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update nec and value optimization agents to work with recurrent middleware

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Zach Dwiel
2017-11-03 13:58:42 -07:00
parent 93a54c7e8e
commit 6c79a442f2
12 changed files with 138 additions and 72 deletions
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23
architectures/tensorflow_components/architecture.py
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@@ -1,5 +1,5 @@
#
# Copyright (c) 2017 Intel Corporation
# Copyright (c) 2017 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -64,7 +64,7 @@ class TensorFlowArchitecture(Architecture):
trainable=False)
self.lock = self.lock_counter.assign_add(1, use_locking=True)
self.lock_init = self.lock_counter.assign(0)
self.release_counter = tf.get_variable("release_counter", [], tf.int32,
initializer=tf.constant_initializer(0, dtype=tf.int32),
trainable=False)
@@ -86,6 +86,7 @@ class TensorFlowArchitecture(Architecture):
tuning_parameters.clip_gradients)
# gradients of the outputs w.r.t. the inputs
# at the moment, this is only used by ddpg
if len(self.outputs) == 1:
self.gradients_wrt_inputs = [tf.gradients(self.outputs[0], input_ph) for input_ph in self.inputs]
self.gradients_weights_ph = tf.placeholder('float32', self.outputs[0].shape, 'output_gradient_weights')
@@ -126,7 +127,7 @@ class TensorFlowArchitecture(Architecture):
def accumulate_gradients(self, inputs, targets, additional_fetches=None):
"""
Runs a forward pass & backward pass, clips gradients if needed and accumulates them into the accumulation
Runs a forward pass & backward pass, clips gradients if needed and accumulates them into the accumulation
placeholders
:param additional_fetches: Optional tensors to fetch during gradients calculation
:param inputs: The input batch for the network
@@ -164,6 +165,7 @@ class TensorFlowArchitecture(Architecture):
# feed the lstm state if necessary
if self.tp.agent.middleware_type == MiddlewareTypes.LSTM:
# we can't always assume that we are starting from scratch here can we?
feed_dict[self.middleware_embedder.c_in] = self.middleware_embedder.c_init
feed_dict[self.middleware_embedder.h_in] = self.middleware_embedder.h_init
@@ -231,20 +233,27 @@ class TensorFlowArchitecture(Architecture):
while self.tp.sess.run(self.release_counter) % self.tp.num_threads != 0:
time.sleep(0.00001)
def predict(self, inputs):
def predict(self, inputs, outputs=None):
"""
Run a forward pass of the network using the given input
:param inputs: The input for the network
:param outputs: The output for the network, defaults to self.outputs
:return: The network output
WARNING: must only call once per state since each call is assumed by LSTM to be a new time step.
"""
feed_dict = dict(zip(self.inputs, force_list(inputs)))
if outputs is None:
outputs = self.outputs
if self.tp.agent.middleware_type == MiddlewareTypes.LSTM:
feed_dict[self.middleware_embedder.c_in] = self.curr_rnn_c_in
feed_dict[self.middleware_embedder.h_in] = self.curr_rnn_h_in
output, (self.curr_rnn_c_in, self.curr_rnn_h_in) = self.tp.sess.run([self.outputs, self.middleware_embedder.state_out], feed_dict=feed_dict)
output, (self.curr_rnn_c_in, self.curr_rnn_h_in) = self.tp.sess.run([outputs, self.middleware_embedder.state_out], feed_dict=feed_dict)
else:
output = self.tp.sess.run(self.outputs, feed_dict)
output = self.tp.sess.run(outputs, feed_dict)
return squeeze_list(output)
@@ -299,7 +308,7 @@ class TensorFlowArchitecture(Architecture):
def set_variable_value(self, assign_op, value, placeholder=None):
"""
Updates the value of a variable.
Updates the value of a variable.
This requires having an assign operation for the variable, and a placeholder which will provide the value
:param assign_op: an assign operation for the variable
:param value: a value to set the variable to