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from keras.preprocessing import sequence
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from keras.models import Sequential
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from keras.layers import Dense, Embedding
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from keras.layers import LSTM
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from keras.datasets import imdb
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max_features = 20000
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# cut texts after this number of words (among top max_features most common words)
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maxlen = 80
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batch_size = 32
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print('Loading data...')
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(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
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print(len(x_train), 'train sequences')
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print(len(x_test), 'test sequences')
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print('Pad sequences (samples x time)')
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x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
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x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
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print('x_train shape:', x_train.shape)
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print('x_test shape:', x_test.shape)
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print('Build model...')
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model = Sequential()
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model.add(Embedding(max_features, 128))
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model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))
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model.add(Dense(1, activation='sigmoid'))
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# try using different optimizers and different optimizer configs
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model.compile(loss='binary_crossentropy',
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optimizer='adam',
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import pandas as pd
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from sortedcontainers import SortedSet
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import numpy as np
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from sklearn.model_selection import train_test_split
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from keras.layers import Dense, Embedding, Input, Flatten
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from keras.layers import LSTM, GRU, Dropout
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from keras.models import Model
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import keras
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from keras.utils import plot_model
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import utils
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import time
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def build_model(want_answer_size, infact_answer_size):
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inputs_want_answer = Input(shape=(want_answer_size, ), name='want_answer_input')
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inputs_infact_answer = Input(shape=(infact_answer_size, ), name='infact_answer_input')
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x_1 = Embedding(want_answer_size, 128, name='want_answer_embedding', embeddings_initializer='he_normal', embeddings_regularizer=keras.regularizers.l2(0.01))(inputs_want_answer)
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x_2 = Embedding(infact_answer_size, 128, name='infact_answer_embedding', embeddings_initializer='he_normal', embeddings_regularizer=keras.regularizers.l2(0.01))(inputs_infact_answer)
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x_1 = GRU(128, dropout=0.4, return_sequences=True, recurrent_initializer='he_normal', recurrent_regularizer=keras.regularizers.l2(0.01))(x_1)
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x_2 = GRU(128, dropout=0.4, return_sequences=True, recurrent_initializer='he_normal', recurrent_regularizer=keras.regularizers.l2(0.01))(x_2)
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x = keras.layers.concatenate([x_1, x_2])
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x = Flatten()(x)
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x = Dropout(0.3)(x)
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x = Dense(64, activation='relu')(x)
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predictions = Dense(2, activation='softmax')(x)
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model = Model(inputs=[inputs_want_answer, inputs_infact_answer], outputs=predictions)
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return model
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if __name__ == '__main__':
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df = pd.read_excel('./预期输出与实际输出数据表.xlsx')
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want_answer_corpus, infact_answer_corpus = utils.build_corpus(df)
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onehot = utils.label2onehot(df['是否正确'])
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x_train_1, x_test_1, y_train, y_test = train_test_split(want_answer_corpus, onehot, random_state=2333)
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x_train_2, x_test_2, _, _ = train_test_split(infact_answer_corpus, onehot, random_state=2333)
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want_answer_corpus_size = len(want_answer_corpus[0])
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infact_answer_corpus_size = len(infact_answer_corpus[0])
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model = build_model(want_answer_corpus_size, infact_answer_corpus_size)
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# plot_model(model, to_file='model.png')
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model.compile(loss='categorical_crossentropy',
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optimizer=keras.optimizers.Adam(lr=1e-4),
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metrics=['accuracy'])
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print(model.summary())
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print('Train...')
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=15,
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validation_data=(x_test, y_test))
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score, acc = model.evaluate(x_test, y_test,
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batch_size=batch_size)
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model.fit([x_train_1, x_train_2], y_train,
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batch_size=16,
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epochs=50)
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score, acc = model.evaluate([x_test_1, x_test_2], y_test,
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batch_size=8, verbose=0)
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print('Test score:', score)
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print('Test accuracy:', acc)
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