VerificationCode/chinese_order/model.py

import math
from keras import Input, Model
from keras.layers import Dense, Concatenate, Embedding, LSTM, Bidirectional, Conv1D, GlobalMaxPooling1D


def lstm_phrase(input_shape, output_shape=1):
    inputs = Input(shape=input_shape[0])

    x = Embedding(input_shape[1]+1, 16, input_length=input_shape[0])(inputs)
    # x = Dropout(0.2)(x)
    x = Bidirectional(LSTM(32))(x)
    # x = Dropout(0.2)(x)
    x = Dense(16)(x)
    x = Dense(output_shape, activation="sigmoid")(x)

    model = Model(inputs=inputs, outputs=x)
    # model.summary(line_length=100)
    return model


def text_cnn_phrase(input_shape, output_shape=1):
    inputs = Input(shape=input_shape[0])
    x = Embedding(input_shape[1]+1, 50, input_length=input_shape[0])(inputs)

    x1 = Conv1D(64, 3, activation="relu", padding="same")(x)
    x1 = GlobalMaxPooling1D()(x1)
    x2 = Conv1D(64, 4, activation="relu", padding="same")(x)
    x2 = GlobalMaxPooling1D()(x2)
    x3 = Conv1D(64, 5, activation="relu", padding="same")(x)
    x3 = GlobalMaxPooling1D()(x3)

    x = Concatenate()([x1, x2, x3])
    x = Dense(output_shape, activation="sigmoid")(x)

    model = Model(inputs=inputs, outputs=x)
    model.summary(line_length=100)
    return model


if __name__ == "__main__":
    text_cnn_phrase((3, 5792))
    print(math.log(5792, 2))