def cbow_base_model(dict_size, emb_size=100, context_window_size=4):
model = keras.models.Sequential()
model.add(Embedding(dict_size, emb_size,
input_length=context_window_size,
embeddings_initializer=keras.initializers.TruncatedNormal(mean=0.0, stddev=0.2),
))
model.add(Lambda(lambda x: K.mean(x, axis=1), output_shape=(emb_size,)))
model.add(Dense(dict_size))
model.add(Activation('softmax')) # TODO: use nce
sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='categorical_crossentropy',)
return model
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