I'm training a language model in Keras and would like to speed up training by using sampled softmax as the final activation function in my network. From the TF docs, it looks like I need to supply arguments for weights and biases, but I'm unsure of what is expected as input for these. It seems like I could write a custom function in Keras as follows:
import keras.backend as K
def sampled_softmax(weights, biases, y_true, y_pred, num_sampled, num_classes):
return K.sampled_softmax(weights, biases, y_true, y_pred, num_sampled, num_classes)
However, I'm unsure of how to "plug this in" to my existing network. The architecture for the LM is pretty dead-simple:
model = Sequential()
model.add(Embedding(input_dim=len(vocab), output_dim=256))
model.add(LSTM(1024, return_sequence=True))
model.add(Dense(output_dim=len(vocab), activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
Given this architecture, could I pass the sampled_softmax function as the loss argument when calling the compile method on the model? Or do this need to be written as a layer that comes after the final fully-connected layer. Any guidance here would be greatly appreciated. Thanks.
The key observation here is that the TensorFlow sampled softmax function returns actual losses, not a set of predictions over the set of possible labels to compare with the ground truth data to then compute losses as a separate step. This makes the model setup a little bit weird.
First, we add a second input layer to the model that encodes the target (training) data a second time as an input, in addition to being the target output. This is used for the labels argument of the sampled_softmax_loss function. It needs to be a Keras input, because it's treated as an input when we go to instantiate and set up the model.
Second, we construct a new custom Keras layer that calls the sampled_softmax_loss function with two Keras layers as its inputs: the output of the dense layer that predicts our classes, and then the second input that contains a copy of the training data. Note that we're doing some serious hackery accessing the _keras_history instance variable to fetch the weight and bias tensors from the output tensor of the original fully-connected layer.
Finally, we have to construct a new "dumb" loss function that ignores the training data and just uses the loss reported by the sampled_softmax_loss function.
Note that because the sampled softmax function returns losses, not class predictions, you can't use this model specification for validation or inference. You'll need to re-use the trained layers from this "training version" in a new specification that applies a standard softmax function to the original dense layer which has the default activation function applied.
There is definitely a more elegant way to do this, but I believe this works, so I figured I'd post it here now as-is rather than wait until I have something that's a little bit neater. For example, you'd probably want to make the number of classes an argument of the SampledSoftmax layer, or better yet, condense this all into the loss function as in the original question and avoid passing in the training data twice.
from keras.models import Model
from keras.layers import Input, Dense, Layer
from keras import backend as K
class SampledSoftmax(Layer):
def __init__(self, **kwargs):
super(SampledSoftmax, self).__init__(**kwargs)
def call(self, inputs):
"""
The first input should be the model as it were, and the second the
target (i.e., a repeat of the training data) to compute the labels
argument
"""
# the labels input to this function is batch size by 1, where the
# value at position (i, 1) is the index that is true (not zero)
# e.g., (0, 0, 1) => (2) or (0, 1, 0, 0) => (1)
return K.tf.nn.sampled_softmax_loss(weights=inputs[0]._keras_history[0].weights[0],
biases=inputs[0]._keras_history[0].bias,
inputs=inputs[0],
labels=K.tf.reshape(K.tf.argmax(inputs[1], 1), [-1, 1]),
num_sampled=1000,
num_classes=200000)
def custom_loss(y_true, y_pred):
return K.tf.reduce_mean(y_pred)
num_classes = 200000
input = Input(shape=(300,))
target_input = Input(shape=(num_classes,))
dense = Dense(num_classes)
outputs = dense(input)
outputs = SampledSoftmax()([outputs, target_input])
model = Model([input, target_input], outputs)
model.compile(optimizer=u'adam', loss=custom_loss)
# train as desired
