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Move embedder, middleware, and head parameters to framework agnostic modules. (#45)

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Part of #28
This commit is contained in:
Sina Afrooze
2018-10-29 14:46:40 -07:00
committed by Scott Leishman
parent 16b3e99f37
commit a888226641
60 changed files with 410 additions and 330 deletions
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4
rl_coach/architectures/tensorflow_components/middlewares/__init__.py
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@@ -0,0 +1,4 @@
from .fc_middleware import FCMiddleware
from .lstm_middleware import LSTMMiddleware
__all__ = ["FCMiddleware", "LSTMMiddleware"]

12
rl_coach/architectures/tensorflow_components/middlewares/fc_middleware.py
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@@ -18,22 +18,12 @@ from typing import Union, List
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.layers import batchnorm_activation_dropout, Dense
from rl_coach.architectures.tensorflow_components.middlewares.middleware import Middleware, MiddlewareParameters
from rl_coach.architectures.tensorflow_components.middlewares.middleware import Middleware
from rl_coach.base_parameters import MiddlewareScheme
from rl_coach.core_types import Middleware_FC_Embedding
from rl_coach.utils import force_list
class FCMiddlewareParameters(MiddlewareParameters):
def __init__(self, activation_function='relu',
scheme: Union[List, MiddlewareScheme] = MiddlewareScheme.Medium,
batchnorm: bool = False, dropout: bool = False,
name="middleware_fc_embedder", dense_layer=Dense, is_training=False):
super().__init__(parameterized_class=FCMiddleware, activation_function=activation_function,
scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name, dense_layer=dense_layer,
is_training=is_training)
class FCMiddleware(Middleware):
def __init__(self, activation_function=tf.nn.relu,
scheme: MiddlewareScheme = MiddlewareScheme.Medium,

13
rl_coach/architectures/tensorflow_components/middlewares/lstm_middleware.py
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@@ -19,23 +19,12 @@ import numpy as np
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.layers import batchnorm_activation_dropout, Dense
from rl_coach.architectures.tensorflow_components.middlewares.middleware import Middleware, MiddlewareParameters
from rl_coach.architectures.tensorflow_components.middlewares.middleware import Middleware
from rl_coach.base_parameters import MiddlewareScheme
from rl_coach.core_types import Middleware_LSTM_Embedding
from rl_coach.utils import force_list
class LSTMMiddlewareParameters(MiddlewareParameters):
def __init__(self, activation_function='relu', number_of_lstm_cells=256,
scheme: MiddlewareScheme = MiddlewareScheme.Medium,
batchnorm: bool = False, dropout: bool = False,
name="middleware_lstm_embedder", dense_layer=Dense, is_training=False):
super().__init__(parameterized_class=LSTMMiddleware, activation_function=activation_function,
scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name, dense_layer=dense_layer,
is_training=is_training)
self.number_of_lstm_cells = number_of_lstm_cells
class LSTMMiddleware(Middleware):
def __init__(self, activation_function=tf.nn.relu, number_of_lstm_cells: int=256,
scheme: MiddlewareScheme = MiddlewareScheme.Medium,

17
rl_coach/architectures/tensorflow_components/middlewares/middleware.py
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@@ -14,7 +14,6 @@
# limitations under the License.
#
import copy
from typing import Type, Union, List
import tensorflow as tf
@@ -23,20 +22,6 @@ from rl_coach.base_parameters import MiddlewareScheme, NetworkComponentParameter
from rl_coach.core_types import MiddlewareEmbedding
class MiddlewareParameters(NetworkComponentParameters):
def __init__(self, parameterized_class: Type['Middleware'],
activation_function: str='relu', scheme: Union[List, MiddlewareScheme]=MiddlewareScheme.Medium,
batchnorm: bool=False, dropout: bool=False, name='middleware', dense_layer=Dense, is_training=False):
super().__init__(dense_layer=dense_layer)
self.activation_function = activation_function
self.scheme = scheme
self.batchnorm = batchnorm
self.dropout = dropout
self.name = name
self.is_training = is_training
self.parameterized_class_name = parameterized_class.__name__
class Middleware(object):
"""
A middleware embedder is the middle part of the network. It takes the embeddings from the input embedders,
@@ -57,6 +42,8 @@ class Middleware(object):
self.scheme = scheme
self.return_type = MiddlewareEmbedding
self.dense_layer = dense_layer
if self.dense_layer is None:
self.dense_layer = Dense
self.is_training = is_training
# layers order is conv -> batchnorm -> activation -> dropout