keras在训练时，报输入数据维度错误的问题
Error when checking model target: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 2 array(s), but instead got the following list of 1 arrays
1.先检查输入的维度是否有误使用shape查看输入的数据维度和定义的模型维度是否相等
2.当维度无错误时，请检查是否时多输入或多输出例如，当有多个输入X1, X2,…,Xn 或多个输出Y1，Y2，… ,Yn时
应当将所有的输入(出)放在一个列表中
model.fit([X1, X2,…,Xn], [Y1，Y2，… ,Yn])
validation_data按同样的操作
我跑的是DAE，单输入，双输出，所以将输出写在一个列表中
def DAE(sizes):
features = sizes[0]
classes = sizes[-1]
input_tensor = layers.Input([features, ], name="input")
x = layers.Dropout(0.2)(input_tensor)
for i in range(1, len(sizes)-1):
x = layers.Dense(units=sizes[i], activation=activations.relu)(x)
out1 = layers.Dense(units=features, activation=activations.relu, name="decode")(x)
out2 = layers.Dense(units=classes, activation=activations.softmax, name="class")(x)
model = Model(input_tensor, [out1, out2])
return model
dae_model = DAE(sizes)
dae_model.compile(optimizer=optimizers.adam(),
loss={"decode":losses.mse, "class":losses.categorical_crossentropy},
loss_weights={"decode":lambda1, "class":lambda2},
metrics=["accuracy"])
dae_model.summary()
dae_model.fit(x_train_2_axis, [x_train_2_axis, y_train_2_axis],
batch_size=32, epochs=10,
validation_data=(x_test_2_axis, [x_test_2_axis, y_test_2_axis]))
