本文共 1312 字，大约阅读时间需要 4 分钟。

数据初始化

import tensorflow as tffrom tensorflow import kerasfrom tensorflow.keras.layers import *((x_train, y_train), (x_test, y_test)) = keras.datasets.mnist.load_data()x_train = x_train.reshape(60000, -1)y_train = keras.utils.np_utils.to_categorical(y_train)

SimpleRNN

model1 = keras.Sequential()model1.add(Embedding(input_dim=256, output_dim=5))model1.add(SimpleRNN(units=2))model1.add(Dense(10))model1.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])model1.fit(x_train, y_train, batch_size=10)

GRU

model2 = keras.Sequential()model2.add(Embedding(input_dim=256, output_dim=5))model2.add(GRU(units=2))model2.add(Dense(10))model2.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])model2.fit(x_train, y_train, batch_size=10)

LSTM

model3 = keras.Sequential()model3.add(Embedding(input_dim=256, output_dim=5))model3.add(LSTM(units=2))model3.add(Dense(10))model3.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])model3.fit(x_train, y_train, batch_size=10)

encoder-decoder

model3 = keras.Sequential()model3.add(Embedding(input_dim=256, output_dim=5))model3.add(LSTM(units=2))model3.add(Dense(10))model3.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])model3.fit(x_train, y_train, batch_size=10)

转载地址：http://jlugf.baihongyu.com/