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utils_lenet.py
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utils_lenet.py
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# -*- coding:utf-8 -*-
# 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.
# ==============================================================================
"""Contains a variant of the LeNet model definition."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import tensorflow.contrib.slim as slim
def lenet(images, num_classes=10, is_training=False,
dropout_keep_prob=0.5,
prediction_fn=slim.softmax,
use_dropout=True,
scope='LeNet'):
"""Creates a variant of the LeNet model.
Note that since the output is a set of 'logits', the values fall in the
interval of (-infinity, infinity). Consequently, to convert the outputs to a
probability distribution over the characters, one will need to convert them
using the softmax function:
logits = lenet.lenet(images, is_training=False)
probabilities = tf.nn.softmax(logits)
predictions = tf.argmax(logits, 1)
Args:
images: A batch of `Tensors` of size [batch_size, height, width, channels].
num_classes: the number of classes in the dataset.
is_training: specifies whether or not we're currently training the model.
This variable will determine the behaviour of the dropout layer.
dropout_keep_prob: the percentage of activation values that are retained.
prediction_fn: a function to get predictions out of logits.
scope: Optional variable_scope.
Returns:
logits: the pre-softmax activations, a tensor of size
[batch_size, `num_classes`]
end_points: a dictionary from components of the network to the corresponding
activation.
"""
end_points = {}
with tf.variable_scope(scope, 'LeNet', [images, num_classes]):
net = slim.conv2d(images, 32, [5, 5], scope='conv1', weights_regularizer=tf.nn.l2_loss)
net = slim.max_pool2d(net, [2, 2], 2, scope='pool1')
net = slim.conv2d(net, 64, [5, 5], scope='conv2', weights_regularizer=tf.nn.l2_loss)
net = slim.max_pool2d(net, [2, 2], 2, scope='pool2')
net = slim.flatten(net)
end_points['Flatten'] = net
net = slim.fully_connected(net, 1024, scope='fc3', weights_regularizer=tf.nn.l2_loss)
if use_dropout:
net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
scope='dropout3')
logits = slim.fully_connected(net, num_classes, activation_fn=None,
scope='fc4', weights_regularizer=tf.nn.l2_loss)
end_points['Logits'] = logits
end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
return logits, end_points
lenet.default_image_size = 28
def lenet_double(images, num_classes=10, is_training=False,
dropout_keep_prob=0.5,
prediction_fn=slim.softmax,
scope='LeNet_double'):
"""Creates a variant of the LeNet model.
Note that since the output is a set of 'logits', the values fall in the
interval of (-infinity, infinity). Consequently, to convert the outputs to a
probability distribution over the characters, one will need to convert them
using the softmax function:
logits = lenet.lenet(images, is_training=False)
probabilities = tf.nn.softmax(logits)
predictions = tf.argmax(logits, 1)
Args:
images: A batch of `Tensors` of size [batch_size, height, width, channels].
num_classes: the number of classes in the dataset.
is_training: specifies whether or not we're currently training the model.
This variable will determine the behaviour of the dropout layer.
dropout_keep_prob: the percentage of activation values that are retained.
prediction_fn: a function to get predictions out of logits.
scope: Optional variable_scope.
Returns:
logits: the pre-softmax activations, a tensor of size
[batch_size, `num_classes`]
end_points: a dictionary from components of the network to the corresponding
activation.
"""
end_points = {}
with tf.variable_scope(scope, 'LeNet', [images, num_classes]):
net = slim.conv2d(images, 64, [5, 5], scope='conv1')
net = slim.max_pool2d(net, [2, 2], 2, scope='pool1')
net = slim.conv2d(net, 128, [5, 5], scope='conv2')
net = slim.max_pool2d(net, [2, 2], 2, scope='pool2')
net = slim.flatten(net)
end_points['Flatten'] = net
net = slim.fully_connected(net, 2048, scope='fc3')
net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
scope='dropout3')
logits = slim.fully_connected(net, num_classes, activation_fn=None,
scope='fc4')
end_points['Logits'] = logits
end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
return logits, end_points
def lenet_arg_scope(weight_decay=0.0):
"""Defines the default lenet argument scope.
Args:
weight_decay: The weight decay to use for regularizing the model.
Returns:
An `arg_scope` to use for the inception v3 model.
"""
with slim.arg_scope(
[slim.conv2d, slim.fully_connected],
weights_regularizer=slim.l2_regularizer(weight_decay),
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
activation_fn=tf.nn.relu) as sc:
return sc