Tensorflow Addons R2 ValueError: Dimension 0 in both shapes must be equal, but are 1 and 5

Question

我一直在尝试将 tfa 指标添加到我的模型编译中，以便在整个培训过程中进行跟踪。但是，当我添加 R2 指标时，出现以下错误。我以为 y_shape=(1,) 会解决这个问题，但事实并非如此。

    ValueError: Dimension 0 in both shapes must be equal, but are 1 and 5. Shapes are [1] and [5]. for '{{node AssignAddVariableOp_8}} = AssignAddVariableOp[dtype=DT_FLOAT](AssignAddVariableOp_8/resource, Sum_6)' with input shapes: [], [5].

我的代码如下所示：

    model = Sequential()
    model.add(Input(shape=(4,)))
    model.add(Normalization())
    model.add(Dense(5, activation="relu", kernel_regularizer=l2(l2=1e-2)))
    print(model.summary())

    opt = Adam(learning_rate = 1e-2)
    model.compile(loss="mean_squared_error", optimizer=tf.keras.optimizers.Adam(learning_rate=1e-2), metrics=[MeanSquaredError(name="mse"), MeanAbsoluteError(name="mae"), tfa.metrics.RSquare(name="R2", y_shape=(1,))])

    history = model.fit(x = training_x,
                        y = training_y,
                        epochs = 10,
                        batch_size = 64,
                        validation_data = (validation_x, validation_y)
                        )

非常感谢任何帮助！请注意，我还尝试将 y_shape 更改为 (5,)，但随后出现错误，即尺寸不相等，而是 5 和 1...

Answer 1

您需要向您的模型添加一个输出层，如下所示：

model.add(Dense(1))

那么您的模型将如下所示：

model = Sequential()
model.add(Input(shape=(4,)))
model.add(Normalization())
model.add(Dense(5, activation="relu", kernel_regularizer=regularizers.l2(l2=1e-2)))
model.add(Dense(1))
print(model.summary())

输出：

Model: "sequential_10"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 normalization_10 (Normaliza  (None, 4)                9         
 tion)                                                           
                                                                 
 dense_12 (Dense)            (None, 5)                 25        
                                                                 
 dense_13 (Dense)            (None, 1)                 6         
                                                                 
=================================================================
Total params: 40
Trainable params: 31
Non-trainable params: 9

Tensorflow Addons R2 ValueError: Dimension 0 in both shapes must be equal, but are 1 and 5

Tensorflow Addons R2 ValueError: Dimension 0 in both shapes must be equal, but are 1 and 5

python

tensorflow

keras

valueerror

tensorflow-addons