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 has been to run a prediction on the input in question, change the value of the particular output that I am training and then doing a single batch update. If I'm right this is the same as setting the loss for all outputs to zero except the one that I'm trying to train.
Is there a better way? I've tried class weights where I set a zero weight for all but the output I'm training but it doesn't give me the results I expect?
I'm using the Theano backend.
Outputting multiple results and optimizing only one of them
Let's say you want to return output from multiple layers, maybe from some intermediate layers, but you need to optimize only one target output. Here's how you can do it:
Let's start with this model:
inputs = Input(shape=(784,))
x = Dense(64, activation='relu')(inputs)
# you want to extract these values
useful_info = Dense(32, activation='relu', name='useful_info')(x)
# final output. used for loss calculation and optimization
result = Dense(1, activation='softmax', name='result')(useful_info)
Compile with multiple outputs, set loss as None for extra outputs:
Give None for outputs that you don't want to use for loss calculation and optimization
model = Model(inputs=inputs, outputs=[result, useful_info])
model.compile(optimizer='rmsprop',
loss=['categorical_crossentropy', None],
metrics=['accuracy'])
Provide only target outputs when training. Skipping extra outputs:
model.fit(my_inputs, {'result': train_labels}, epochs=.., batch_size=...)
# this also works:
#model.fit(my_inputs, [train_labels], epochs=.., batch_size=...)
One predict to get them all
Having one model you can run predict only once to get all outputs you need:
predicted_labels, useful_info = model.predict(new_x)
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'])
