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Add Flatten layer to architectures + make flatten optional in embedders (#483)

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Flatten layer required for embedders that mix conv and dense
(Cherry picking from #478)
This commit is contained in:
Guy Jacob
2021-05-12 11:11:10 +03:00
committed by GitHub
parent c369984c2e
commit 235a259223
7 changed files with 49 additions and 10 deletions
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5
rl_coach/architectures/tensorflow_components/embedders/image_embedder.py
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@@ -34,9 +34,10 @@ class ImageEmbedder(InputEmbedder):
def __init__(self, input_size: List[int], activation_function=tf.nn.relu,
scheme: EmbedderScheme=EmbedderScheme.Medium, batchnorm: bool=False, dropout_rate: float=0.0,
name: str= "embedder", input_rescaling: float=255.0, input_offset: float=0.0, input_clipping=None,
dense_layer=Dense, is_training=False):
dense_layer=Dense, is_training=False, flatten=True):
super().__init__(input_size, activation_function, scheme, batchnorm, dropout_rate, name, input_rescaling,
input_offset, input_clipping, dense_layer=dense_layer, is_training=is_training)
input_offset, input_clipping, dense_layer=dense_layer, is_training=is_training,
flatten=flatten)
self.return_type = InputImageEmbedding
if len(input_size) != 3 and scheme != EmbedderScheme.Empty:
raise ValueError("Image embedders expect the input size to have 3 dimensions. The given size is: {}"