在 Keras model.fit 中将训练数据指定为元组 (x, y) 的正确方法，具有多个输入和输出

Question

I am training a Keras Tensorflow model with three inputs and two outputs:

mymodel = tf.keras.Model([X1, X2, X3], [y1, y2])

When I fit this model by separately specifying x and y data, it works fine without any hitches:

history = mymodel.fit([X1, X2, X3], [y1, y2], batch_size=128, epochs=5)

However, I would like to provide the training data as a single tuple (x, y) in order to maintain compatibility with a custom data generator. When I do this, it throws an error:

data = ([X1, X2, X3], [y1, y2])
history = mymodel.fit(data, batch_size=128, epochs=5)

No gradients provided for any variable: ['dense/kernel:0', 'dense/bias:0',...

I guess my format for the data tuple is wrong.

How can I correctly specify my training data?

Answer 1

What you need is to build your data pipeline with a generator or tf.data API. According to the documentation of the training API, source :

Model.fit(
    x=None,
    y=None,
    batch_size=None,
    epochs=1,
    ...

Arguments

- x: Input data. It could be:
     A tf.data dataset. Should return a tuple of either (inputs, targets) 
     or (inputs, targets, sample_weights).

     A generator or keras.utils.Sequence returning (inputs, targets) or 
    (inputs, targets, sample_weights).

- y: If x is a dataset, generator, or keras.utils.Sequence instance, 
     y should not be specified (since targets will be obtained from x).

FYI, but if your data is numpy array or tensorflow tensor ( x ), then you need to provide the corresponding y . According to the doc

- x: 
    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).

- y:
   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 
   and tensor targets, or inversely).