def test_zero_padding_1d(): num_samples = 2 input_dim = 2 num_steps = 5 shape = (num_samples, num_steps, input_dim) inputs = np.ones(shape) # basic test layer_test(convolutional.ZeroPadding1D, kwargs={'padding': 2}, input_shape=inputs.shape) layer_test(convolutional.ZeroPadding1D, kwargs={'padding': (1, 2)}, input_shape=inputs.shape) # correctness test layer = convolutional.ZeroPadding1D(padding=2) layer.build(shape) outputs = layer(K.variable(inputs)) np_output = K.eval(outputs) for offset in [0, 1, -1, -2]: assert_allclose(np_output[:, offset, :], 0.) assert_allclose(np_output[:, 2:-2, :], 1.) layer = convolutional.ZeroPadding1D(padding=(1, 2)) layer.build(shape) outputs = layer(K.variable(inputs)) np_output = K.eval(outputs) for left_offset in [0]: assert_allclose(np_output[:, left_offset, :], 0.) for right_offset in [-1, -2]: assert_allclose(np_output[:, right_offset, :], 0.) assert_allclose(np_output[:, 1:-2, :], 1.) layer.get_config()
def test_zero_padding_1d(): nb_samples = 2 input_dim = 2 nb_steps = 5 input = np.ones((nb_samples, nb_steps, input_dim)) # basic test layer_test(convolutional.ZeroPadding1D, kwargs={'padding': 2}, input_shape=input.shape) layer_test(convolutional.ZeroPadding1D, kwargs={'padding': (1, 2)}, input_shape=input.shape) layer_test(convolutional.ZeroPadding1D, kwargs={'padding': { 'left_pad': 1, 'right_pad': 2 }}, input_shape=input.shape) # correctness test layer = convolutional.ZeroPadding1D(padding=2) layer.set_input(K.variable(input), shape=input.shape) out = K.eval(layer.output) for offset in [0, 1, -1, -2]: assert_allclose(out[:, offset, :], 0.) assert_allclose(out[:, 2:-2, :], 1.) layer = convolutional.ZeroPadding1D(padding=(1, 2)) layer.set_input(K.variable(input), shape=input.shape) out = K.eval(layer.output) for left_offset in [0]: assert_allclose(out[:, left_offset, :], 0.) for right_offset in [-1, -2]: assert_allclose(out[:, right_offset, :], 0.) assert_allclose(out[:, 1:-2, :], 1.) layer.get_config()