手把手教你使用TensorFlow2实现RNN

• 概述
• 权重共享
• 计算过程:
• 案例
• 完整代码

概述

RNN (Recurrent Netural Network) 是用于处理序列数据的神经网络. 所谓序列数据, 即前面的输入和后面的输入有一定的联系.

权重共享

传统神经网络:

RNN:

RNN 的权重共享和 CNN 的权重共享类似, 不同时刻共享一个权重, 大大减少了参数数量.

计算过程:

计算状态 (State)

计算输出:

案例

数据集

IBIM 数据集包含了来自互联网的 50000 条关于电影的评论, 分为正面评价和负面评价.

RNN 层

 class RNN(tf.keras.Model): def __init__(self, units): super(RNN, self).__init__() # 初始化 [b, 64] (b 表示 batch_size) self.state0 = [tf.zeros([batch_size, units])] self.state1 = [tf.zeros([batch_size, units])] # [b, 80] => [b, 80, 100] self.embedding = tf.keras.layers.Embedding(total_words, embedding_len, input_length=max_review_len) self.rnn_cell0 = tf.keras.layers.SimpleRNNCell(units=units, dropout=0.2) self.rnn_cell1 = tf.keras.layers.SimpleRNNCell(units=units, dropout=0.2) # [b, 80, 100] => [b, 64] => [b, 1] self.out_layer = tf.keras.layers.Dense(1) def call(self, inputs, training=None): """ :param inputs: [b, 80] :param training: :return: """ state0 = self.state0 state1 = self.state1 x = self.embedding(inputs) for word in tf.unstack(x, axis=1): out0, state0 = self.rnn_cell0(word, state0, training=training) out1, state1 = self.rnn_cell1(out0, state1, training=training) # [b, 64] -> [b, 1] x = self.out_layer(out1) prob = tf.sigmoid(x) return prob 

获取数据

 def get_data(): # 获取数据 (X_train, y_train), (X_test, y_test) = tf.keras.datasets.imdb.load_data(num_words=total_words) # 更改句子长度 X_train = tf.keras.preprocessing.sequence.pad_sequences(X_train, maxlen=max_review_len) X_test = tf.keras.preprocessing.sequence.pad_sequences(X_test, maxlen=max_review_len) # 调试输出 print(X_train.shape, y_train.shape)  # (25000, 80) (25000,) print(X_test.shape, y_test.shape)  # (25000, 80) (25000,) # 分割训练集 train_db = tf.data.Dataset.from_tensor_slices((X_train, y_train)) train_db = train_db.shuffle(10000).batch(batch_size, drop_remainder=True) # 分割测试集 test_db = tf.data.Dataset.from_tensor_slices((X_test, y_test)) test_db = test_db.batch(batch_size, drop_remainder=True) return train_db, test_db 

完整代码

 import tensorflow as tf class RNN(tf.keras.Model): def __init__(self, units): super(RNN, self).__init__() # 初始化 [b, 64] self.state0 = [tf.zeros([batch_size, units])] self.state1 = [tf.zeros([batch_size, units])] # [b, 80] => [b, 80, 100] self.embedding = tf.keras.l<a style="color:transparent">来源gao($daima.com搞@代@#码(网</a>ayers.Embedding(total_words, embedding_len, input_length=max_review_len) self.rnn_cell0 = tf.keras.layers.SimpleRNNCell(units=units, dropout=0.2) self.rnn_cell1 = tf.keras.layers.SimpleRNNCell(units=units, dropout=0.2) # [b, 80, 100] => [b, 64] => [b, 1] self.out_layer = tf.keras.layers.Dense(1) def call(self, inputs, training=None): """ :param inputs: [b, 80] :param training: :return: """ state0 = self.state0 state1 = self.state1 x = self.embedding(inputs) for word in tf.unstack(x, axis=1): out0, state0 = self.rnn_cell0(word, state0, training=training) out1, state1 = self.rnn_cell1(out0, state1, training=training) # [b, 64] -> [b, 1] x = self.out_layer(out1) prob = tf.sigmoid(x) return prob # 超参数 total_words = 10000  # 文字数量 max_review_len = 80  # 句子长度 embedding_len = 100  # 词维度 batch_size = 1024  # 一次训练的样本数目 learning_rate = 0.0001  # 学习率 iteration_num = 20  # 迭代次数 optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)  # 优化器 loss = tf.losses.BinaryCrossentropy(from_logits=True)  # 损失 model = RNN(64) # 调试输出summary model.build(input_shape=[None, 64]) print(model.summary()) # 组合 model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"]) def get_data(): # 获取数据 (X_train, y_train), (X_test, y_test) = tf.keras.datasets.imdb.load_data(num_words=total_words) # 更改句子长度 X_train = tf.keras.preprocessing.sequence.pad_sequences(X_train, maxlen=max_review_len) X_test = tf.keras.preprocessing.sequence.pad_sequences(X_test, maxlen=max_review_len) # 调试输出 print(X_train.shape, y_train.shape)  # (25000, 80) (25000,) print(X_test.shape, y_test.shape)  # (25000, 80) (25000,) # 分割训练集 train_db = tf.data.Dataset.from_tensor_slices((X_train, y_train)) train_db = train_db.shuffle(10000).batch(batch_size, drop_remainder=True) # 分割测试集 test_db = tf.data.Dataset.from_tensor_slices((X_test, y_test)) test_db = test_db.batch(batch_size, drop_remainder=True) return train_db, test_db if __name__ == "__main__": # 获取分割的数据集 train_db, test_db = get_data() # 拟合 model.fit(train_db, epochs=iteration_num, validation_data=test_db, validation_freq=1) 

输出结果:

Model: “rnn”
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) multiple 1000000
_________________________________________________________________
simple_rnn_cell (SimpleRNNCe multiple 10560
_________________________________________________________________
simple_rnn_cell_1 (SimpleRNN multiple 8256
_________________________________________________________________
dense (Dense) multiple 65
=================================================================
Total params: 1,018,881
Trainable params: 1,018,881
Non-trainable params: 0
_________________________________________________________________
None

(25000, 80) (25000,)
(25000, 80) (25000,)
Epoch 1/20
2021-07-10 17:59:45.150639: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
24/24 [==============================] – 12s 294ms/step – loss: 0.7113 – accuracy: 0.5033 – val_loss: 0.6968 – val_accuracy: 0.4994
Epoch 2/20
24/24 [==============================] – 7s 292ms/step – loss: 0.6951 – accuracy: 0.5005 – val_loss: 0.6939 – val_accuracy: 0.4994
Epoch 3/20
24/24 [==============================] – 7s 297ms/step – loss: 0.6937 – accuracy: 0.5000 – val_loss: 0.6935 – val_accuracy: 0.4994
Epoch 4/20
24/24 [==============================] – 8s 316ms/step – loss: 0.6934 – accuracy: 0.5001 – val_loss: 0.6933 – val_accuracy: 0.4994
Epoch 5/20
24/24 [==============================] – 7s 301ms/step – loss: 0.6934 – accuracy: 0.4996 – val_loss: 0.6933 – val_accuracy: 0.4994
Epoch 6/20
24/24 [==============================] – 8s 334ms/step – loss: 0.6932 – accuracy: 0.5000 – val_loss: 0.6932 – val_accuracy: 0.4994
Epoch 7/20
24/24 [==============================] – 10s 398ms/step – loss: 0.6931 – accuracy: 0.5006 – val_loss: 0.6932 – val_accuracy: 0.4994
Epoch 8/20
24/24 [==============================] – 9s 382ms/step – loss: 0.6930 – accuracy: 0.5006 – val_loss: 0.6931 – val_accuracy: 0.4994
Epoch 9/20
24/24 [==============================] – 8s 322ms/step – loss: 0.6924 – accuracy: 0.4995 – val_loss: 0.6913 – val_accuracy: 0.5240
Epoch 10/20
24/24 [==============================] – 8s 321ms/step – loss: 0.6812 – accuracy: 0.5501 – val_loss: 0.6655 – val_accuracy: 0.5767
Epoch 11/20
24/24 [==============================] – 8s 318ms/step – loss: 0.6381 – accuracy: 0.6896 – val_loss: 0.6235 – val_accuracy: 0.7399
Epoch 12/20
24/24 [==============================] – 8s 323ms/step – loss: 0.6088 – accuracy: 0.7655 – val_loss: 0.6110 – val_accuracy: 0.7533
Epoch 13/20
24/24 [==============================] – 8s 321ms/step – loss: 0.5949 – accuracy: 0.7956 – val_loss: 0.6111 – val_accuracy: 0.7878
Epoch 14/20
24/24 [==============================] – 8s 324ms/step – loss: 0.5859 – accuracy: 0.8142 – val_loss: 0.5993 – val_accuracy: 0.7904
Epoch 15/20
24/24 [==============================] – 8s 330ms/step – loss: 0.5791 – accuracy: 0.8318 – val_loss: 0.5961 – val_accuracy: 0.7907
Epoch 16/20
24/24 [==============================] – 8s 340ms/step – loss: 0.5739 – accuracy: 0.8421 – val_loss: 0.5942 – val_accuracy: 0.7961
Epoch 17/20
24/24 [==============================] – 9s 378ms/step – loss: 0.5701 – accuracy: 0.8497 – val_loss: 0.5933 – val_accuracy: 0.8014
Epoch 18/20
24/24 [==============================] – 9s 361ms/step – loss: 0.5665 – accuracy: 0.8589 – val_loss: 0.5958 – val_accuracy: 0.8082
Epoch 19/20
24/24 [==============================] – 8s 353ms/step – loss: 0.5630 – accuracy: 0.8681 – val_loss: 0.5931 – val_accuracy: 0.7966
Epoch 20/20
24/24 [==============================] – 8s 314ms/step – loss: 0.5614 – accuracy: 0.8702 – val_loss: 0.5925 – val_accuracy: 0.7959

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