Traceback:
model = Model(input_tensor,x,name = 'vgg16_trunk')
File "/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py", line 91, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/keras/engine/network.py", line 93, in __init__
self._init_graph_network(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/keras/engine/network.py", line 231, in _init_graph_network
self.inputs, self.outputs)
File "/usr/local/lib/python3.6/dist-packages/keras/engine/network.py", line 1443, in _map_graph_network
str(layers_with_complete_input))
ValueError: Graph disconnected: cannot obtain value for tensor Tensor("input_2:0", shape=(?, 32, 32, 3), dtype=float32) at layer "input_2". The following previous layers were accessed without issue: []
如何在 vgg16 中解决这个问题？
def create_model(input_shape):
channel_axis = 1 if K.image_data_format() == "channels_first" else -1
input_tensor = Input(shape=input_shape)
base_model = VGG16(classes=10,input_tensor=None,input_shape=input_shape,include_top=False)
x = base_model.output
x = BatchNormalization(axis=channel_axis, momentum=mom,
epsilon=eps, gamma_initializer=gamma)(x)
x = LeakyReLU(leakiness)(x)
model = Model(input_tensor,x,name = 'vgg16_trunk')
return model
传递您在此处创建的input_tensor：
input_tensor = 输入(形状=input_shape(
创建base_model的位置：
base_model = VGG16(类=10，input_tensor=input_tensor，include_top=假(
另请注意，张量已经具有input_shape，因此在创建base_model时无需再次将其作为参数。