当用keras框架时，如果输入不是tensor张量，输入模型训练就会报错，提示输入数据得是张量才可以。

下面是解决的方案之一：

直接运用keras的Input API就行，可以把数据实例化为Tensor，如下所示：

input_window = Input(shape = input_shape)

这样就把输入数据转换为tensor了。

keras的Input API函数定义如下，具体可按自己的需要改写参数：

def Input(shape=None, batch_shape=None,
          name=None, dtype=None, sparse=False,
          tensor=None):
    """`Input()` is used to instantiate a Keras tensor.

    A Keras tensor is a tensor object from the underlying backend
    (Theano, TensorFlow or CNTK), which we augment with certain
    attributes that allow us to build a Keras model
    just by knowing the inputs and outputs of the model.

    For instance, if a, b and c are Keras tensors,
    it becomes possible to do:
    `model = Model(input=[a, b], output=c)`

    The added Keras attributes are:
        `_keras_shape`: Integer shape tuple propagated
            via Keras-side shape inference.
        `_keras_history`: Last layer applied to the tensor.
            the entire layer graph is retrievable from that layer,
            recursively.

    # Arguments
        shape: A shape tuple (integer), not including the batch size.
            For instance, `shape=(32,)` indicates that the expected input
            will be batches of 32-dimensional vectors.
        batch_shape: A shape tuple (integer), including the batch size.
            For instance, `batch_shape=(10, 32)` indicates that
            the expected input will be batches of 10 32-dimensional vectors.
            `batch_shape=(None, 32)` indicates batches of an arbitrary number
            of 32-dimensional vectors.
        name: An optional name string for the layer.
            Should be unique in a model (do not reuse the same name twice).
            It will be autogenerated if it isn't provided.
        dtype: The data type expected by the input, as a string
            (`float32`, `float64`, `int32`...)
        sparse: A boolean specifying whether the placeholder
            to be created is sparse.
        tensor: Optional existing tensor to wrap into the `Input` layer.
            If set, the layer will not create a placeholder tensor.

    # Returns
        A tensor.

    # Example

    ```python
    # this is a logistic regression in Keras
    x = Input(shape=(32,))
    y = Dense(16, activation='softmax')(x)
    model = Model(x, y)
    ```
    """
    if not batch_shape and tensor is None:
        assert shape is not None, ('Please provide to Input either a `shape`'
                                   ' or a `batch_shape` argument. Note that '
                                   '`shape` does not include the batch '
                                   'dimension.')
    if shape is not None and not batch_shape:
        batch_shape = (None,) + tuple(shape)
    if not dtype:
        dtype = K.floatx()
    input_layer = InputLayer(batch_input_shape=batch_shape,
                             name=name, dtype=dtype,
                             sparse=sparse,
                             input_tensor=tensor)
    # Return tensor including _keras_shape and _keras_history.
    # Note that in this case train_output and test_output are the same pointer.
    outputs = input_layer._inbound_nodes[0].output_tensors
    return unpack_singleton(outputs)