# # 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. # 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. # from architectures.neon_components.embedders import * from architectures.neon_components.heads import * from architectures.neon_components.middleware import * from architectures.neon_components.architecture import * from configurations import InputTypes, OutputTypes, MiddlewareTypes class GeneralNeonNetwork(NeonArchitecture): def __init__(self, tuning_parameters, name="", global_network=None, network_is_local=True): self.global_network = global_network self.network_is_local = network_is_local self.num_heads_per_network = 1 if tuning_parameters.agent.use_separate_networks_per_head else \ len(tuning_parameters.agent.output_types) self.num_networks = 1 if not tuning_parameters.agent.use_separate_networks_per_head else \ len(tuning_parameters.agent.output_types) self.input_embedders = [] self.output_heads = [] self.activation_function = self.get_activation_function( tuning_parameters.agent.hidden_layers_activation_function) NeonArchitecture.__init__(self, tuning_parameters, name, global_network, network_is_local) def get_activation_function(self, activation_function_string): activation_functions = { 'relu': neon.Rectlin(), 'tanh': neon.Tanh(), 'sigmoid': neon.Logistic(), 'elu': neon.Explin(), 'selu': None, 'none': None } assert activation_function_string in activation_functions.keys(), \ "Activation function must be one of the following {}".format(activation_functions.keys()) return activation_functions[activation_function_string] def get_input_embedder(self, embedder_type): # the observation can be either an image or a vector def get_observation_embedding(with_timestep=False): if self.input_height > 1: return ImageEmbedder((self.input_depth, self.input_height, self.input_width), self.batch_size, name="observation") else: return VectorEmbedder((self.input_depth, self.input_width + int(with_timestep)), self.batch_size, name="observation") input_mapping = { InputTypes.Observation: get_observation_embedding(), InputTypes.Measurements: VectorEmbedder(self.measurements_size, self.batch_size, name="measurements"), InputTypes.GoalVector: VectorEmbedder(self.measurements_size, self.batch_size, name="goal_vector"), InputTypes.Action: VectorEmbedder((self.num_actions,), self.batch_size, name="action"), InputTypes.TimedObservation: get_observation_embedding(with_timestep=True), } return input_mapping[embedder_type] def get_middleware_embedder(self, middleware_type): return {MiddlewareTypes.LSTM: None, # LSTM over Neon is currently not supported in Coach MiddlewareTypes.FC: FC_Embedder}.get(middleware_type)(self.activation_function) def get_output_head(self, head_type, head_idx, loss_weight=1.): output_mapping = { OutputTypes.Q: QHead, OutputTypes.DuelingQ: DuelingQHead, OutputTypes.V: None, # Policy Optimization algorithms over Neon are currently not supported in Coach OutputTypes.Pi: None, # Policy Optimization algorithms over Neon are currently not supported in Coach OutputTypes.MeasurementsPrediction: None, # DFP over Neon is currently not supported in Coach OutputTypes.DNDQ: None, # NEC over Neon is currently not supported in Coach OutputTypes.NAF: None, # NAF over Neon is currently not supported in Coach OutputTypes.PPO: None, # PPO over Neon is currently not supported in Coach OutputTypes.PPO_V: None # PPO over Neon is currently not supported in Coach } return output_mapping[head_type](self.tp, head_idx, loss_weight, self.network_is_local) def get_model(self, tuning_parameters): """ :param tuning_parameters: A Preset class instance with all the running paramaters :type tuning_parameters: Preset :return: A model """ assert len(self.tp.agent.input_types) > 0, "At least one input type should be defined" assert len(self.tp.agent.output_types) > 0, "At least one output type should be defined" assert self.tp.agent.middleware_type is not None, "Exactly one middleware type should be defined" assert len(self.tp.agent.loss_weights) > 0, "At least one loss weight should be defined" assert len(self.tp.agent.output_types) == len(self.tp.agent.loss_weights), \ "Number of loss weights should match the number of output types" local_network_in_distributed_training = self.global_network is not None and self.network_is_local tuning_parameters.activation_function = self.activation_function done_creating_input_placeholders = False for network_idx in range(self.num_networks): with name_scope('network_{}'.format(network_idx)): #################### # Input Embeddings # #################### state_embedding = [] for idx, input_type in enumerate(self.tp.agent.input_types): # get the class of the input embedder self.input_embedders.append(self.get_input_embedder(input_type)) # in the case each head uses a different network, we still reuse the input placeholders prev_network_input_placeholder = self.inputs[idx] if done_creating_input_placeholders else None # create the input embedder instance and store the input placeholder and the embedding input_placeholder, embedding = self.input_embedders[-1](prev_network_input_placeholder) if len(self.inputs) < len(self.tp.agent.input_types): self.inputs.append(input_placeholder) state_embedding.append(embedding) done_creating_input_placeholders = True ############## # Middleware # ############## state_embedding = ng.concat_along_axis(state_embedding, state_embedding[0].axes[0]) \ if len(state_embedding) > 1 else state_embedding[0] self.middleware_embedder = self.get_middleware_embedder(self.tp.agent.middleware_type) _, self.state_embedding = self.middleware_embedder(state_embedding) ################ # Output Heads # ################ for head_idx in range(self.num_heads_per_network): for head_copy_idx in range(self.tp.agent.num_output_head_copies): if self.tp.agent.use_separate_networks_per_head: # if we use separate networks per head, then the head type corresponds top the network idx head_type_idx = network_idx else: # if we use a single network with multiple heads, then the head type is the current head idx head_type_idx = head_idx self.output_heads.append(self.get_output_head(self.tp.agent.output_types[head_type_idx], head_copy_idx, self.tp.agent.loss_weights[head_type_idx])) if self.network_is_local: output, target_placeholder, input_placeholder = self.output_heads[-1](self.state_embedding) self.targets.extend(target_placeholder) else: output, input_placeholder = self.output_heads[-1](self.state_embedding) self.outputs.extend(output) self.inputs.extend(input_placeholder) # Losses self.losses = [] for output_head in self.output_heads: self.losses += output_head.loss self.total_loss = sum(self.losses) # Learning rate if self.tp.learning_rate_decay_rate != 0: raise Exception("learning rate decay is not supported in neon") # Optimizer if local_network_in_distributed_training and \ hasattr(self.tp.agent, "shared_optimizer") and self.tp.agent.shared_optimizer: # distributed training and this is the local network instantiation self.optimizer = self.global_network.optimizer else: if tuning_parameters.agent.optimizer_type == 'Adam': self.optimizer = neon.Adam( learning_rate=tuning_parameters.learning_rate, gradient_clip_norm=tuning_parameters.clip_gradients ) elif tuning_parameters.agent.optimizer_type == 'RMSProp': self.optimizer = neon.RMSProp( learning_rate=tuning_parameters.learning_rate, gradient_clip_norm=tuning_parameters.clip_gradients, decay_rate=0.9, epsilon=0.01 ) elif tuning_parameters.agent.optimizer_type == 'LBFGS': raise Exception("LBFGS optimizer is not supported in neon") else: raise Exception("{} is not a valid optimizer type".format(tuning_parameters.agent.optimizer_type))