keras_None_targets.py

import keras.backend as K
from keras.callbacks import CSVLogger
from keras.datasets import mnist
from keras.layers import Input, Lambda, Dense, Flatten, BatchNormalization, Activation
from keras.models import Model


def main():
    # Both inputs and targets are `Input` tensors
    input_x = Input((28, 28), name='input_x', dtype='uint8')  # uint8 [0-255]
    y_true = Input((1,), name='y_true', dtype='uint8')  # uint8 [0-9]
    # Build prediction network as usual
    h = Flatten()(input_x)
    h = Lambda(lambda _x: K.cast(_x, 'float32'),
               output_shape=lambda _x: _x,
               name='cast')(h)  # cast uint8 to float32
    h = BatchNormalization()(h)  # normalize pixels
    for i in range(3):  # hidden relu and batchnorm layers
        h = Dense(256)(h)
        h = BatchNormalization()(h)
        h = Activation('relu')(h)
    y_pred = Dense(10, activation='softmax', name='y_pred')(h)  # softmax output layer
    # Lambda layer performs loss calculation (negative log likelihood)
    loss = Lambda(lambda (_yt, _yp): -K.log(_yp[K.reshape(K.arange(K.shape(_yt)[0]), (-1, 1)), _yt] + K.epsilon()),
                  output_shape=lambda (_yt, _yp): _yt,
                  name='loss')([y_true, y_pred])

    # Model `inputs` are both x and y. `outputs` is the loss.
    model = Model(inputs=[input_x, y_true], outputs=[loss])
    # Manually add the loss to the model
    model.add_loss(K.sum(loss, axis=None))
    # Compile with the loss weight set to None, so it will be omitted
    model.compile('adam', loss=[None], loss_weights=[None])
    # Add accuracy to the metrics
    # Cannot add as a metric to compile, because metrics for skipped outputs are skipped
    accuracy = K.mean(K.equal(K.argmax(y_pred, axis=1), K.flatten(y_true)))
    model.metrics_names.append('accuracy')
    model.metrics_tensors.append(accuracy)
    # Model summary
    model.summary()

    # Train model
    train, test = mnist.load_data()
    cb = CSVLogger("mnist_training.csv")
    model.fit(list(train), [None], epochs=300, batch_size=128, callbacks=[cb], validation_data=(list(test), [None]))


if __name__ == "__main__":
    main()

How to use None to omit outputs during training, so you can use an Input as both an input and a target. Model then has no target when you train.