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)) ])