tf 从RNN到BERT

数据初始化

import tensorflow as tf
from tensorflow import keras
from 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)

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