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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 numpy as np | |
| import tensorflow as tf | |
| class Parameters: | |
| def __init__(self, u): | |
| self.utility = u | |
| self.init_seed_counter = 0 | |
| self.word_init = {} | |
| def parameters(self, utility): | |
| params = {} | |
| inits = [] | |
| embedding_dims = self.utility.FLAGS.embedding_dims | |
| params["unit"] = tf.Variable( | |
| self.RandomUniformInit([len(utility.operations_set), embedding_dims])) | |
| params["word"] = tf.Variable( | |
| self.RandomUniformInit([utility.FLAGS.vocab_size, embedding_dims])) | |
| params["word_match_feature_column_name"] = tf.Variable( | |
| self.RandomUniformInit([1])) | |
| params["controller"] = tf.Variable( | |
| self.RandomUniformInit([2 * embedding_dims, embedding_dims])) | |
| params["column_controller"] = tf.Variable( | |
| self.RandomUniformInit([2 * embedding_dims, embedding_dims])) | |
| params["column_controller_prev"] = tf.Variable( | |
| self.RandomUniformInit([embedding_dims, embedding_dims])) | |
| params["controller_prev"] = tf.Variable( | |
| self.RandomUniformInit([embedding_dims, embedding_dims])) | |
| global_step = tf.Variable(1, name="global_step") | |
| #weigths of question and history RNN (or LSTM) | |
| key_list = ["question_lstm"] | |
| for key in key_list: | |
| # Weights going from inputs to nodes. | |
| for wgts in ["ix", "fx", "cx", "ox"]: | |
| params[key + "_" + wgts] = tf.Variable( | |
| self.RandomUniformInit([embedding_dims, embedding_dims])) | |
| # Weights going from nodes to nodes. | |
| for wgts in ["im", "fm", "cm", "om"]: | |
| params[key + "_" + wgts] = tf.Variable( | |
| self.RandomUniformInit([embedding_dims, embedding_dims])) | |
| #Biases for the gates and cell | |
| for bias in ["i", "f", "c", "o"]: | |
| if (bias == "f"): | |
| print("forget gate bias") | |
| params[key + "_" + bias] = tf.Variable( | |
| tf.random_uniform([embedding_dims], 1.0, 1.1, self.utility. | |
| tf_data_type[self.utility.FLAGS.data_type])) | |
| else: | |
| params[key + "_" + bias] = tf.Variable( | |
| self.RandomUniformInit([embedding_dims])) | |
| params["history_recurrent"] = tf.Variable( | |
| self.RandomUniformInit([3 * embedding_dims, embedding_dims])) | |
| params["history_recurrent_bias"] = tf.Variable( | |
| self.RandomUniformInit([1, embedding_dims])) | |
| params["break_conditional"] = tf.Variable( | |
| self.RandomUniformInit([2 * embedding_dims, embedding_dims])) | |
| init = tf.global_variables_initializer() | |
| return params, global_step, init | |
| def RandomUniformInit(self, shape): | |
| """Returns a RandomUniform Tensor between -param_init and param_init.""" | |
| param_seed = self.utility.FLAGS.param_seed | |
| self.init_seed_counter += 1 | |
| return tf.random_uniform( | |
| shape, -1.0 * | |
| (np.float32(self.utility.FLAGS.param_init) | |
| ).astype(self.utility.np_data_type[self.utility.FLAGS.data_type]), | |
| (np.float32(self.utility.FLAGS.param_init) | |
| ).astype(self.utility.np_data_type[self.utility.FLAGS.data_type]), | |
| self.utility.tf_data_type[self.utility.FLAGS.data_type], | |
| param_seed + self.init_seed_counter) | |