使用tensorflow怎么把RNN路径学习训练代码改成biRNN路径学习训练

将RNN改成biRNN可以通过在RNN层前加一个双向层来实现。下面是一个使用TensorFlow实现biRNN路径学习训练的示例代码：

import tensorflow as tf

# 定义输入和输出的维度
num_input = 28
timesteps = 28
num_hidden = 128
num_classes = 10

# 定义输入占位符
X = tf.placeholder("float", [None, timesteps, num_input])
Y = tf.placeholder("float", [None, num_classes])

# 定义双向RNN层
fw_cell = tf.contrib.rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)
bw_cell = tf.contrib.rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)
outputs, _ = tf.nn.bidirectional_dynamic_rnn(fw_cell, bw_cell, X, dtype=tf.float32)

# 将双向RNN的输出拼接起来
output = tf.concat(outputs, 2)

# 定义输出层
weight = tf.Variable(tf.random_normal([2*num_hidden, num_classes]))
bias = tf.Variable(tf.random_normal([num_classes]))
output = tf.transpose(output, [1, 0, 2])
last = tf.gather(output, int(output.get_shape()[0])-1)
prediction = tf.matmul(last, weight) + bias

# 定义损失函数和优化器
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=0.01).minimize(loss)

# 定义准确率
correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(1000):
        batch_x, batch_y = mnist.train.next_batch(128)
        batch_x = batch_x.reshape((128, timesteps, num_input))
        sess.run(optimizer, feed_dict={X: batch_x, Y: batch_y})
        if i % 100 == 0:
            acc = sess.run(accuracy, feed_dict={X: batch_x, Y: batch_y})
            loss_value = sess.run(loss, feed_dict={X: batch_x, Y: batch_y})
            print("Iter " + str(i) + ", Minibatch Loss= " + \
                  "{:.6f}".format(loss_value) + ", Training Accuracy= " + \
                  "{:.5f}".format(acc))

在这个代码中，我们添加了一个双向RNN层，并将其输出拼接起来作为输出层的输入。其他部分与标准的RNN路径学习训练的代码类似。