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d302168c8c
* Parallel agents related bug fixes: checkpoint restore, tensorboard integration. Adding narrow networks support. Reference code for unlimited number of checkpoints
78 lines
3.0 KiB
Python
78 lines
3.0 KiB
Python
#
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# Copyright (c) 2017 Intel Corporation
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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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import tensorflow as tf
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import numpy as np
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from configurations import EmbedderWidth
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class MiddlewareEmbedder(object):
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def __init__(self, activation_function=tf.nn.relu, embedder_width=EmbedderWidth.Wide, name="middleware_embedder"):
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self.name = name
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self.input = None
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self.output = None
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self.embedder_width = embedder_width
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self.activation_function = activation_function
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def __call__(self, input_layer):
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with tf.variable_scope(self.get_name()):
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self.input = input_layer
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self._build_module()
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return self.input, self.output
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def _build_module(self):
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pass
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def get_name(self):
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return self.name
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class LSTM_Embedder(MiddlewareEmbedder):
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def _build_module(self):
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"""
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self.state_in: tuple of placeholders containing the initial state
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self.state_out: tuple of output state
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todo: it appears that the shape of the output is batch, feature
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the code here seems to be slicing off the first element in the batch
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which would definitely be wrong. need to double check the shape
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"""
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middleware = tf.layers.dense(self.input, 512, activation=self.activation_function, name='fc1')
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lstm_cell = tf.contrib.rnn.BasicLSTMCell(256, state_is_tuple=True)
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self.c_init = np.zeros((1, lstm_cell.state_size.c), np.float32)
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self.h_init = np.zeros((1, lstm_cell.state_size.h), np.float32)
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self.state_init = [self.c_init, self.h_init]
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self.c_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.c])
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self.h_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.h])
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self.state_in = (self.c_in, self.h_in)
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rnn_in = tf.expand_dims(middleware, [0])
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step_size = tf.shape(middleware)[:1]
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state_in = tf.contrib.rnn.LSTMStateTuple(self.c_in, self.h_in)
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lstm_outputs, lstm_state = tf.nn.dynamic_rnn(
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lstm_cell, rnn_in, initial_state=state_in, sequence_length=step_size, time_major=False)
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lstm_c, lstm_h = lstm_state
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self.state_out = (lstm_c[:1, :], lstm_h[:1, :])
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self.output = tf.reshape(lstm_outputs, [-1, 256])
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class FC_Embedder(MiddlewareEmbedder):
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def _build_module(self):
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width = 512 if self.embedder_width == EmbedderWidth.Wide else 64
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self.output = tf.layers.dense(self.input, width, activation=self.activation_function, name='fc1')
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