本文整理汇总了Python中keras.utils.layer_utils.convert_all_kernels_in_model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_all_kernels_in_model方法的具体用法？Python layer_utils.convert_all_kernels_in_model怎么用？Python layer_utils.convert_all_kernels_in_model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_all_kernels_in_model方法的2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: __init__
# 需要导入模块: from keras.utils import layer_utils [as 别名]
# 或者: from keras.utils.layer_utils import convert_all_kernels_in_model [as 别名]
def __init__(self, inputs, blocks, weights=None,
trainable=True, name='encoder'):
inverse_pyramid = []
# convolutional block
conv_blocks = blocks[:-1]
for i, block in enumerate(conv_blocks):
if i == 0:
x = block(inputs)
inverse_pyramid.append(x)
elif i < len(conv_blocks) - 1:
x = block(x)
inverse_pyramid.append(x)
else:
x = block(x)
# fully convolutional block
fc_block = blocks[-1]
y = fc_block(x)
inverse_pyramid.append(y)
outputs = list(reversed(inverse_pyramid))
super(Encoder, self).__init__(
inputs=inputs, outputs=outputs)
# load pre-trained weights
if weights is not None:
weights_path = get_file(
'{}_weights_tf_dim_ordering_tf_kernels.h5'.format(name),
weights,
cache_subdir='models')
layer_names = load_weights(self, weights_path)
if K.image_data_format() == 'channels_first':
layer_utils.convert_all_kernels_in_model(self)
# Freezing basenet weights
if trainable is False:
for layer in self.layers:
if layer.name in layer_names:
layer.trainable = False 示例2: 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_all_kernels_in_model [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)