Stop using deprecated enum tf.lite.Optimize.OPTIMIZE_FOR_SIZE and some other clean up
PiperOrigin-RevId: 313688439 Change-Id: Ice2c0d9aefc57681c20d11dade772251a9eab84e
This commit is contained in:
Khanh LeViet
TensorFlower Gardener
parent
347fe6ece4
commit
5c9dd54540
@ -61,6 +61,9 @@
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" \u003ctd\u003e\n",
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" \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/performance/post_training_float16_quant.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
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" \u003c/td\u003e\n",
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" \u003ctd\u003e\n",
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" \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/tensorflow/lite/g3doc/performance/post_training_float16_quant.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
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" \u003c/td\u003e\n",
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"\u003c/table\u003e"
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]
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},
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@ -113,13 +116,7 @@
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"import logging\n",
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"logging.getLogger(\"tensorflow\").setLevel(logging.DEBUG)\n",
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"\n",
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"try:\n",
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" # %tensorflow_version only exists in Colab.\n",
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" import tensorflow.compat.v2 as tf\n",
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"except Exception:\n",
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" pass\n",
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"tf.enable_v2_behavior()\n",
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"\n",
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"import tensorflow as tf\n",
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"from tensorflow import keras\n",
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"import numpy as np\n",
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"import pathlib"
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@ -173,12 +170,12 @@
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" keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation=tf.nn.relu),\n",
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" keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
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" keras.layers.Flatten(),\n",
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" keras.layers.Dense(10, activation=tf.nn.softmax)\n",
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" keras.layers.Dense(10)\n",
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"])\n",
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"\n",
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"# Train the digit classification model\n",
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"model.compile(optimizer='adam',\n",
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" loss='sparse_categorical_crossentropy',\n",
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" loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
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" metrics=['accuracy'])\n",
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"model.fit(\n",
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" train_images,\n",
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@ -597,28 +594,11 @@
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"\n",
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"Detailed documentation on the TFLite GPU delegate and how to use it in your application can be found [here](https://www.tensorflow.org/lite/performance/gpu_advanced?source=post_page---------------------------)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "BeUSdwKVixvk"
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},
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"outputs": [],
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"source": [
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""
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]
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}
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],
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"metadata": {
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"colab": {
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"collapsed_sections": [],
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"last_runtime": {
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"build_target": "//learning/brain/python/client:colab_notebook_py3",
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"kind": "private"
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},
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"name": "post_training-float16-quant.ipynb",
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"private_outputs": true,
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"provenance": [],
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@ -61,6 +61,9 @@
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" \u003ctd\u003e\n",
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" \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/performance/post_training_integer_quant.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
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" \u003c/td\u003e\n",
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" \u003ctd\u003e\n",
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" \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/tensorflow/lite/g3doc/performance/post_training_integer_quant.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
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" \u003c/td\u003e\n",
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"\u003c/table\u003e"
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]
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},
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@ -114,13 +117,7 @@
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"import logging\n",
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"logging.getLogger(\"tensorflow\").setLevel(logging.DEBUG)\n",
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"\n",
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"try:\n",
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" # %tensorflow_version only exists in Colab.\n",
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" import tensorflow.compat.v2 as tf\n",
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"except Exception:\n",
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" pass\n",
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"tf.enable_v2_behavior()\n",
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"\n",
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"import tensorflow as tf\n",
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"from tensorflow import keras\n",
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"import numpy as np\n",
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"import pathlib"
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@ -158,15 +155,15 @@
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"model = keras.Sequential([\n",
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" keras.layers.InputLayer(input_shape=(28, 28)),\n",
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" keras.layers.Reshape(target_shape=(28, 28, 1)),\n",
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" keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation=tf.nn.relu),\n",
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" keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation='relu'),\n",
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" keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
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" keras.layers.Flatten(),\n",
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" keras.layers.Dense(10, activation=tf.nn.softmax)\n",
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" keras.layers.Dense(10)\n",
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"])\n",
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"\n",
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"# Train the digit classification model\n",
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"model.compile(optimizer='adam',\n",
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" loss='sparse_categorical_crossentropy',\n",
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" loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
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" metrics=['accuracy'])\n",
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"model.fit(\n",
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" train_images,\n",
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@ -277,7 +274,7 @@
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},
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"outputs": [],
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"source": [
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"converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]"
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"converter.optimizations = [tf.lite.Optimize.DEFAULT]"
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]
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},
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{
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@ -655,10 +652,6 @@
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"metadata": {
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"colab": {
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"collapsed_sections": [],
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"last_runtime": {
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"build_target": "//learning/brain/python/client:colab_notebook_py3",
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"kind": "private"
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},
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"name": "post_training_integer_quant.ipynb",
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"private_outputs": true,
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"provenance": [],
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@ -61,6 +61,9 @@
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" \u003ctd\u003e\n",
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" \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/performance/post_training_quant.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
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" \u003c/td\u003e\n",
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" \u003ctd\u003e\n",
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" \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/tensorflow/lite/g3doc/performance/post_training_quant.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
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" \u003c/td\u003e\n",
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"\u003c/table\u003e"
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]
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},
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@ -134,13 +137,7 @@
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"import logging\n",
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"logging.getLogger(\"tensorflow\").setLevel(logging.DEBUG)\n",
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"\n",
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"try:\n",
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" # %tensorflow_version only exists in Colab.\n",
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" import tensorflow.compat.v2 as tf\n",
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"except Exception:\n",
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" pass\n",
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"tf.enable_v2_behavior()\n",
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"\n",
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"import tensorflow as tf\n",
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"from tensorflow import keras\n",
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"import numpy as np\n",
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"import pathlib"
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@ -181,12 +178,12 @@
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" keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation=tf.nn.relu),\n",
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" keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
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" keras.layers.Flatten(),\n",
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" keras.layers.Dense(10, activation=tf.nn.softmax)\n",
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" keras.layers.Dense(10)\n",
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"])\n",
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"\n",
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"# Train the digit classification model\n",
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"model.compile(optimizer='adam',\n",
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" loss='sparse_categorical_crossentropy',\n",
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" loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
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" metrics=['accuracy'])\n",
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"model.fit(\n",
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" train_images,\n",
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@ -620,10 +617,6 @@
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"metadata": {
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"colab": {
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"collapsed_sections": [],
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"last_runtime": {
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"build_target": "//learning/brain/python/client:colab_notebook_py3",
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"kind": "private"
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},
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"name": "post_training_quant.ipynb",
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"private_outputs": true,
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"provenance": [],
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