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114 lines
4.6 KiB
Python
114 lines
4.6 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 numpy as np
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import tensorflow as tf
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from rl_coach.architectures.tensorflow_components.architecture import batchnorm_activation_dropout
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from rl_coach.architectures.tensorflow_components.middlewares.middleware import Middleware, MiddlewareParameters
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from rl_coach.base_parameters import MiddlewareScheme
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from rl_coach.core_types import Middleware_LSTM_Embedding
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class LSTMMiddlewareParameters(MiddlewareParameters):
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def __init__(self, activation_function='relu', number_of_lstm_cells=256,
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scheme: MiddlewareScheme = MiddlewareScheme.Medium,
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batchnorm: bool = False, dropout: bool = False,
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name="middleware_lstm_embedder"):
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super().__init__(parameterized_class=LSTMMiddleware, activation_function=activation_function,
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scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name)
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self.number_of_lstm_cells = number_of_lstm_cells
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class LSTMMiddleware(Middleware):
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schemes = {
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MiddlewareScheme.Empty:
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[],
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# ppo
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MiddlewareScheme.Shallow:
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[
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[64]
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],
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# dqn
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MiddlewareScheme.Medium:
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[
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[512]
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],
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MiddlewareScheme.Deep: \
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[
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[128],
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[128],
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[128]
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]
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}
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def __init__(self, activation_function=tf.nn.relu, number_of_lstm_cells: int=256,
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scheme: MiddlewareScheme = MiddlewareScheme.Medium,
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batchnorm: bool = False, dropout: bool = False,
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name="middleware_lstm_embedder"):
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super().__init__(activation_function=activation_function, batchnorm=batchnorm,
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dropout=dropout, scheme=scheme, name=name)
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self.return_type = Middleware_LSTM_Embedding
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self.number_of_lstm_cells = number_of_lstm_cells
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self.layers = []
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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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self.layers.append(self.input)
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# optionally insert some dense layers before the LSTM
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if isinstance(self.scheme, MiddlewareScheme):
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layers_params = LSTMMiddleware.schemes[self.scheme]
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else:
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layers_params = self.scheme
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for idx, layer_params in enumerate(layers_params):
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self.layers.append(
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tf.layers.dense(self.layers[-1], layer_params[0], name='fc{}'.format(idx))
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)
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self.layers.extend(batchnorm_activation_dropout(self.layers[-1], self.batchnorm,
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self.activation_function, self.dropout,
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self.dropout_rate, idx))
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# add the LSTM layer
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lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(self.number_of_lstm_cells, 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(self.layers[-1], [0])
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step_size = tf.shape(self.layers[-1])[:1]
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state_in = tf.nn.rnn_cell.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, self.number_of_lstm_cells])
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