提交 2c34d0c7 authored 作者: Frederic Bastien's avatar Frederic Bastien

pep8

上级 e0825746
...@@ -35,7 +35,8 @@ class TestConv2d(unittest.TestCase): ...@@ -35,7 +35,8 @@ class TestConv2d(unittest.TestCase):
self.border_modes = ["valid", "full", (0, 0), (1, 1), (5, 5), (5, 2)] self.border_modes = ["valid", "full", (0, 0), (1, 1), (5, 5), (5, 2)]
self.filter_flip = [True, False] self.filter_flip = [True, False]
def get_output_shape(self, inputs_shape, filters_shape, subsample, border_mode): def get_output_shape(self, inputs_shape, filters_shape,
subsample, border_mode):
if border_mode == "valid": if border_mode == "valid":
border_mode = (0, 0) border_mode = (0, 0)
if border_mode == "full": if border_mode == "full":
...@@ -139,8 +140,10 @@ class TestConv2d(unittest.TestCase): ...@@ -139,8 +140,10 @@ class TestConv2d(unittest.TestCase):
utt.verify_grad(abstract_conv2d_gradweight, [inputs_val, output_val], utt.verify_grad(abstract_conv2d_gradweight, [inputs_val, output_val],
mode=mode, eps=1) mode=mode, eps=1)
def run_gradinput(self, inputs_shape, filters_shape, output_shape, ref=dnn_gradinput, def run_gradinput(self, inputs_shape, filters_shape,
subsample=(1, 1), filter_flip=True, verify_grad=True, mode=mode_without_gpu, output_shape, ref=dnn_gradinput,
subsample=(1, 1), filter_flip=True,
verify_grad=True, mode=mode_without_gpu,
border_mode='valid', device='cpu', provide_shape=False): border_mode='valid', device='cpu', provide_shape=False):
output_val = numpy.random.random(output_shape).astype('float32') output_val = numpy.random.random(output_shape).astype('float32')
......
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