基于TensorFlow实现的mmRNN的代码示例

以下是基于TensorFlow实现的mmRNN的代码示例：

import tensorflow as tf
from tensorflow.contrib import rnn

# 定义mmRNN的类
class mmRNN:
    def __init__(self, input_dim, hidden_dim, output_dim):
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        self.weight_initializer = tf.random_normal_initializer(stddev=0.1)
        self.const_initializer = tf.constant_initializer(0.0)

        # 定义输入变量和输出变量
        self.input_data = tf.placeholder(tf.float32, [None, None, self.input_dim])
        self.output_data = tf.placeholder(tf.float32, [None, self.output_dim])

        # 定义前向网络
        with tf.variable_scope("forward"):
            self.forward_cell = rnn.BasicLSTMCell(self.hidden_dim)
            self.forward_output, _ = tf.nn.dynamic_rnn(self.forward_cell, self.input_data, dtype=tf.float32)

        # 定义反向网络
        with tf.variable_scope("backward"):
            self.backward_cell = rnn.BasicLSTMCell(self.hidden_dim)
            self.backward_output, _ = tf.nn.dynamic_rnn(self.backward_cell, self.input_data, dtype=tf.float32, time_major=True)

        # 得到最后一个时间步上前向和反向网络的输出
        forward_last_output = self.forward_output[:, -1, :]
        backward_last_output = self.backward_output[:, 0, :]

        # 将前向和反向网络的输出拼接起来
        concat_output = tf.concat([forward_last_output, backward_last_output], axis=1)

        # 定义输出层
        with tf.variable_scope("output"):
            self.output_weight = tf.get_variable("output_weight", [self.hidden_dim * 2, self.output_dim], initializer=self.weight_initializer)
            self.output_bias = tf.get_variable("output_bias", [self.output_dim], initializer=self.const_initializer)

            self.prediction = tf.matmul(concat_output, self.output_weight) + self.output_bias

        # 定义损失和优化器
        self.loss = tf.reduce_mean(tf.square(self.prediction - self.output_data))
        self.optimizer = tf.train.AdamOptimizer().minimize(self.loss)

# 使用mmRNN进行训练和预测
input_dim = 10
hidden_dim = 100
output_dim = 1
batch_size = 64
num_steps = 100

# 构造数据集
train_x = np.random.randn(1000, num_steps, input_dim)
train_y = np.random.randn(1000, output_dim)

# 定义mmRNN模型
model = mmRNN(input_dim, hidden_dim, output_dim)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    # 训练模型
    for i in range(1000):
        x_batch = train_x[i*batch_size:(i+1)*batch_size]
        y_batch = train_y[i*batch_size:(i+1)*batch_size]
        loss, _ = sess.run([model.loss, model.optimizer], feed_dict={model.input_data: x_batch, model.output_data: y_batch})
        print("step: {}, loss: {}".format(i, loss))

    # 使用模型进行预测
    test_x = np.random.randn(10, num_steps, input_dim)
    prediction = sess.run(model.prediction, feed_dict={model.input_data: test_x})

    print("prediction: {}".format(prediction))