Training only one output of a network in Keras

Question

I have a network in Keras with many outputs, however, my training data only provides information for a single output at a time.

At the moment my method for training

Answer 1

In order to achieve this I ended up using the 'Functional API'. You basically create multiple models, using the same layers input and hidden layers but different output layers.

For example:

https://keras.io/getting-started/functional-api-guide/

from keras.layers import Input, Dense
from keras.models import Model

# This returns a tensor
inputs = Input(shape=(784,))

# a layer instance is callable on a tensor, and returns a tensor
x = Dense(64, activation='relu')(inputs)
x = Dense(64, activation='relu')(x)
predictions_A = Dense(1, activation='softmax')(x)
predictions_B = Dense(1, activation='softmax')(x)

# This creates a model that includes
# the Input layer and three Dense layers
modelA = Model(inputs=inputs, outputs=predictions_A)
modelA.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
modelB = Model(inputs=inputs, outputs=predictions_B)
modelB.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])