Tensorflow实现用循环神经网络中的LSTM分类手写数字MNIST数据集

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这次我们用RNN来进行分类的训练，我们使用的数据集是手写数字MNIST数据集。让RNN从每张图片的第一行像素读到最后一行，然后再进行分类判断。

（1）我们构建的RNN结构图。

（2）最后的训练结果：

（3）最后的测试结果：

（4）完整的代码。

# coding:utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
tf.set_random_seed(1) # set random seed

# 导入数据
mnist = input_data.read_data_sets("MNIST_data",one_hot=True)

# hyperparameters
lr = 0.001 # learning rate
training_iters = 100000 # train step 上限
batch_size = 128
n_inputs = 28 # MNIST data input(img shape:28*28)
n_steps = 28 # time steps
n_hidden_units = 128 # neurons in hidden layer
n_classes = 10 # MNIST classes(0-9 digits)

# x y placeholder
x = tf.placeholder(tf.float32,[None,n_steps,n_inputs])
y = tf.placeholder(tf.float32,[None,n_classes])

# 对weights biases初始值的定义
weights = {
    # shape(28,128)
    "in":tf.Variable(tf.random_normal([n_inputs,n_hidden_units])),
    # shape(128,10)
    "out":tf.Variable(tf.random_normal([n_hidden_units,n_classes]))
}
biases = {
    # shape(128,)
    "in":tf.Variable(tf.constant(0.1,shape=[n_hidden_units])),
    # shape(10,)
    "out":tf.Variable(tf.constant(0.1,shape=[n_classes]))
}

def RNN(X, weights, biases):
    # hidden layer for input to cell
    ###################################################################################
    # 原始的X是3维数据，我们需要把它变成2维数据才能使用weights的矩阵乘法
    # X==>(128 batch * 28 steps, 28 inputs)
    X = tf.reshape(X,[-1,n_inputs])

    # X_in = W*X+b
    X_in = tf.matmul(X, weights["in"]) + biases["in"]
    # X_in ==> (128 batches, 28 steps, 128 hidden) 换回3维
    X_in = tf.reshape(X_in,[-1,n_steps,n_hidden_units])

    # cell
    ####################################################################################
    lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden_units,forget_bias=1.0,state_is_tuple=True)
    init_state = lstm_cell.zero_state(batch_size, dtype=tf.float32) # 初始化全零 state
    outputs, final_state = tf.nn.dynamic_rnn(lstm_cell,X_in,initial_state=init_state,time_major=False)

    # hidden layer for output as the final results
    ####################################################################################
    # 把outputs变成列表[(batch,outputs)...]*steps
    outputs = tf.unstack(tf.transpose(outputs,[1,0,2]))
    # 选取最后一个output
    results = tf.matmul(outputs[-1],weights["out"])+biases["out"]

    return results

logits = RNN(x, weights, biases)
pred = tf.nn.softmax(logits)

# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y))
train_op = tf.train.AdamOptimizer(lr).minimize(cost)

# Evaluate mode
correct_pred = tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))

# Initialize the variables
init = tf.global_variables_initializer()

# start training
with tf.Session() as sess:
    # Run the initializer
    sess.run(init)
    step = 0

    while step*batch_size < training_iters:
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        batch_xs = batch_xs.reshape([batch_size,n_steps,n_inputs])
        sess.run([train_op],feed_dict={x:batch_xs,y:batch_ys})
        if step % 20 ==0:
            loss,acc = sess.run([cost,accuracy],feed_dict={x:batch_xs,y:batch_ys})
            print("step"+str(step)+",Minibatch Loss="+"{:.4f}".format(loss)
                  +",Training Accuracy="+"{:.3f}".format(acc))
        step +=1

    print("Optimization Finished!")

    # calculate accuracy for 128 mnist test image
    test_len = 128
    test_data = mnist.test.images[:test_len].reshape((-1,n_steps,n_inputs))
    test_label = mnist.test.labels[:test_len]
    print("Testing Accuracy:",sess.run(accuracy,feed_dict={x:test_data,y:test_label}))

Reference：

观看莫烦视频笔试，地址：https://morvanzhou.github.io/tutorials/machine-learning/tensorflow/5-08-RNN2/