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- x, x_test, y, y_test = train_test_split(x_, y_, test_size=0.1)
- x_train, x_dev, y_train, y_dev = train_test_split(x, y, test_size=0.1)
- embedding_vecor_length = 100
- model = Sequential()
- model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length))
- model.add(Conv1D(filters=32, kernel_size=3, padding='same', activation='relu'))
- model.add(MaxPooling1D(pool_size=2))
- model.add(keras.layers.Dropout(0.3))
- model.add(Conv1D(filters=32, kernel_size=4, padding='same', activation='relu'))
- model.add(MaxPooling1D(pool_size=2))
- model.add(keras.layers.Dropout(0.3))
- model.add(Conv1D(filters=32, kernel_size=5, padding='same', activation='relu'))
- model.add(MaxPooling1D(pool_size=2))
- model.add(keras.layers.Dropout(0.3))
- model.add(Conv1D(filters=32, kernel_size=7, padding='same', activation='relu'))
- model.add(MaxPooling1D(pool_size=2))
- model.add(keras.layers.Dropout(0.3))
- model.add(Conv1D(filters=32, kernel_size=9, padding='same', activation='relu'))
- model.add(MaxPooling1D(pool_size=2))
- model.add(keras.layers.Dropout(0.3))
- model.add(Conv1D(filters=32, kernel_size=12, padding='same', activation='relu'))
- model.add(MaxPooling1D(pool_size=2))
- model.add(keras.layers.Dropout(0.3))
- model.add(Conv1D(filters=32, kernel_size=15, padding='same', activation='relu'))
- model.add(MaxPooling1D(pool_size=2))
- model.add(keras.layers.Dropout(0.3))
- model.add(LSTM(200,dropout=0.3, recurrent_dropout=0.3))
- model.add(Dense(labels_count, activation='softmax'))
- model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
- print(model.summary())
- model.fit(x_train, y_train, epochs=25, batch_size=30)
- scores = model.evaluate(x_tеst, y_test)
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