我的网络规格如下：
Layer (type) Output Shape Param #
input_38 (InputLayer) (None, 474) 0
dense_149 (Dense) (None, 120) 57000
dense_150 (Dense) (None, 120) 14520
dropout_38 (Dropout) (None, 120) 0
dense_151 (Dense) (None, 120) 14520
dense_152 (Dense) (None, 1) 121
现在，我正在训练模型并使用以下方法保存前2个密集层的权重：
reshaped_weights1 = model.layers[1].get_weights()
reshaped_weights2 = model.layers[2].get_weights()
并尝试在具有与上述相同结构的另一个模型（微调）中使用这些权重，但是使用用于训练第一个模型的数据的子集 . 该模型如下所示：
def createModelHelper1(dropoutRate = 0.0, numNeurons=40, optimizer = 'adam', numNeuronsFirstTwo=40):
inputLayer = Input(shape=(data.shape[1],))
denseLayer1 = Dense(numNeuronsFirstTwo, kernel_regularizer=l2(0.001))(inputLayer)
denseLayer2 = Dense(numNeuronsFirstTwo, kernel_regularizer=l2(0.001))(denseLayer1)
dropoutLayer = Dropout(dropoutRate)(denseLayer2)
denseLayer3 = Dense(numNeurons, kernel_regularizer=l2(0.001))(dropoutLayer)
outputLayer = Dense(1, activation='sigmoid')(denseLayer3)
model = Model(input=inputLayer, output=outputLayer)
model.layers[1].set_weights(reshaped_weights1) #ERROR
model.layers[1].trainable = False #freezing the layer
model.layers[2].set_weights(reshaped_weights2)
model.layers[2].trainable = False #freezing the layer
model.compile(loss='binary_crossentropy', optimizer=optimizer)
return model
我收到错误声明：
The weight shape (474,60) not compatible with provided weight shape (474, 120).
我在这做错了什么？两种型号的结构完全相同？非常感谢提前！