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# Copyright 2015-present The Scikit Flow 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 division, print_function, absolute_import
import os
from collections import namedtuple
from math import sqrt
from sklearn import metrics
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib import skflow
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 here: https://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
"""
# Configurations for each bottleneck block
BottleneckBlock = namedtuple(
'BottleneckBlock', ['num_layers', 'num_filters', 'bottleneck_size'])
blocks = [BottleneckBlock(3, 128, 32),
BottleneckBlock(3, 256, 64),
BottleneckBlock(3, 512, 128),
BottleneckBlock(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 = skflow.ops.conv2d(x, 64, [7, 7], batch_norm=True,
activation=activation, bias=False)
# 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 = skflow.ops.conv2d(net, blocks[0].num_filters,
[1, 1], [1, 1, 1, 1],
padding='VALID', bias=True)
# Create each bottleneck building block for each layer
for block_i, block in enumerate(blocks):
for layer_i in range(block.num_layers):
name = 'block_%d/layer_%d' % (block_i, layer_i)
# 1x1 convolution responsible for reducing dimension
with tf.variable_scope(name + '/conv_in'):
conv = skflow.ops.conv2d(net, block.num_filters,
[1, 1], [1, 1, 1, 1],
padding='VALID',
activation=activation,
batch_norm=True,
bias=False)
with tf.variable_scope(name + '/conv_bottleneck'):
conv = skflow.ops.conv2d(conv, block.bottleneck_size,
[3, 3], [1, 1, 1, 1],
padding='SAME',
activation=activation,
batch_norm=True,
bias=False)
# 1x1 convolution responsible for restoring dimension
with tf.variable_scope(name + '/conv_out'):
conv = skflow.ops.conv2d(conv, block.num_filters,
[1, 1], [1, 1, 1, 1],
padding='VALID',
activation=activation,
batch_norm=True,
bias=False)
# shortcut connections that turn the network into its counterpart
# residual function (identity shortcut)
net = conv + net
try:
# upscale to the next block size
next_block = blocks[block_i + 1]
with tf.variable_scope('block_%d/conv_upscale' % block_i):
net = skflow.ops.conv2d(net, next_block.num_filters,
[1, 1], [1, 1, 1, 1],
bias=False,
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]])
return skflow.models.logistic_regression(net, y)
# Download and load MNIST data.
mnist = input_data.read_data_sets('MNIST_data')
# Restore model if graph is saved into a folder.
if os.path.exists("models/resnet/graph.pbtxt"):
classifier = skflow.TensorFlowEstimator.restore("models/resnet/")
else:
# Create a new resnet classifier.
classifier = skflow.TensorFlowEstimator(
model_fn=res_net, n_classes=10, batch_size=100, steps=100,
learning_rate=0.001, continue_training=True)
while True:
# Train model and save summaries into logdir.
classifier.fit(mnist.train.images, mnist.train.labels, logdir="models/resnet/")
# Calculate accuracy.
score = metrics.accuracy_score(
mnist.test.labels, classifier.predict(mnist.test.images, batch_size=64))
print('Accuracy: {0:f}'.format(score))
# Save model graph and checkpoints.
classifier.save("models/resnet/")
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