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Merge pull request #61 from ahundt/pep8

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test_convolutional.py pep8 compliance
This commit is contained in:
Thomas Boquet
2017-04-01 09:51:13 -04:00
committed by GitHub
parent fcd054fe40 116626c695
commit 2b77a35fb0
1 changed files with 38 additions and 17 deletions
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tests/keras_contrib/layers/test_convolutional.py
+38 -17
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@@ -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},