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Support return_state parameter in ConvRecurrent2D #7407

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merged 13 commits into from
Jul 26, 2017
Merged

Support return_state parameter in ConvRecurrent2D #7407

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47c7e8a
Support return_state parameter in ConvRecurrent2D
ericwu09 Jul 23, 2017
d6b07d2
Fix under-indent error
ericwu09 Jul 24, 2017
729ecdc
Support for return_state parameter for ConvLSTM2D
ericwu09 Jul 24, 2017
fc89935
return_state unit test
ericwu09 Jul 24, 2017
c079da3
Fix PEP8 errors
ericwu09 Jul 24, 2017
035660f
Fix PEP8 errors
ericwu09 Jul 24, 2017
ae1c18b
Update convolutional_recurrent.py
ericwu09 Jul 25, 2017
aedc490
Fix TF functionality
ericwu09 Jul 25, 2017
24eefa1
Fix Python3.5 error
ericwu09 Jul 25, 2017
813c1f0
Formatting and comment changes
ericwu09 Jul 25, 2017
d46500c
Adding channels_first
ericwu09 Jul 25, 2017
472d953
runtime error
ericwu09 Jul 25, 2017
d0b9778
remove batch size check
ericwu09 Jul 25, 2017
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43 changes: 26 additions & 17 deletions keras/layers/convolutional_recurrent.py
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Original file line number Diff line number Diff line change
Expand Up @@ -129,16 +129,24 @@ def compute_output_shape(self, input_shape):
dilation=self.dilation_rate[1])
if self.return_sequences:
if self.data_format == 'channels_first':
return (input_shape[0], input_shape[1],
self.filters, rows, cols)
output_shape = (input_shape[0], input_shape[1],
self.filters, rows, cols)
elif self.data_format == 'channels_last':
return (input_shape[0], input_shape[1],
rows, cols, self.filters)
output_shape = (input_shape[0], input_shape[1],
rows, cols, self.filters)
else:
if self.data_format == 'channels_first':
return (input_shape[0], self.filters, rows, cols)
output_shape = (input_shape[0], self.filters, rows, cols)
elif self.data_format == 'channels_last':
return (input_shape[0], rows, cols, self.filters)
output_shape = (input_shape[0], rows, cols, self.filters)

if self.return_state:
if self.data_format == 'channels_first':
output_shape = [output_shape] + [(input_shape[0], self.filters, rows, cols) for _ in range(2)]
elif self.data_format == 'channels_last':
output_shape = [output_shape] + [(input_shape[0], rows, cols, self.filters) for _ in range(2)]

return output_shape

def get_config(self):
config = {'filters': self.filters,
Expand Down Expand Up @@ -429,24 +437,25 @@ def reset_states(self):
'input_shape must be provided '
'(including batch size). '
'Got input shape: ' + str(input_shape))

if self.return_sequences:
out_row, out_col, out_filter = output_shape[2:]
if self.return_state:
output_shape = output_shape[1]
else:
output_shape = (input_shape[0],) + output_shape[2:]
else:
out_row, out_col, out_filter = output_shape[1:]
if self.return_state:
output_shape = output_shape[1]
else:
output_shape = (input_shape[0],) + output_shape[1:]

if hasattr(self, 'states'):
K.set_value(self.states[0],
np.zeros((input_shape[0],
out_row, out_col, out_filter)))
np.zeros(output_shape))
K.set_value(self.states[1],
np.zeros((input_shape[0],
out_row, out_col, out_filter)))
np.zeros(output_shape))
else:
self.states = [K.zeros((input_shape[0],
out_row, out_col, out_filter)),
K.zeros((input_shape[0],
out_row, out_col, out_filter))]
self.states = [K.zeros(output_shape),
K.zeros(output_shape)]

def get_constants(self, inputs, training=None):
constants = []
Expand Down
19 changes: 19 additions & 0 deletions tests/keras/layers/convolutional_recurrent_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,25 @@ def test_convolutional_recurrent():
input_channel)

for return_sequences in [True, False]:

# test for return state:
input = Input(batch_shape=inputs.shape)
kwargs = {'data_format': data_format,
'return_sequences': return_sequences,
'return_state': True,
'stateful': True,
'filters': filters,
'kernel_size': (num_row, num_col),
'padding': 'valid'}
layer = convolutional_recurrent.ConvLSTM2D(**kwargs)
layer.build(inputs.shape)
outputs = layer(input)
output, states = outputs[0], outputs[1:]
assert len(states) == 2
model = Model(input, states[0])
state = model.predict(inputs)
np.testing.assert_allclose(K.eval(layer.states[0]), state, atol=1e-4)

# test for output shape:
output = layer_test(convolutional_recurrent.ConvLSTM2D,
kwargs={'data_format': data_format,
Expand Down