def test_separable_conv_1d_invalid():
filters = 6
padding = 'valid'
with pytest.raises(ValueError):
model = Sequential([convolutional.SeparableConv1D(
filters=filters, kernel_size=3, padding=padding,
batch_input_shape=(None, 5, None))])
def test_separable_conv_1d():
num_samples = 2
filters = 6
stack_size = 3
num_step = 9
for padding in _convolution_paddings:
for strides in [1, 2]:
for multiplier in [1, 2]:
for dilation_rate in [1, 2]:
if padding == 'same' and strides != 1:
continue
if dilation_rate != 1 and strides != 1:
continue
if dilation_rate != 1 and K.backend() == 'cntk':
continue
layer_test(convolutional.SeparableConv1D,
kwargs={
'filters': filters,
'kernel_size': 3,
'padding': padding,
'strides': strides,
'depth_multiplier': multiplier,
'dilation_rate': dilation_rate
},
input_shape=(num_samples, num_step, stack_size))
layer_test(convolutional.SeparableConv1D,
kwargs={
'filters': filters,
'kernel_size': 3,
'padding': padding,
'data_format': 'channels_first',
'activation': None,
'depthwise_regularizer': 'l2',
'pointwise_regularizer': 'l2',
'bias_regularizer': 'l2',
'activity_regularizer': 'l2',
'pointwise_constraint': 'unit_norm',
'depthwise_constraint': 'unit_norm',
'strides': 1,
'use_bias': True,
'depth_multiplier': multiplier
},
input_shape=(num_samples, stack_size, num_step))
# Test invalid use case
with pytest.raises(ValueError):
model = Sequential([
convolutional.SeparableConv1D(filters=filters,
kernel_size=3,
padding=padding,
batch_input_shape=(None, 5, None))
])
