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| # Copyright 2016 Google Inc. 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. | |
| # ============================================================================== | |
| """Author: aneelakantan (Arvind Neelakantan) | |
| """ | |
| import tensorflow as tf | |
| def get_embedding(word, utility, params): | |
| return tf.nn.embedding_lookup(params["word"], word) | |
| def apply_dropout(x, dropout_rate, mode): | |
| if (dropout_rate > 0.0): | |
| if (mode == "train"): | |
| x = tf.nn.dropout(x, dropout_rate) | |
| else: | |
| x = x | |
| return x | |
| def LSTMCell(x, mprev, cprev, key, params): | |
| """Create an LSTM cell. | |
| Implements the equations in pg.2 from | |
| "Long Short-Term Memory Based Recurrent Neural Network Architectures | |
| For Large Vocabulary Speech Recognition", | |
| Hasim Sak, Andrew Senior, Francoise Beaufays. | |
| Args: | |
| w: A dictionary of the weights and optional biases as returned | |
| by LSTMParametersSplit(). | |
| x: Inputs to this cell. | |
| mprev: m_{t-1}, the recurrent activations (same as the output) | |
| from the previous cell. | |
| cprev: c_{t-1}, the cell activations from the previous cell. | |
| keep_prob: Keep probability on the input and the outputs of a cell. | |
| Returns: | |
| m: Outputs of this cell. | |
| c: Cell Activations. | |
| """ | |
| i = tf.matmul(x, params[key + "_ix"]) + tf.matmul(mprev, params[key + "_im"]) | |
| i = tf.nn.bias_add(i, params[key + "_i"]) | |
| f = tf.matmul(x, params[key + "_fx"]) + tf.matmul(mprev, params[key + "_fm"]) | |
| f = tf.nn.bias_add(f, params[key + "_f"]) | |
| c = tf.matmul(x, params[key + "_cx"]) + tf.matmul(mprev, params[key + "_cm"]) | |
| c = tf.nn.bias_add(c, params[key + "_c"]) | |
| o = tf.matmul(x, params[key + "_ox"]) + tf.matmul(mprev, params[key + "_om"]) | |
| o = tf.nn.bias_add(o, params[key + "_o"]) | |
| i = tf.sigmoid(i, name="i_gate") | |
| f = tf.sigmoid(f, name="f_gate") | |
| o = tf.sigmoid(o, name="o_gate") | |
| c = f * cprev + i * tf.tanh(c) | |
| m = o * c | |
| return m, c | |