From 116626c69576e41a2baece33e2cc3a9a762f4299 Mon Sep 17 00:00:00 2001 From: Andrew Hundt Date: Thu, 30 Mar 2017 22:39:42 -0400 Subject: [PATCH] test_convolutional.py pep8 compliance --- .../layers/test_convolutional.py | 55 +++++++++++++------ 1 file changed, 38 insertions(+), 17 deletions(-) diff --git a/tests/keras_contrib/layers/test_convolutional.py b/tests/keras_contrib/layers/test_convolutional.py index 0019a16..509a32c 100644 --- a/tests/keras_contrib/layers/test_convolutional.py +++ b/tests/keras_contrib/layers/test_convolutional.py @@ -32,43 +32,57 @@ def test_deconvolution_3d(): if border_mode == 'same' and subsample != (1, 1, 1): continue - dim1 = conv_input_length(kernel_dim1, 7, border_mode, subsample[0]) - dim2 = conv_input_length(kernel_dim2, 5, border_mode, subsample[1]) - dim3 = conv_input_length(kernel_dim3, 3, border_mode, subsample[2]) + dim1 = conv_input_length(kernel_dim1, 7, + border_mode, + subsample[0]) + dim2 = conv_input_length(kernel_dim2, 5, + border_mode, + subsample[1]) + dim3 = conv_input_length(kernel_dim3, 3, + border_mode, + subsample[2]) layer_test(convolutional.Deconvolution3D, kwargs={'filters': nbias_filter, 'kernel_size': (7, 5, 3), - 'output_shape': (batch_size, nbias_filter, dim1, dim2, dim3), + 'output_shape': (batch_size, nbias_filter, + dim1, dim2, dim3), 'padding': border_mode, 'strides': subsample, 'data_format': 'channels_first'}, - input_shape=(nbias_samples, stack_size, kernel_dim1, kernel_dim2, kernel_dim3), + input_shape=(nbias_samples, stack_size, + kernel_dim1, kernel_dim2, kernel_dim3), fixed_batch_size=True, tolerance=None) layer_test(convolutional.Deconvolution3D, kwargs={'filters': nbias_filter, 'kernel_size': (7, 5, 3), - 'output_shape': (batch_size, nbias_filter, dim1, dim2, dim3), + 'output_shape': (batch_size, nbias_filter, + dim1, dim2, dim3), 'padding': border_mode, 'strides': subsample, 'data_format': 'channels_first', 'kernel_regularizer': 'l2', 'bias_regularizer': 'l2', 'activity_regularizer': 'l2'}, - input_shape=(nbias_samples, stack_size, kernel_dim1, kernel_dim2, kernel_dim3), + input_shape=(nbias_samples, stack_size, + kernel_dim1, kernel_dim2, kernel_dim3), fixed_batch_size=True, tolerance=None) layer_test(convolutional.Deconvolution3D, kwargs={'filters': nbias_filter, 'kernel_size': (7, 5, 3), - 'output_shape': (nbias_filter, dim1, dim2, dim3), + 'output_shape': (nbias_filter, dim1, + dim2, dim3), 'padding': border_mode, 'strides': subsample, 'data_format': 'channels_first', 'kernel_regularizer': 'l2', 'bias_regularizer': 'l2', 'activity_regularizer': 'l2'}, - input_shape=(nbias_samples, stack_size, kernel_dim1, kernel_dim2, kernel_dim3), tolerance=None) + input_shape=(nbias_samples, stack_size, + kernel_dim1, + kernel_dim2, kernel_dim3), + tolerance=None) @keras_test @@ -97,8 +111,8 @@ def test_cosineconvolution_2d(): 'strides': subsample, 'use_bias': use_bias_mode, 'data_format': data_format}, - input_shape=(nbias_samples, nbias_row, nbias_col, stack_size)) - + input_shape=(nbias_samples, nbias_row, + nbias_col, stack_size)) layer_test(convolutional.CosineConvolution2D, kwargs={'filters': nbias_filter, @@ -110,8 +124,8 @@ def test_cosineconvolution_2d(): 'kernel_regularizer': 'l2', 'bias_regularizer': 'l2', 'activity_regularizer': 'l2'}, - input_shape=(nbias_samples, nbias_row, nbias_col, stack_size)) - + input_shape=(nbias_samples, nbias_row, + nbias_col, stack_size)) if data_format == 'channels_first': X = np.random.randn(1, 3, 5, 5) @@ -123,7 +137,9 @@ def test_cosineconvolution_2d(): W0 = X[0, :, :, :, None] model = Sequential() - model.add(convolutional.CosineConvolution2D(1, (5, 5), use_bias=True, input_shape=input_dim, data_format=data_format)) + model.add(convolutional.CosineConvolution2D(1, (5, 5), use_bias=True, + input_shape=input_dim, + data_format=data_format)) model.compile(loss='mse', optimizer='rmsprop') W = model.get_weights() W[0] = W0 @@ -133,7 +149,9 @@ def test_cosineconvolution_2d(): assert_allclose(out, np.ones((1, 1, 1, 1), dtype=K.floatx()), atol=1e-5) model = Sequential() - model.add(convolutional.CosineConvolution2D(1, (5, 5), use_bias=False, input_shape=input_dim, data_format=data_format)) + model.add(convolutional.CosineConvolution2D(1, (5, 5), use_bias=False, + input_shape=input_dim, + data_format=data_format)) model.compile(loss='mse', optimizer='rmsprop') W = model.get_weights() W[0] = -2 * W0 @@ -149,13 +167,16 @@ def test_subias_pixel_upscaling(): nbias_col = 16 for scale_factor in [2, 3, 4]: - input_data = np.random.random((nbias_samples, 4 * (scale_factor ** 2), nbias_row, nbias_col)) + input_data = np.random.random((nbias_samples, + 4 * (scale_factor ** 2), + nbias_row, nbias_col)) if K.image_data_format() == 'channels_last': input_data = input_data.transpose((0, 2, 3, 1)) input_tensor = K.variable(input_data) - expected_output = K.eval(KC.depth_to_space(input_tensor, scale=scale_factor)) + expected_output = K.eval(KC.depth_to_space(input_tensor, + scale=scale_factor)) layer_test(convolutional.SubPixelUpscaling, kwargs={'scale_factor': scale_factor},