74 lines
3.0 KiB
Python
74 lines
3.0 KiB
Python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This example showcases how simple it is to build image classification networks.
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It follows description from this TensorFlow tutorial:
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https://www.tensorflow.org/versions/master/tutorials/mnist/pros/index.html#deep-mnist-for-experts
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from sklearn import metrics
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import tensorflow as tf
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from tensorflow.contrib import learn
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### Download and load MNIST data.
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mnist = learn.datasets.load_dataset('mnist')
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### Linear classifier.
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classifier = learn.TensorFlowLinearClassifier(
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n_classes=10, batch_size=100, steps=1000, learning_rate=0.01)
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classifier.fit(mnist.train.images, mnist.train.labels)
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score = metrics.accuracy_score(mnist.test.labels, classifier.predict(mnist.test.images))
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print('Accuracy: {0:f}'.format(score))
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### Convolutional network
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def max_pool_2x2(tensor_in):
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return tf.nn.max_pool(tensor_in, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
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padding='SAME')
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def conv_model(X, y):
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# reshape X to 4d tensor with 2nd and 3rd dimensions being image width and height
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# final dimension being the number of color channels
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X = tf.reshape(X, [-1, 28, 28, 1])
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# first conv layer will compute 32 features for each 5x5 patch
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with tf.variable_scope('conv_layer1'):
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h_conv1 = learn.ops.conv2d(X, n_filters=32, filter_shape=[5, 5],
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bias=True, activation=tf.nn.relu)
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h_pool1 = max_pool_2x2(h_conv1)
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# second conv layer will compute 64 features for each 5x5 patch
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with tf.variable_scope('conv_layer2'):
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h_conv2 = learn.ops.conv2d(h_pool1, n_filters=64, filter_shape=[5, 5],
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bias=True, activation=tf.nn.relu)
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h_pool2 = max_pool_2x2(h_conv2)
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# reshape tensor into a batch of vectors
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h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
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# densely connected layer with 1024 neurons
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h_fc1 = learn.ops.dnn(h_pool2_flat, [1024], activation=tf.nn.relu, dropout=0.5)
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return learn.models.logistic_regression(h_fc1, y)
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# Training and predicting
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classifier = learn.TensorFlowEstimator(
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model_fn=conv_model, n_classes=10, batch_size=100, steps=20000,
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learning_rate=0.001)
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classifier.fit(mnist.train.images, mnist.train.labels)
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score = metrics.accuracy_score(mnist.test.labels, classifier.predict(mnist.test.images))
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print('Accuracy: {0:f}'.format(score))
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