def test_convolution_3d():
nb_samples = 2
nb_filter = 2
stack_size = 3
kernel_dim1 = 2
kernel_dim2 = 3
kernel_dim3 = 1
input_len_dim1 = 10
input_len_dim2 = 11
input_len_dim3 = 12
for border_mode in _convolution_border_modes:
for subsample in [(1, 1, 1), (2, 2, 2)]:
if border_mode == 'same' and subsample != (1, 1, 1):
continue
layer_test(convolutional.Convolution3D,
kwargs={'nb_filter': nb_filter,
'kernel_dim1': kernel_dim1,
'kernel_dim2': kernel_dim2,
'kernel_dim3': kernel_dim3,
'border_mode': border_mode,
'subsample': subsample},
input_shape=(nb_samples,
input_len_dim1, input_len_dim2, input_len_dim3,
stack_size))
layer_test(convolutional.Convolution3D,
kwargs={'nb_filter': nb_filter,
'kernel_dim1': kernel_dim1,
'kernel_dim2': kernel_dim2,
'kernel_dim3': kernel_dim3,
'border_mode': border_mode,
'W_regularizer': 'l2',
'b_regularizer': 'l2',
'activity_regularizer': 'activity_l2',
'subsample': subsample},
input_shape=(nb_samples,
input_len_dim1, input_len_dim2, input_len_dim3,
stack_size))
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