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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 | |