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tf.contrib.rnn.static_bidirectional_rnn() 函数

#1、将mnist每个字体的像素序列当做时间序列,喂给网络#2、实现一个双向RNN网络,其中的cell可以是LSTM,也可以是GRUimport tensorflow as tfimport numpy as nplearning_rate = 0.01    # 因为优化器是adam,所以学习速率较低max_samples = 400000    # 最大训练样本数为40万batch_size = 128display_step = 10       # 每间隔10次训练,就展示一次训练情况n_input = 28 # 图像的宽度为28,因此设置输入为28n_steps = 28 # 图像的高度为28,因此设置LSTM的展开步数(unrolled steps of LSTM)也设置为28n_hidden = 256 # 定义一个方向的cell的数量n_classes = 10 # 0-9,共有10个分类.from tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("MNIST_data/", one_hot=True)# endregion# region 定义softmax层的权重# 因为要合成两个LSTM的输入,所以第一个维度是2*n_hiddenweights = {    # Hidden layer weights => 2*n_hidden because of foward + backward cells    'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes]))}biases = {    'out': tf.Variable(tf.random_normal([n_classes]))}# endregion# region 构建计算图# x是一个二维结构,但是和卷积网络中的空间二维结构不同,# 这里的二维被理解成第一个维度是时间序列n_steps,第二维度是每个时间点下的数据n_inputx = tf.placeholder("float", [None, n_steps, n_input])y = tf.placeholder("float", [None, n_classes])# 将x拆成一个长度为n_steps的列表,每个元素tensor的尺寸为[batch_size,n_input]x_unstack = tf.unstack(x, axis=1)# lstm_fw_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)lstm_fw_cell = tf.contrib.rnn.GRUCell(n_hidden)# lstm_bw_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)lstm_bw_cell = tf.contrib.rnn.GRUCell(n_hidden)outputs, _, _ = tf.contrib.rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x_unstack,                                           dtype=tf.float32)pred=tf.matmul(outputs[-1], weights['out']) + biases['out']cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)# tf.argmax(pred,1)求每行中最大的元素的角标(即列号)correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))# tf.cast(correct_pred, tf.float32),将correct_pred转化为浮点型accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))init = tf.global_variables_initializer()# endregion# region 执行计算图with tf.Session() as sess:    sess.run(init)    step = 1    while step * batch_size < max_samples:# step*128<400000        # 直接读出来的batch_x的尺寸为[batch_size,784]        batch_x, batch_y = mnist.train.next_batch(batch_size)        # batch_x经过reshape后,尺寸变为(batch_size, n_steps, n_input)        batch_x = batch_x.reshape((batch_size, n_steps, n_input))        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})        if step % display_step == 0:            acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})            loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})            print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \                  "{:.6f}".format(loss) + ", Training Accuracy= " + \                  "{:.5f}".format(acc))        step += 1    print("Optimization Finished!")    test_len = 10000    test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))    test_label = mnist.test.labels[:test_len]    print("Testing Accuracy:", \        sess.run(accuracy, feed_dict={x: test_data, y: test_label}))# endregion

 

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