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fixing the dropout rate code (#72)

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addresses issue #53
This commit is contained in:
Itai Caspi
2018-11-08 16:53:47 +02:00
committed by GitHub
parent 389c65cbbe
commit 3a0a1159e9
11 changed files with 33 additions and 33 deletions
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7
rl_coach/architectures/tensorflow_components/embedders/embedder.py
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@@ -34,15 +34,14 @@ class InputEmbedder(object):
can be multiple embedders in a single network
"""
def __init__(self, input_size: List[int], activation_function=tf.nn.relu,
scheme: EmbedderScheme=None, batchnorm: bool=False, dropout: bool=False,
scheme: EmbedderScheme=None, batchnorm: bool=False, dropout_rate: float=0.0,
name: str= "embedder", input_rescaling=1.0, input_offset=0.0, input_clipping=None, dense_layer=Dense,
is_training=False):
self.name = name
self.input_size = input_size
self.activation_function = activation_function
self.batchnorm = batchnorm
self.dropout = dropout
self.dropout_rate = 0
self.dropout_rate = dropout_rate
self.input = None
self.output = None
self.scheme = scheme
@@ -68,7 +67,7 @@ class InputEmbedder(object):
# we allow adding batchnorm, dropout or activation functions after each layer.
# The motivation is to simplify the transition between a network with batchnorm and a network without
# batchnorm to a single flag (the same applies to activation function and dropout)
if self.batchnorm or self.activation_function or self.dropout:
if self.batchnorm or self.activation_function or self.dropout_rate > 0:
for layer_idx in reversed(range(len(self.layers_params))):
self.layers_params.insert(layer_idx+1,
BatchnormActivationDropout(batchnorm=self.batchnorm,