Tensorflow實現RNN（LSTM）手寫數字識別

CopperDong發表於2018-05-27
RNN
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# 載入資料
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# 輸入圖片是28
n_input = 28
max_time = 28
lstm_size = 100  # 隱藏單元
n_class = 10  # 10個分類
batch_size = 50   # 每次50個樣本
n_batch_size = mnist.train.num_examples // batch_size    # 計算一共有多少批次


# 這裡None表示第一個維度可以是任意長度
# 建立佔位符
x = tf.placeholder(tf.float32,[None, 28*28])
# 正確的標籤
y = tf.placeholder(tf.float32,[None, 10])

# 初始化權重 ,stddev為標準差
weight = tf.Variable(tf.truncated_normal([lstm_size, n_class], stddev=0.1))
# 初始化偏置層
biases = tf.Variable(tf.constant(0.1, shape=[n_class]))


# 定義RNN網路
def RNN(X, weights, biases):
    #  原始資料為[batch_size,28*28]
    # input = [batch_size, max_time, n_input]
    input = tf.reshape(X,[-1, max_time, n_input ])
    # 定義LSTM的基本單元
    lstm_cell = tf.contrib.rnn.BasicLSTMCell(lstm_size)
    # final_state[0] 是cell state
    # final_state[1] 是hidden stat
    outputs, final_state = tf.nn.dynamic_rnn(lstm_cell, input, dtype=tf.float32)
    results = tf.nn.softmax(tf.matmul(final_state[1],weights)+biases)
    return results


# 計算RNN的返回結果
prediction = RNN(x, weight, biases)
# 損失函式
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
# 使用AdamOptimizer進行優化
train_step = tf.train.AdamOptimizer(1e-4).minimize(loss)
# 將結果存下來
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1))
# 計算正確率
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
# 初始化
init = tf.global_variables_initializer()


with tf.Session() as sess:
    sess.run(init)
    for epoch in range(6):
        for batch in range(n_batch_size):
            # 取出下一批次資料
            batch_xs,batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train_step, feed_dict={x: batch_xs,y: batch_ys})
            if(batch%100==0):
                print(str(batch)+"/" + str(n_batch_size))
        acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
        print("Iter" + str(epoch) + " ,Testing Accuracy = " + str(acc))


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