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@jjallaire
Created January 5, 2018 20:52
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from keras.models import Model
from keras import layers
from keras import Input
text_vocabulary_size = 10000
question_vocabulary_size = 10000
answer_vocabulary_size = 500
text_input = Input(shape=(None,), dtype='int32', name='text')
embedded_text = layers.Embedding(
64, text_vocabulary_size)(text_input)
encoded_text = layers.LSTM(32)(embedded_text)
question_input = Input(shape=(None,),
dtype='int32',
name='question')
embedded_question = layers.Embedding(
32, question_vocabulary_size)(question_input)
encoded_question = layers.LSTM(16)(embedded_question)
concatenated = layers.concatenate([encoded_text, encoded_question], axis = -1)
answer = layers.Dense(answer_vocabulary_size,
activation='softmax')(concatenated)
model = Model([text_input, question_input], answer)
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['acc'])
import numpy as np
num_samples = 1000
max_length = 100
text = np.random.randint(1, text_vocabulary_size,
size=(num_samples, max_length))
question = np.random.randint(1, question_vocabulary_size,
size=(num_samples, max_length))
answers = np.random.randint(0, 1,
size=(num_samples, answer_vocabulary_size))
model.fit([text, question], answers, epochs=10, batch_size=128)
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