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# Copyright 2017 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.
# ==============================================================================
import tensorflow as tf
def fc_layer(name,
bottom,
output_dim,
bias_term=True,
weights_initializer=None,
biases_initializer=None,
reuse=None):
# flatten bottom input
shape = bottom.get_shape().as_list()
input_dim = 1
for d in shape[1:]:
input_dim *= d
flat_bottom = tf.reshape(bottom, [-1, input_dim])
# weights and biases variables
with tf.variable_scope(name, reuse=reuse):
# initialize the variables
if weights_initializer is None:
weights_initializer = tf.contrib.layers.xavier_initializer()
if bias_term and biases_initializer is None:
biases_initializer = tf.constant_initializer(0.)
# weights has shape [input_dim, output_dim]
weights = tf.get_variable(
'weights', [input_dim, output_dim], initializer=weights_initializer)
if bias_term:
biases = tf.get_variable(
'biases', output_dim, initializer=biases_initializer)
if not reuse:
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES,
tf.nn.l2_loss(weights))
if bias_term:
fc = tf.nn.xw_plus_b(flat_bottom, weights, biases)
else:
fc = tf.matmul(flat_bottom, weights)
return fc
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