Permalink
136 lines (112 sloc) 4.64 KB
# Copyright 2016 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.
"""This showcases how simple it is to build image classification networks.
It follows description from this TensorFlow tutorial:
https://www.tensorflow.org/versions/master/tutorials/mnist/pros/index.html#deep-mnist-for-experts
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
N_DIGITS = 10 # Number of digits.
X_FEATURE = 'x' # Name of the input feature.
def conv_model(features, labels, mode):
"""2-layer convolution model."""
# Reshape feature to 4d tensor with 2nd and 3rd dimensions being
# image width and height final dimension being the number of color channels.
feature = tf.reshape(features[X_FEATURE], [-1, 28, 28, 1])
# First conv layer will compute 32 features for each 5x5 patch
with tf.variable_scope('conv_layer1'):
h_conv1 = tf.layers.conv2d(
feature,
filters=32,
kernel_size=[5, 5],
padding='same',
activation=tf.nn.relu)
h_pool1 = tf.layers.max_pooling2d(
h_conv1, pool_size=2, strides=2, padding='same')
# Second conv layer will compute 64 features for each 5x5 patch.
with tf.variable_scope('conv_layer2'):
h_conv2 = tf.layers.conv2d(
h_pool1,
filters=64,
kernel_size=[5, 5],
padding='same',
activation=tf.nn.relu)
h_pool2 = tf.layers.max_pooling2d(
h_conv2, pool_size=2, strides=2, padding='same')
# reshape tensor into a batch of vectors
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
# Densely connected layer with 1024 neurons.
h_fc1 = tf.layers.dense(h_pool2_flat, 1024, activation=tf.nn.relu)
h_fc1 = tf.layers.dropout(
h_fc1,
rate=0.5,
training=(mode == tf.estimator.ModeKeys.TRAIN))
# Compute logits (1 per class) and compute loss.
logits = tf.layers.dense(h_fc1, N_DIGITS, activation=None)
# Compute predictions.
predicted_classes = tf.argmax(logits, 1)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
'class': predicted_classes,
'prob': tf.nn.softmax(logits)
}
return tf.estimator.EstimatorSpec(mode, predictions=predictions)
# Compute loss.
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
# Create training op.
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
# Compute evaluation metrics.
eval_metric_ops = {
'accuracy': tf.metrics.accuracy(
labels=labels, predictions=predicted_classes)
}
return tf.estimator.EstimatorSpec(
mode, loss=loss, eval_metric_ops=eval_metric_ops)
def main(unused_args):
tf.logging.set_verbosity(tf.logging.INFO)
### Download and load MNIST dataset.
mnist = tf.contrib.learn.datasets.DATASETS['mnist']('/tmp/mnist')
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={X_FEATURE: mnist.train.images},
y=mnist.train.labels.astype(np.int32),
batch_size=100,
num_epochs=None,
shuffle=True)
test_input_fn = tf.estimator.inputs.numpy_input_fn(
x={X_FEATURE: mnist.train.images},
y=mnist.train.labels.astype(np.int32),
num_epochs=1,
shuffle=False)
### Linear classifier.
feature_columns = [
tf.feature_column.numeric_column(
X_FEATURE, shape=mnist.train.images.shape[1:])]
classifier = tf.estimator.LinearClassifier(
feature_columns=feature_columns, n_classes=N_DIGITS)
classifier.train(input_fn=train_input_fn, steps=200)
scores = classifier.evaluate(input_fn=test_input_fn)
print('Accuracy (LinearClassifier): {0:f}'.format(scores['accuracy']))
### Convolutional network
classifier = tf.estimator.Estimator(model_fn=conv_model)
classifier.train(input_fn=train_input_fn, steps=200)
scores = classifier.evaluate(input_fn=test_input_fn)
print('Accuracy (conv_model): {0:f}'.format(scores['accuracy']))
if __name__ == '__main__':
tf.app.run()