Train Multi-Input Keras NN with batch of training data

Question

I would like to use Keras to train a multi-input NN with a batch of training data, but I'm not able to pass a set of input and output samples to execute a fit or a train_on_batch on the model.

My NN is defined as following:

    i1 = keras.layers.Input(shape=(2,))
    i2 = keras.layers.Input(shape=(2,))
    i3 = keras.layers.Input(shape=(2,))
    i_layer = keras.layers.Dense(2, activation='sigmoid')
    embedded_i1 = i_layer(i1)
    embedded_i2 = i_layer(i2)
    embedded_i3 = i_layer(i3)

    middle_concatenation = keras.layers.concatenate([embedded_i1, embedded_i2, embedded_i3], axis=1)

    out = keras.layers.Dense(1, activation='sigmoid')(middle_concatenation)

    model = keras.models.Model(inputs=[i1, i2, i3], outputs=out)
    model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])

For example, an instance of the input (successfully used for predict the output) is the following:

[array([[0.1, 0.2]]), array([[0.3, 0.5]]), array([[0.1, 0.3]])]

But when I try to train my model with:

    inputs = [[np.array([[0.1, 0.2]]), np.array([[0.3, 0.5]]), np.array([[0.1, 0.3]])],
                     [np.array([[0.2, 0.1]]), np.array([[0.5, 0.3]]), np.array([[0.3, 0.1]])]
                         ]
    outputs = np.ones(len(inputs))
    model.fit(inputs, outputs)

I get this error:

ValueError: Error when checking model input: you are passing a list as input to your model, but the model expects a list of 3 Numpy arrays instead. The list you passed was: [[array([[ 0.1,  0.2]]), array([[ 0.3,  0.5]]), array([[ 0.1,  0.3]])], [array([[ 0.2,  0.1]]), array([[ 0.5,  0.3]]), array([[ 0.3,  0.1]])]]

What am I doing wrong?
How can I train a multi-input NN with a batch of input/output samples?

Thank you!

Answer 1

the problem is just incorrect formatting. You can't pass a list to keras, only numpy arrays, so when you have your data structured like

 inputs = [[np.array([[0.1, 0.2]]), np.array([[0.3, 0.5]]), np.array([[0.1, 0.3]])],
                     [np.array([[0.2, 0.1]]), np.array([[0.5, 0.3]]), np.array([[0.3, 0.1]])]
                         ]

You need to pass one list element into your model at a time. You will also need to pass one output value to the model at a time. To do this, structure you outputs like this

outputs = [np.ones(1) for x in inputs]

[array([ 1.]), array([ 1.])]

Then you can loop over the the fit function like this

 for z in range(0,len(inputs)):
     model.fit(inputs[z],outputs[z],batch_size=1)

you can also replace model.fit with model.train_on_batch() instead, see docs

however to avoid the loop, you could just have 3 numpy arrays stored in your inputs list and have you single outputs as a numpy array. If you only want to train on a single batch at a time, you could set your batch size to do that.

inputs = [np.array([[0.1, 0.2],[0.2, 0.1]]), np.array([[0.3, 0.5],[0.5, 0.3]]), np.array([[0.1, 0.3],[0.3, 0.1]])]

outputs = np.ones(inputs[0].shape[0])

model.fit(inputs,outputs,batch_size=1)