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Removed tensorflow specific code in presets (#59)

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* Add generic layer specification for using in presets

* Modify presets to use the generic scheme
This commit is contained in:
Sina Afrooze
2018-11-06 07:39:29 -08:00
committed by Gal Leibovich
parent 811152126c
commit 93571306c3
19 changed files with 233 additions and 101 deletions
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28
rl_coach/architectures/tensorflow_components/general_network.py
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@@ -24,6 +24,7 @@ from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
from rl_coach.architectures.head_parameters import HeadParameters
from rl_coach.architectures.middleware_parameters import MiddlewareParameters
from rl_coach.architectures.tensorflow_components.architecture import TensorFlowArchitecture
from rl_coach.architectures.tensorflow_components import utils
from rl_coach.base_parameters import AgentParameters, EmbeddingMergerType
from rl_coach.core_types import PredictionType
from rl_coach.spaces import SpacesDefinition, PlanarMapsObservationSpace
@@ -99,27 +100,6 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture):
return ret_dict
@staticmethod
def get_activation_function(activation_function_string: str):
"""
Map the activation function from a string to the tensorflow framework equivalent
:param activation_function_string: the type of the activation function
:return: the tensorflow activation function
"""
activation_functions = {
'relu': tf.nn.relu,
'tanh': tf.nn.tanh,
'sigmoid': tf.nn.sigmoid,
'elu': tf.nn.elu,
'selu': tf.nn.selu,
'leaky_relu': tf.nn.leaky_relu,
'none': None
}
assert activation_function_string in activation_functions.keys(), \
"Activation function must be one of the following {}. instead it was: {}"\
.format(activation_functions.keys(), activation_function_string)
return activation_functions[activation_function_string]
def get_input_embedder(self, input_name: str, embedder_params: InputEmbedderParameters):
"""
Given an input embedder parameters class, creates the input embedder and returns it
@@ -144,7 +124,7 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture):
embedder_path = 'rl_coach.architectures.tensorflow_components.embedders:' + mod_names[emb_type]
embedder_params_copy = copy.copy(embedder_params)
embedder_params_copy.activation_function = self.get_activation_function(embedder_params.activation_function)
embedder_params_copy.activation_function = utils.get_activation_function(embedder_params.activation_function)
embedder_params_copy.input_rescaling = embedder_params_copy.input_rescaling[emb_type]
embedder_params_copy.input_offset = embedder_params_copy.input_offset[emb_type]
embedder_params_copy.name = input_name
@@ -162,7 +142,7 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture):
mod_name = middleware_params.parameterized_class_name
middleware_path = 'rl_coach.architectures.tensorflow_components.middlewares:' + mod_name
middleware_params_copy = copy.copy(middleware_params)
middleware_params_copy.activation_function = self.get_activation_function(middleware_params.activation_function)
middleware_params_copy.activation_function = utils.get_activation_function(middleware_params.activation_function)
module = dynamic_import_and_instantiate_module_from_params(middleware_params_copy, path=middleware_path)
return module
@@ -176,7 +156,7 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture):
mod_name = head_params.parameterized_class_name
head_path = 'rl_coach.architectures.tensorflow_components.heads:' + mod_name
head_params_copy = copy.copy(head_params)
head_params_copy.activation_function = self.get_activation_function(head_params_copy.activation_function)
head_params_copy.activation_function = utils.get_activation_function(head_params_copy.activation_function)
return dynamic_import_and_instantiate_module_from_params(head_params_copy, path=head_path, extra_kwargs={
'agent_parameters': self.ap, 'spaces': self.spaces, 'network_name': self.network_wrapper_name,
'head_idx': head_idx, 'is_local': self.network_is_local})