本文整理汇总了Python中keras.utils.layer_utils.convert_dense_weights_data_format方法的典型用法代码示例。如果您正苦于以下问题：Python layer_utils.convert_dense_weights_data_format方法的具体用法？Python layer_utils.convert_dense_weights_data_format怎么用？Python layer_utils.convert_dense_weights_data_format使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类keras.utils.layer_utils的用法示例。
在下文中一共展示了layer_utils.convert_dense_weights_data_format方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_convert_weights
# 需要导入模块: from keras.utils import layer_utils [as 别名]
# 或者: from keras.utils.layer_utils import convert_dense_weights_data_format [as 别名]
def test_convert_weights():
def get_model(shape, data_format):
model = Sequential()
model.add(Conv2D(filters=2,
kernel_size=(4, 3),
input_shape=shape,
data_format=data_format))
model.add(Flatten())
model.add(Dense(5))
return model
for data_format in ['channels_first', 'channels_last']:
if data_format == 'channels_first':
shape = (3, 5, 5)
target_shape = (5, 5, 3)
prev_shape = (2, 3, 2)
flip = lambda x: np.flip(np.flip(x, axis=2), axis=3)
transpose = lambda x: np.transpose(x, (0, 2, 3, 1))
target_data_format = 'channels_last'
elif data_format == 'channels_last':
shape = (5, 5, 3)
target_shape = (3, 5, 5)
prev_shape = (2, 2, 3)
flip = lambda x: np.flip(np.flip(x, axis=1), axis=2)
transpose = lambda x: np.transpose(x, (0, 3, 1, 2))
target_data_format = 'channels_first'
model1 = get_model(shape, data_format)
model2 = get_model(target_shape, target_data_format)
conv = K.function([model1.input], [model1.layers[0].output])
x = np.random.random((1,) + shape)
# Test equivalence of convert_all_kernels_in_model
convout1 = conv([x])[0]
layer_utils.convert_all_kernels_in_model(model1)
convout2 = flip(conv([flip(x)])[0])
assert_allclose(convout1, convout2, atol=1e-5)
# Test equivalence of convert_dense_weights_data_format
out1 = model1.predict(x)
layer_utils.convert_dense_weights_data_format(model1.layers[2], prev_shape, target_data_format)
for (src, dst) in zip(model1.layers, model2.layers):
dst.set_weights(src.get_weights())
out2 = model2.predict(transpose(x))
assert_allclose(out1, out2, atol=1e-5)