+iWYMSxcyvMdF\npoYIw3tcxLB0IeenmYvt/Lc6Opkp8QJ/aDUn9oZN41tjRZwQvjVWZG/YNIZWK/FVU6wIWTqUNabZ\n3Iz7F2+3hptx/2KNaTbvXx1arCc919Gxp8RLtTk7HQdEm7Nzbr5HYurcP2S+d3IiAYxbSGvyBWyd\njGoyH4xb8OQEzZM2M2CAfn91ij8lXodvVeK3Crnls70teiuCoqxbegaDbm4jMXXuD6yeV/b3brnX\nQ4zseZGrF/1RB8OQ5rNxdj/DtKV+THwoDKeHf+RmojcPNovizMQ/Cq/wOjp3iK7DzwkPxz7bpw0e\n92wqPJ2Cwdqr798/q+fV6zW6IJEr6XKwDOmhr8KVmqQ6C688fZGU0MnULr0fc+AnXPutbaGUXUfn\nXlDiBf77/R37bD9d27HPtj4S8/7EfjyFI47t/IwgUznWxGxn1MaHEY9YuPYQVDpJ2RtOHPa4BLGj\nuB6hx7TWKb6UeIG/uUxWn+2ZpV7l0COOfTL1kZj3J47GU2TmqGc7FBAWUxsSgqH8X2AuRWIZM7Wv\nQOcNHTAY8h9FU0enqFDsdfhLl2rCICGBfAU7c6QH1nX49y/ZjaewYr13NV7oRseGQaSEToZr1aDc\nX3CjArhegdiR9N4Synq66fddp8hSInX4S5dClSqaPjdz2IS89Nj0qfCKFjl9ednfu4lPNyAldDLe\nCeU0YZ/QAlyvar9B86nUprtuu9EpthRLgW/tnV+4kGlHcDj1HpxJ64FG2/e6o4kxrOizWBUdspsW\nccmSjPfuUt19jDIPJ83tKqNiofbC5bBxJs4p7oyKhWgvx/nrthud4kCxFPjZ6XMDT2uDrCJqVwQR\n3lcVGXtzGu2Si2U1lCjy+kX2x+QNzHt7Pn98DGyYy0k8qB3Tg7RlP8KGuRz62HH+uu1Gp1ggIvfl\n0rhxY7ldlBLRlDgZl3g8ZZbRX9S4ytIypLWocZVlltFfxNMz27yWLNF2K6X9Llly28XSuY8YVWO1\nQLqMYq4IyCjmCqRLZ1ZnaT9ubvp91yk6AHGSjVwtll3b7HpjHiQwxrSPx+N82N56K4/H+TDGtC/b\n7/XsQifrXhtFn6hy3RhVYy3zGE2n4FY8YvyMYXzKEepzXLR4SgSH67YbnWJFsRT4jvS5AKedHA+y\nyu4NkV3oZN2AV/T54w+Yd7obiHDtchfGhZ2ivvETeLEB/XonsjdsGoGntcfjvX0v4PJ/9XU3TZ0i\nT7Gc4tDaG8vsjhl5aDRjb05jZmQtxpi28n68P2PDTkGpVxnjIJ/sDHW6Aa94sfjgXL69XotXwk7x\nwGUjf9Y38dgfldlqehXPTls56PYdxI4Cu6880Hv9OkWPYtnDB8ceNtkNstpcJn+DrHQDXvHAOsDK\nqup76O9qXKxp4oEzRn6uf4GaLztzJug7yp+sDxtujcDVv/J0iirFfuDVnaAPviq+2N/beIx8a6zI\n2D7xiFMqpJeidFo6KWWvodKh49f/xwbT+xmO1yfI0blfKZEDrwCqvtyJrn0HgrOz9oQ6O9O170Cq\nvtwpT8frg6+KL/b2mWeMlnhKK7x4etkwcE4ixf0aCIiCDQ2T6cwaLbExGoLD9a88nSJJsRX44TvD\nqXvNzLp6i+ka9BgAwaENWVfvax77u2qe89EHXxVP7O0we2qmExD5Ki+ZDkDDVWBIBQWcDYR0Zwia\nT61O3TVhH9YLzM60GKvPiKZT9Ci2Aj+oRhCHK//IY7E+rAvdgfvIh/k5aD+PxfqyOkJ3syjpZOih\n7xzPHtNYQvkv6+uCS5qi/NaXoMJJ2PU8AEt90YT99tdQrd9j+FNBhVJuHZ07odgK/NjIEF6N9Oew\n935KXalK0kPHKf13HbpueBKVbi7s4ukUMo5cd6OMBsq7nKfj0n9xNfpD3CO/AO9vKH8siGtlIOif\na7i0fIs25yMI8dInPtcpehRbgX/sv0d427QF17P1uFnxHM6XHyKl2lE+bp6MGUNhF0+nkHFkn3Gr\nE0uDyMmsM33IKOax3vQBhu1juerxO9VPPEKsZwrqaHucvjTr/vg6RZJiKfA7vRvOvnJhXO3dn1OP\n/Emlo4GklU6Cc/VICJ1LYPN++iAanSz2mU8Hjud/JybTmbV8zGjEuAWXlu/gFPUGZ6ufxfloK1L9\nVlG7+cucOAHPvxNN37m6Ll+n6FAsBX67ZCf+53MEqf89/NGZSztmgcEMD/5Bwz+8OPDweT1Ugk4W\n+vWDxYthv2c3DEqIrQmDt1cjvWU4RL1NqepxdImtwaK28TzafCQpXXoRvUTX5esUHYqdH/7SpdB6\noJFRHW/ynd8VQAAFZmdGba2Kp+EYr+7MeM2enloPT0cnA0YjI2oZWHr6Q9JNbUk3buVm2HO0O+LM\njobnSF72E+pEiO6PHFWRkwAAIABJREFUr3NfUWL88K2DaWqYExjzXT0Mv/wbSiVDqSRK/TqCZTFx\n1N3ZNctxeqgEHUdE9/ucb3fGsN70PmOZRYqpE4a4oWwK+JtXfhEwhej++DpFimIl8K2DaRLwYKKx\nHeZm8zDcLAU3y+DU9CNSjdsZx4wsx+kPrY4jYiu0I6L8MABmMYb2xtcwN/mcRls7Mb8JNHl4JtWr\n6/Pf6hQdCkTgK6WeVEr9qZQ6qpR61cH+0kqplZb9/1NKGQvivJk5EdwJn+YDWWV8gJg+4bhKMs32\n1sP9nBcpypWbvXtzzHgqwzFubpqLno5OZsaPB0aPphcRDDQOYnPYZ4yMbENC9CLCInuwq8c0Uv6a\nqYfP1iky3LHAV0oZgI+BjkBDoK9SqmGmZM8Dl0TkUeADYPqdntcRfvFVORC6mBktDHQ+4E6j/fX4\nOegArQ5UI2DFZNIODoCasXqoBJ08E1uhHRET92Gs9S0zI2sRafqEjvzA16ZFzIysRYeaGXsLemA1\nnfuZOzbaKqVaAFNFJNSy/hqAiLxnl2ajJc0vSiln4C+gquRw8tsx2qYpZ3o0f4x1oTson+DLVY/9\nPL3xcb6J+RkX0gAwGCAtLb9XqVPicXICEd7gTd4OdqVRpQhmHthFa5PCQLoWdsFnBVx6BPXzeN2Q\nq1No3G2jbU3gpN36Kcs2h2lEJA24AlQugHNnwICZtTHbNWHv+RvlE3xZG7MdA7dG1lpjmevo5AsP\nD6JpwyeMYsDpQ+z2PkKXPgZWGh/UhH3vbuCzEk4H6TYhnXxjDdVtifGIUnfHJnRfGW2VUsOVUnFK\nqbjz58/n+3gzBro2b8lVj/2UP+HHVY/9dG3eEjMGDAYYNQrmzcs9Hx2dzET3+5xeRBBBL3xMVRm1\nsj3XxZ3Bz57H9dm2OCszrFiN27kQ3Sakky/sp1IFMFv6p3fDJlQQAv80UNtuvZZlm8M0FpVOBeBC\n5oxE5FMRaSIiTapWzXtESysd/r+9O4+LstofOP45MwwquC+5w6hppuaSkChuGEaSG3ZBE83qmmV1\nq6tImpk3jTJSb7fbT8tMrwumomFqdkli3KNA09T0asqAS+67qAwz5/fHA8iuCDjMcN6v17wYZp55\nnjOgX86c8z3f0z+UtQHbGBjbncsLf2NgbHfWBmzjif6hZGSoYK/cu6yxfD/PZLxIZJH5P3T+pRu3\nXG3cdAXDzy8RnHqegQPVnJBSPAVtpZqltOeESiPgJwIthRDNhBCuwDBgbZ5j1gKjMu//BYgvavz+\nXu1tcRa/nSNZnbADCaxO2IHfzpHsbVH8TwuKklN4OPhF+IPZTB/PZNob/8XOx3Zkp/3KLp+xwaMS\n1U8ftndTFQdzp3VApblOqFRW2gohAoFPAD2wQEoZIYSYBiRJKdcKISoDS4BOwAVgmJTyaFHnLA87\nXilKQUzNBQNC9Fx3McDRx6nFeS622Ak2AwM3d+FUQEs6dtSObVG7BeG+4fZtsFKuGY23h3MKUtxK\nAEVN2pbKJuZSyg3AhjyPvZvj/k0guDSupSj2ltimJk3PWzl8MgTrI9Fc1FvBpkPo01jbZzOulp/Y\nt1eHQWcgZmiMvZurlHMREfm3Us1S2uuEytWkraI4Au8O0ZyMW0TltssQm98Bqx70NqQO0NmQtlvc\nvKonIyqGkzv8sjMw1GpcJUvOfxOTJ8OoUVpPHrTUcSibdUKl0sNXlIoksYY/E80TiYhegQx+Hs60\nB89t2c9bDBLXHS/TZZ+VF17QVuFaLNpzWZkXoCZ3K6qsrJysHn1KCixadH8WgaoevqIUU3g4LOCv\n6M096Hy4nhbsbXqtMKsELK4Yusyml3Eq6em3g30WtRq34oqK0nrzeYdv0tJgxNxIHm02k+MuRqTQ\nPg7OfmcmgRGlt+eCCviKcg/O1WyJh88b7OxwEBerAGHVNjy3VMJFCqxC8o9h+3jQ52Xwzf8fVlVo\nrXiyevbWQnZY7XRCx+7gGaxsWhOBZLaoSVj6DPxvlF6YVgFfUe7BK5Em9vVZTedD9Xny1wboDz0B\n1kqACxnJfbDuHUmXFBeO9ZkPJ/JvkqJW41Y8ReXb9yGOX8wTmRndhLDg47T3G8T44OPMjG7CuKWf\nlVobVMBXlHtQo00ibxvX4fHdUrau/x33r1cwdtmT6H4bCk0SsV5vyN4mN6i0LBr/lNxdOlWhtWIq\n7FNdCw6zEy+20oNx5j20S+rO3l7f0iApiHHmPaX6cVBN2irKPQj3DQdfiLwI9f57mGGbXgazjSjz\nfK5fr4+114fc3DyRjeZP8a6TTLuqZlJTtZ59RISasK2IPDwKzrfXD3+Sm0dfICghhseMH7HXaz78\n8TinfBcxO7kD4+SlUmuD6uErSgmEh8MX8S1h0iRCWMlUY290XnNg8xSsvrP4JPAQVc+nsDA0jhkz\n4PWvZxNFoL2brdhBYAG/9ha+L5JyqSe3At7hSmA4G4PnQ2pXaPEjA3fVIyz4OLNHvFZqbVABX1Hu\nUc5c6iFz/JnQajDTgn/HNXoJVUxvod/1LGu9TzIosBHBH3RgyfcvE/ZDGP7N/VVufgUTFaWlXuY1\n/cQ6KreNwpD4AtL7S7hVDVqvY2BiI77dcJKZrhOJq1J6tbadbhNzRbkf8uZSA7Tq/iKnjgcyrHEY\nD52oxnvmTVwPDMPq/RXNTruTXP86BtMsRrcdx6JFuV/r5qY243FmhZVPsKLjE2N7xgefAEsVqHkM\ncaoN1T/fTkz15/C7vKbY16owm5gryv1SUMbFoW1f8viFagw7cZQPg/dQ1bgO64b5uJ9uRnKD6zxy\nGvpv8WDu3ILzsFVuvvPKOe/agsN8w2BsCASSaPMc+LOjFuwvNUbW/52bPv/H8s75998uKRXwFeUe\nFJY4seaqP37Sk0nRHTgZ/HcY0Zfr9ZNpdsqdvfUhxieFIFYV65yK48tKw+1DHGepx/P8h0305p+8\nSULgEmgRR+c/alHdcIZKic9xK2AKJx9fV+rtUAFfUe5BYXn0Hh7aZikfmjfS+U8bPBgHR/w59vkF\niJ0JAWE84DOpWOdUHF/WhO1rfMYw385YjFvpz3omGAPh0a/odrAOIckXidlSm8reMfjoX8ZqjCv1\ndqiAryj3ICJCG3fPKyUF+nzgT4tWi/jNeJHGf7SGhrvJMG7HmBDE2NgWzG9upJXIXTdf5eY7t4UL\noT9rGOHbHr1VQPAw0oy/YGv8K6129WBPi/N4nwS/hFPEjIohqI+RDaEb7nziYlKTtopyj6KitHH3\nfJNxRhMEh7Ax+gJ/7daa1Es9oe0qOBCEYd8Q9A128kjLTzmz5TQpOhM12yTy2TPhasLWSQVGRPKT\neT6XrreiW7IrP/XYgtz/F3h0oZaV43aesbEtmPNnRvEK3xdCTdoqShkIDdX+f2aVtc3WOBGiVzLH\nvILUoy+A9+fo9w8CBJbhf+FmwDsMO3yGhSYTdceG8M2/tdILKk3TOfnf0HGp3glo9R07/GKR+4PB\n+wvQp4P7efRHerMsIQlT6Pwyb4vq4StKCel0Wgnkgszk7xzxWcfcgKOQ2h08tqGzVGLyTzeZ20XH\nJP1HJB4JY+1alabptIxGZouajH/mMLjm+CULaHbKnQvV0unxx1f08BlJeClsjqZ6+IpShoqabG1O\nMlEJSehTfcBzK6T6ovvpDab3gn4/N+XDiFHc2hCn0jSdWWoq48x7aJjwDAiyb7VPNcJcrTLDK71A\nQtdxeAebyrwpKuArSglFRIDBUPBzE/iYdJ//w+bxEz1SAI9tZPSIpMqJh1nqdY1Jxr7MvjIaAl+B\nV1vneq1K03QSHh4MMr7Onz5f394zQcKFWpfoumcAi11WM6n7JBJPJpZ5U1TAV5QSCg3VsjDq1Mn/\n3BGfddwMmMLbsbX4hwncLYCQ3Gh0gJapdfgweA+vD00B77mQ3CfXa1WapmMKjIhk9jszsydlZldt\nzNrhX4BrGgYrDExsBOlu4JpGQpeVTHtiEhm2jPuy2b0K+IpSCkJD4dw5bSxfSli6NHMyt3kc4oeZ\nRCRcIK5JTaYt64Au9iMqXarHodaHyJAG1rWG5olPwIY52edTaZqOy/+GjrD0GcwWNUFKvmymA72V\nh0/WIjYKvt1vYJbuPVrr+uPVtN19C/agJm0V5b4ZUiOOLVc68CCH+Rkfqr5Zl2s1L1L1Ui2ufXKO\nDrp9/CbbqxLKji5zkjYs+Djdk9qxzWuftpGJvFQqaZd3oiZtFcWOsipjxlzxpwN7+IDJ6APHcK3G\nRVwuNeBajYvoA8fwT9ub2GxaTFDB3oFlTtJ2T2rH1l6b6Z7UjnHmPdhSUu2edqsCvqKUoayqmlmL\ns+LxJzpwE1bvryBxDBmf/AnHHsPq/RXRgbezNEzJJiK3l97m1cp95OHBbGMHtnnto8fmXmzz2sds\nYwdS8UBK7d/CmDH2Cfoq4CtKGSqoqqapGXRLfAQ2fK49EP8hLhl61rfKfD7ZRMiqELwb5d8LVyn/\nZo94jbDM/Wi3mDZn71M7xHh7IxN7pd2qgK8oZaig1Mpn/+8tdmzYjTvXmcI03M2PkREVy3lDFd41\nvUvIqhBW/mUlfs387n+DlbsWGQmmyXG5lkibJscx708br56YSMixS9gQDDFfomP0RH5tnHsjE3uk\n3ao9bRWlDBW0j+ksxuNOGusYgB+bOE19FpufpXrScKa7T2fK7hpwyUpkDUpl5aVS+qKiYHHsi0Qc\nDWRNSjP8SMGU0ozBy67Su+9hFqz5ks+sYbdfYM685WCPtFvVw1eUMlRQVc0L1KMLCTQjGRuCG1QG\no4lzXZcwcjd82jKNActusPjdw4jmJmr1j8w33qu2SLSfrHmZyUfXIYKHMtj4Ou/yHoONryOChxL6\n47p8w3h52S3tVkpZLm+dO3eWiuIMli6V0tNTSiG0r2PHSunmlpWxL2Xf/p6SidVlZZ/3pdsEN1nZ\n533JxOqy8zP1JRPqSozx0s1NO0/W+XK+HmSu55WyVbOmlH3YKG0g441ItwluEr8p0jChhow3Iq2I\nXL+bnLesfwNl+bsCkmQhcVXl4SuKHWSVVk5NhdFPCZa11WNZsZZ0qsCwwRj0V9EjuRkVD2ZtLN/T\nU0vZLGx/1KznlbLlL+LYTQeiCQGgn583t3p9TKXNE/jelEhLfTJNreZ8r7tfv5+i8vDVGL6i2EFo\naI5cewGt97VjfPAoSBoLOgsWg+StzbDDbCU+87CsSb7CJvtU7Z2yFRkJR/57mIl8gB7JYGK4YfwZ\ni9cIHt0cyBGvTxicvILp7avhFpe/+ml5WDmtxvAVxc5M1QcxzbwJl6S/Qq/pAFTaPIF/elUhyXiV\nPmhb3WVN8hW1vaJSNgIjIvkg5RHmN3idwca/A+DqMwPLiAGQVgev9IN8E21BjHqe/T565s3TevRC\naF/LS6lrFfAVxQ5yTrr+7dbHWIxbqfTYTCql68BqwJrch/TobxDBQxltHIHBcLuHWNBEcHnpQTqj\nyO2RGG8e5XLNo9ha/ZfrI56mz9CqnOv7MegtUOMYeqsgqW1NYkbF0KJnYvbmOOVt5bQK+Ipyn+Vc\nfSsl7G94HEvwSIbvt/L9Mhuu541kPPM07dhHTLSF1Manad15FGO3aTthh4ZSbnuQziRyeyQvrXuJ\nrZtcWJa+kLHxDSGjkhbkW68HnQ29xZWx8Q35vMcl1pknc3KH330rhHYvShTwhRC1hRAbhRCHM7/W\nKuQ4qxBid+ZtbUmuqSiOLt/q28aJWKJjePd7TzD3Ru4bBq432NM1Dsy92WHtwd6AJbTYWy/7JeW1\nB+lMvBt5s3jXcuJuTMGyZTKLe5tBl55rExP+7MyyHqn8I/ohtu0Ps1vJhLtVoiwdIUQkcEFKOUMI\nMRGoJaV8q4Djrkkpqxbn3CpLR3FWhW2J+Dhx7KEDKwnhEx8rawO2QWo38NjBwNjurE7YgYvMuP8N\nrqACA8FwZjpr/Wdi0Kdh0dtAb9M2MMkiwLBnKD1iRhOPP2D/bKmyrJY5CFiUeX8RMLiE51MUp1fY\n5Gq80Kpp9mYT3yZspXrqI+C5nUqpXnybsBU91vvb0ApO9/tvrN05mc6/dMPimpEr2Hc7mLnbjQRL\n+xXE+/yW/brynC1V0oBfX0r5Z+b9U0D9Qo6rLIRIEkIkCCEK/aMghBiTeVzS2bNnS9g0RSmfCpp0\nBa3X/yP+WNEzyKcHVzz2Uj2lPbc8khjk0wOp09//xlZgZ3390AeOYafPFi3Q24R2u1aXn1qfp39i\nI6oc6g3nWkOfKWDUqp2W52ypO+bhCyHigAYFPJWr1puUUgohChsf8pRSnhBCNAfihRB7pZRH8h4k\npZwHzANtSOeOrVcUB5Q13p618EqnA2uOznsnn1D2BSxhYGx3vk3YyiCfHqwN2EaQcSTf2qfJFdKj\nly7wi/dXYNND7CyofRi8P0fndh5D4nMcuOzCjQ1fagcbTdA4EbczfuU6W+qOAV9K6V/Yc0KI00KI\nhlLKP4UQDYEzhZzjRObXo0KITUAnIF/AV5SKIufCK12ez9n7mp+lXexIVidEIYGYX3YQZBzJjvrq\nU29Zi4wE78tx+EWNxtgE6h8zcrppCjy8Bjy20TaxO3/omvHg5Qz2b19InTpQtSqkpvjhIf2IKOfZ\nUiVdabsWGAXMyPyarwOSmbmTJqW8JYSoC/gCamcHRcmUr6Lmsg3sAwyZ02OeTcH8tV2aVuEcufoi\nEcu0CpiGlCBOb58FL3cEz63oUrqxf8NmXuRLvuQlDAb417/Kd4DPq6Rj+DOAvkKIw4B/5vcIIbyE\nEPMzj3kYSBJC7AFMwAwp5e8lvK6iOI3CxvSzlOdJQEdUVKXRYRtvV8CcxXgIfBXq7+WRUyA9dmDw\n+YjVDKFOHVi40LGCPaCqZSpKebB0qZR6fcEVFj09tWP69ZNyVrdo+VF3IeONSKnXy1ndomW/flLG\nH42XH237yK7vwREUVGkUpAxw2ShTdZ7ZFTArTagmGdFXMhU5NhBpAzmlay3JVCH7vz/L3m+jSBRR\nLVOttFWUciA0FBYtKrpkQnLzRxlvS8F8fAghwfBK08GMt6Ww/6GW+bZEVPXyC1bQlpNBrGJnRgf+\nsDVDANHmz7iV9AY8uBGXI70I3tCbFDxZfPICw+vOxGqMs0vbS4Mqj6wo5UjOsskeHlqwzxo2mN1N\nx/gnJMTOpNmpGiQ/8wa4puGWDtN1H5NRPYzGjeGNN+D8+dz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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jWxvLGexKv0D", + "colab_type": "text" + }, + "source": [ + "We can see from the graph that the predictions for the original model, the converted model, and the quantized model are all close enough to be indistinguishable. This means that our quantized model is ready to use!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HPSFmDL7pv2L", + "colab_type": "text" + }, + "source": [ + "## Generate a TensorFlow Lite for Microcontrollers Model\n", + "Convert the TensorFlow Lite quantized model into a C source file that can be loaded by TensorFlow Lite for Microcontrollers." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "j1FB4ieeg0lw", + "colab_type": "code", + "outputId": "a2ba48f0-c440-409a-dad0-747a22ac3a64", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 476 + } + }, + "source": [ + "# Install xxd if it is not available\n", + "!apt-get update && apt-get -qq install xxd\n", + "# Convert to a C source file\n", + "!xxd -i {MODEL_TFLITE} > {MODEL_TFLITE_MICRO}\n", + "# Update variable names\n", + "REPLACE_TEXT = MODEL_TFLITE.replace('/', '_').replace('.', '_')\n", + "!sed -i 's/'{REPLACE_TEXT}'/g_model/g' {MODEL_TFLITE_MICRO}" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Get:1 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran35/ InRelease [3,626 B]\n", + "Ign:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 InRelease\n", + "Hit:3 http://archive.ubuntu.com/ubuntu bionic InRelease\n", + "Get:4 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n", + "Hit:5 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n", + "Ign:6 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 InRelease\n", + "Hit:7 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 Release\n", + "Hit:8 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 Release\n", + "Get:9 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n", + "Get:10 http://ppa.launchpad.net/marutter/c2d4u3.5/ubuntu bionic InRelease [15.4 kB]\n", + "Get:11 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]\n", + "Get:14 http://ppa.launchpad.net/marutter/c2d4u3.5/ubuntu bionic/main Sources [1,810 kB]\n", + "Get:15 http://security.ubuntu.com/ubuntu bionic-security/restricted amd64 Packages [38.5 kB]\n", + "Get:16 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [873 kB]\n", + "Get:17 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [1,368 kB]\n", + "Get:18 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [835 kB]\n", + "Get:19 http://archive.ubuntu.com/ubuntu bionic-updates/restricted amd64 Packages [57.5 kB]\n", + "Get:20 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [1,176 kB]\n", + "Get:21 http://ppa.launchpad.net/marutter/c2d4u3.5/ubuntu bionic/main amd64 Packages [873 kB]\n", + "Fetched 7,301 kB in 3s (2,475 kB/s)\n", + "Reading package lists... Done\n", + "Selecting previously unselected package xxd.\n", + "(Reading database ... 144568 files and directories currently installed.)\n", + "Preparing to unpack .../xxd_2%3a8.0.1453-1ubuntu1.3_amd64.deb ...\n", + "Unpacking xxd (2:8.0.1453-1ubuntu1.3) ...\n", + "Setting up xxd (2:8.0.1453-1ubuntu1.3) ...\n", + "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JvRy0ZyMhQOX", + "colab_type": "text" + }, + "source": [ + "## Deploy to a Microcontroller\n", + "\n", + "Follow the instructions in the [hello_world](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/hello_world) README.md for [TensorFlow Lite for MicroControllers](https://www.tensorflow.org/lite/microcontrollers/overview) to deploy this model on a specific microcontroller.\n", + "\n", + "**Reference Model:** If you have not modified this notebook, you can follow the instructions as is, to deploy the model. Refer to the [`hello_world/train/models`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/hello_world/train/models) directory to access the models generated in this notebook.\n", + "\n", + "**New Model:** If you have generated a new model, then update the values assigned to the variables defined in [`hello_world/model.cc`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/hello_world/model.cc) with values displayed after running the following cell." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "l4-WhtGpvb-E", + "colab_type": "code", + "outputId": "ba008623-d568-43b1-a824-68adbe811567", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "source": [ + "# Print the C source file\n", + "!cat {MODEL_TFLITE_MICRO}" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "text": [ + "unsigned char g_model[] = {\n", + " 0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 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a/tensorflow/lite/micro/examples/micro_speech/README.md +++ b/tensorflow/lite/micro/examples/micro_speech/README.md @@ -545,4 +545,4 @@ with the model and sample inputs. So far you have used an existing trained model to run inference on microcontrollers. If you wish to train your own model, follow the instructions -in [train/README.md](train/README.md). +given in the [train/](train/) directory. diff --git a/tensorflow/lite/micro/examples/micro_speech/train/README.md b/tensorflow/lite/micro/examples/micro_speech/train/README.md index 25277feb5ab..8e65f2bb13a 100644 --- a/tensorflow/lite/micro/examples/micro_speech/train/README.md +++ b/tensorflow/lite/micro/examples/micro_speech/train/README.md @@ -23,28 +23,48 @@ stop go ``` +The scripts used in training the model have been sourced from the +[Simple Audio Recognition](https://www.tensorflow.org/tutorials/sequences/audio_recognition) +tutorial. + ## Table of contents - [Overview](#overview) -- [Trained Models](#trained-models) - [Training](#training) +- [Trained Models](#trained-models) - [Model Architecture](#model-architecture) - [Dataset](#dataset) - [Preprocessing Speech Input](#preprocessing-speech-input) +- [Other Training Methods](#other-training-methods) ## Overview -1. Training Jupyter Notebook: [`train_micro_speech_model.ipynb`](train_micro_speech_model.ipynb) -. The training scripts used in this notebook are in the -[Simple Audio Recognition](https://www.tensorflow.org/tutorials/sequences/audio_recognition) -tutorial. -2. Dataset Type: **Speech** -3. Dataset: Speech Commands, Version 2. ([Download Link](https://storage.cloud.google.com/download.tensorflow.org/data/speech_commands_v0.02.tar.gz) +1. Dataset: Speech Commands, Version 2. ([Download Link](https://storage.cloud.google.com/download.tensorflow.org/data/speech_commands_v0.02.tar.gz) , [Paper](https://arxiv.org/abs/1804.03209)) -4. Deep Learning Framework: **TensorFlow 1.5** -5. Language: **Python 3.7** -6. Model Size: **<20 kB** -7. Model Category: **Multiclass Classification** +2. Dataset Type: **Speech** +3. Deep Learning Framework: **TensorFlow 1.5** +4. Language: **Python 3.7** +5. Model Size: **<20 kB** +6. Model Category: **Multiclass Classification** + +## Training + +Train the model in the cloud using Google Colaboratory or locally using a +Jupyter Notebook. + + + + +
+ Google Colaboratory + + Jupyter Notebook +
+ +*Estimated Training Time: ~2 Hours.* + +For more options, refer to the [Other Training Methods](#other-training-methods) +section. ## Trained Models @@ -52,7 +72,7 @@ tutorial. | ------------- |-------------| The `models` directory in the above zip file can be generated by following the -instructions in the [Training](#training) section below. It +instructions in the [Training](#training) section above. It includes the following 3 model files: | Name | Format | Target Framework | Target Device | @@ -61,67 +81,11 @@ includes the following 3 model files: | `model.tflite` *(<20 kB)* | Fully Quantized* TFLite Model | TensorFlow Lite | Mobile Devices| | `model.cc` | C Source File | TensorFlow Lite for Microcontrollers | Microcontrollers | -*Fully quantized implies that the model is **strictly int8** quantized +**Fully quantized implies that the model is **strictly int8** quantized **including** the input(s) and output(s).* - -## Training - -### 1. Use [Google Colaboratory](https://colab.research.google.com) - -*We strongly recommend trying this approach first.* - -| Run in Google Colaboratory | train_micro_speech_model.ipynb | -| ------------- |-------------| - -**Estimated Training Time:** ~2 hours. -**Advantage:** It allows the use of a free Tesla K80 GPU for training and avoids -the need to install dependencies. -**Disadvantage:** Your training time is limited as the session can only run -upto 12 hours in a row if you keep the browser open and 90 minutes if you close -the browser. - -### 2. Use [Google Cloud](https://cloud.google.com/) - -1. Create a Virtual Machine (VM) using a pre-configured Deep Learning VM Image. - -``` -export IMAGE_FAMILY="tf-latest-cpu" -export ZONE="us-west1-b" # Or any other required region -export INSTANCE_NAME="model-trainer" -export INSTANCE_TYPE="n1-standard-8" # or any other instance type -gcloud compute instances create $INSTANCE_NAME \ - --zone=$ZONE \ - --image-family=$IMAGE_FAMILY \ - --image-project=deeplearning-platform-release \ - --machine-type=$INSTANCE_TYPE \ - --boot-disk-size=120GB \ - --min-cpu-platform=Intel\ Skylake -``` - -2. As soon as instance has been created you can SSH to it: - -``` -gcloud compute ssh "jupyter@${INSTANCE_NAME}" -``` - -3. Train a model by following the instructions in the [`train_micro_speech_model.ipynb`](train_micro_speech_model.ipynb) -jupyter notebook. - -4. Finally, don't forget to remove the instance when training is done: - -``` -gcloud compute instances delete "${INSTANCE_NAME}" --zone="${ZONE}" -``` - -**Estimated Training Time:** ~2 hours (with GPU) and ~1 day (with CPU). -**Advantage:** There are no time constraints on how long the training process -can take and it avoids the need to install dependencies. -**Disadvantage:** Google Cloud isn't free. You need to pay -depending on how long you use run the VM and what resources you use. - ## Model Architecture This is a simple model comprising of a Convolutional 2D layer, a Fully Connected @@ -197,3 +161,41 @@ python tensorflow/tensorflow/examples/speech_commands/wav_to_features.py \ --window_stride=20 --preprocess=average --quantize=1 ``` + +## Other Training Methods + +### Use [Google Cloud](https://cloud.google.com/). + +*Note: Google Cloud isn't free. You need to pay depending on how long you use +run the VM and what resources you use.* + +1. Create a Virtual Machine (VM) using a pre-configured Deep Learning VM Image. + +``` +export IMAGE_FAMILY="tf-latest-cpu" +export ZONE="us-west1-b" # Or any other required region +export INSTANCE_NAME="model-trainer" +export INSTANCE_TYPE="n1-standard-8" # or any other instance type +gcloud compute instances create $INSTANCE_NAME \ + --zone=$ZONE \ + --image-family=$IMAGE_FAMILY \ + --image-project=deeplearning-platform-release \ + --machine-type=$INSTANCE_TYPE \ + --boot-disk-size=120GB \ + --min-cpu-platform=Intel\ Skylake +``` + +2. As soon as instance has been created you can SSH to it: + +``` +gcloud compute ssh "jupyter@${INSTANCE_NAME}" +``` + +3. Train a model by following the instructions in the [`train_micro_speech_model.ipynb`](train_micro_speech_model.ipynb) +jupyter notebook. + +4. Finally, don't forget to remove the instance when training is done: + +``` +gcloud compute instances delete "${INSTANCE_NAME}" --zone="${ZONE}" +```