def test_convolution_1d(self):
nb_samples = 9
nb_steps = 7
input_dim = 10
filter_length = 6
nb_filter = 5
weights_in = [np.ones((nb_filter, input_dim, filter_length, 1)), np.ones(nb_filter)]
input = np.ones((nb_samples, nb_steps, input_dim))
for weight in [None, weights_in]:
for border_mode in ['valid', 'full', 'same']:
for subsample_length in [1, 3]:
if border_mode == 'same' and subsample_length != 1:
continue
for W_regularizer in [None, 'l2']:
for b_regularizer in [None, 'l2']:
for act_regularizer in [None, 'l2']:
layer = convolutional.Convolution1D(
nb_filter, filter_length, weights=weight,
border_mode=border_mode, W_regularizer=W_regularizer,
b_regularizer=b_regularizer, activity_regularizer=act_regularizer,
subsample_length=subsample_length, input_shape=(None, input_dim))
layer.input = theano.shared(value=input)
for train in [True, False]:
out = layer.get_output(train).eval()
assert input.shape[0] == out.shape[0]
if border_mode == 'same' and subsample_length == 1:
assert input.shape[1] == out.shape[1]
config = layer.get_config()
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