tf 支持?jǐn)?shù)據(jù)格式

"""x: Input data. It could be:- A Numpy array (or array-like), or a list of arrays(in case the model has multiple inputs).- A TensorFlow tensor, or a list of tensors(in case the model has multiple inputs).- A dict mapping input names to the corresponding array/tensors,if the model has named inputs.- A `tf.data` dataset. Should return a tupleof either `(inputs, targets)` or`(inputs, targets, sample_weights)`.- A generator or `keras.utils.Sequence` returning `(inputs, targets)`or `(inputs, targets, sample weights)`.A more detailed description of unpacking behavior for iterator types(Dataset, generator, Sequence) is given below.y: Target data. Like the input data `x`,it could be either Numpy array(s) or TensorFlow tensor(s).It should be consistent with `x` (you cannot have Numpy inputs andtensor targets, or inversely). If `x` is a dataset, generator,or `keras.utils.Sequence` instance, `y` shouldnot be specified (since targets will be obtained from `x`). """

numpy array

tf.Tensor

dict

tf.data.dataSet

generator keras.utils.Sequence

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