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