Question

我已经使用功能性API实现了Keras模型：

x_inp, x_out = graphsage_model.in_out_tensors()
prediction = layers.Dense(units=train_targets.shape[1], activation="softmax")(x_out)
model = Model(inputs=x_inp, outputs=prediction)
model.compile(
    optimizer=optimizers.Adam(lr=0.005),
    loss=losses.categorical_crossentropy,
    metrics=["acc"],
)

具有张量的是：

x_inp:  [<tf.Tensor 'input_1:0' shape=(None, 1, 1433) dtype=float32>, <tf.Tensor 'input_2:0' shape=(None, 10, 1433) dtype=float32>, <tf.Tensor 'input_3:0' shape=(None, 50, 1433) dtype=float32>]
x_out:  Tensor("lambda/Identity:0", shape=(None, 32), dtype=float32)
prediction:  Tensor("dense/Identity:0", shape=(None, 7), dtype=float32)
train_targets.shape[1] = 7

据我所知，在功能API方法中，我的模型在输入层有50个单位，在隐藏层有32个单位，在输出层有7个单位。为了了解Keras的顺序模型与功能性API的工作方式不同，我尝试用顺序方法来实现：

model = models.Sequential()
model.add(layers.Dense(32, activation='softmax', input_shape=(50,)))
model.add(layers.Dense(7, activation='softmax'))

但这给了我以下错误：

ValueError：检查模型输入时出错：Numpy数组的列表您传递给模型的信息不是模型期望的大小。预期会看到1个数组，但得到以下3个列表数组：

有关x_inp和x_out的计算方式的其他信息：

def in_out_tensors(self, multiplicity=None):
    """
    Builds a GraphSAGE model for node or link/node pair prediction, depending on the generator used to construct
    the model (whether it is a node or link/node pair generator).

    Returns:
        tuple: (x_inp, x_out), where ``x_inp`` is a list of Keras input tensors
        for the specified GraphSAGE model (either node or link/node pair model) and ``x_out`` contains
        model output tensor(s) of shape (batch_size, layer_sizes[-1])

    """
    if multiplicity is None:
        multiplicity = self.multiplicity

    if multiplicity == 1:
        return self._node_model()
    elif multiplicity == 2:
        return self._link_model()
    else:
        raise RuntimeError(
            "Currently only multiplicities of 1 and 2 are supported. Consider using node_model or "
            "link_model method explicitly to build node or link prediction model, respectively."
        )

def _node_model(self):
    """
    Builds a GraphSAGE model for node prediction

    Returns:
        tuple: (x_inp, x_out) where ``x_inp`` is a list of Keras input tensors
        for the specified GraphSAGE model and ``x_out`` is the Keras tensor
        for the GraphSAGE model output.

    """
    # Create tensor inputs for neighbourhood sampling
    x_inp = [
        Input(shape=(s, self.input_feature_size)) for s in self.neighbourhood_sizes
    ]

    # Output from GraphSAGE model
    x_out = self(x_inp)

    # Returns inputs and outputs
    return x_inp, x_out

Keras：您要传递给模型的Numpy数组列表与模型预期的大小不同

0 个答案: