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# 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 example builds deep residual network for mnist data.
Reference Paper: http://arxiv.org/pdf/1512.03385.pdf
Note that this is still a work-in-progress. Feel free to submit a PR
to make this better.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import namedtuple
from math import sqrt
import os
import tensorflow as tf
batch_norm = tf.contrib.layers.batch_norm
convolution2d = tf.contrib.layers.convolution2d
def res_net(x, y, activation=tf.nn.relu):
"""Builds a residual network.
Note that if the input tensor is 2D, it must be square in order to be
converted to a 4D tensor.
Borrowed structure from:
github.com/pkmital/tensorflow_tutorials/blob/master/10_residual_network.py
Args:
x: Input of the network
y: Output of the network
activation: Activation function to apply after each convolution
Returns:
Predictions and loss tensors.
"""
# Configurations for each bottleneck group.
BottleneckGroup = namedtuple('BottleneckGroup',
['num_blocks', 'num_filters', 'bottleneck_size'])
groups = [
BottleneckGroup(3, 128, 32), BottleneckGroup(3, 256, 64),
BottleneckGroup(3, 512, 128), BottleneckGroup(3, 1024, 256)
]
input_shape = x.get_shape().as_list()
# Reshape the input into the right shape if it's 2D tensor
if len(input_shape) == 2:
ndim = int(sqrt(input_shape[1]))
x = tf.reshape(x, [-1, ndim, ndim, 1])
# First convolution expands to 64 channels
with tf.variable_scope('conv_layer1'):
net = convolution2d(
x, 64, 7, normalizer_fn=batch_norm, activation_fn=activation)
# Max pool
net = tf.nn.max_pool(net, [1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
# First chain of resnets
with tf.variable_scope('conv_layer2'):
net = convolution2d(net, groups[0].num_filters, 1, padding='VALID')
# Create the bottleneck groups, each of which contains `num_blocks`
# bottleneck groups.
for group_i, group in enumerate(groups):
for block_i in range(group.num_blocks):
name = 'group_%d/block_%d' % (group_i, block_i)
# 1x1 convolution responsible for reducing dimension
with tf.variable_scope(name + '/conv_in'):
conv = convolution2d(
net,
group.bottleneck_size,
1,
padding='VALID',
activation_fn=activation,
normalizer_fn=batch_norm)
with tf.variable_scope(name + '/conv_bottleneck'):
conv = convolution2d(
conv,
group.bottleneck_size,
3,
padding='SAME',
activation_fn=activation,
normalizer_fn=batch_norm)
# 1x1 convolution responsible for restoring dimension
with tf.variable_scope(name + '/conv_out'):
input_dim = net.get_shape()[-1].value
conv = convolution2d(
conv,
input_dim,
1,
padding='VALID',
activation_fn=activation,
normalizer_fn=batch_norm)
# shortcut connections that turn the network into its counterpart
# residual function (identity shortcut)
net = conv + net
try:
# upscale to the next group size
next_group = groups[group_i + 1]
with tf.variable_scope('block_%d/conv_upscale' % group_i):
net = convolution2d(
net,
next_group.num_filters,
1,
activation_fn=None,
biases_initializer=None,
padding='SAME')
except IndexError:
pass
net_shape = net.get_shape().as_list()
net = tf.nn.avg_pool(
net,
ksize=[1, net_shape[1], net_shape[2], 1],
strides=[1, 1, 1, 1],
padding='VALID')
net_shape = net.get_shape().as_list()
net = tf.reshape(net, [-1, net_shape[1] * net_shape[2] * net_shape[3]])
target = tf.one_hot(y, depth=10, dtype=tf.float32)
logits = tf.contrib.layers.fully_connected(net, 10, activation_fn=None)
loss = tf.contrib.losses.softmax_cross_entropy(logits, target)
return tf.softmax(logits), loss
def res_net_model(x, y):
prediction, loss = res_net(x, y)
predicted = tf.argmax(prediction, 1)
accuracy = tf.equal(predicted, tf.cast(y, tf.int64))
predictions = {'prob': prediction, 'class': predicted, 'accuracy': accuracy}
train_op = tf.contrib.layers.optimize_loss(
loss,
tf.contrib.framework.get_global_step(),
optimizer='Adagrad',
learning_rate=0.001)
return predictions, loss, train_op
# Download and load MNIST data.
mnist = tf.contrib.learn.datasets.load_dataset('mnist')
# Create a new resnet classifier.
classifier = tf.contrib.learn.Estimator(model_fn=res_net_model)
tf.logging.set_verbosity(tf.logging.INFO) # Show training logs. (avoid silence)
# Train model and save summaries into logdir.
classifier.fit(mnist.train.images,
mnist.train.labels,
batch_size=100,
steps=1000)
# Calculate accuracy.
result = classifier.evaluate(
x=mnist.test.images,
y=mnist.test.labels,
metrics={
'accuracy':
tf.contrib.learn.metric_spec.MetricSpec(
metric_fn=tf.contrib.metrics.streaming_accuracy,
prediction_key='accuracy'),
})
score = result['accuracy']
print('Accuracy: {0:f}'.format(score))
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