rnn-mnist 没有注释iter0, testing accuracy=0.9774 iter1, testing accuracy=0.9831 iter2, testing accuracy=0.984 iter3, testing accuracy=0.984 iter4, testing accuracy=0.9843 iter5, testing accuracy=0.985

木俊 2018-08-01

展开全文

代码调试过程中的错误：<ipython-input-8-a4f3de4a35a8> in RNN(X, weights, biases)
     38     inputs = tf.reshape(X,[-1,max_time,n_inputs])
     39     #定义LSTM基本CELL
---> 40     lstm_cell = tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(lstm_size)
     41     #lstm_cell = rnn.BasicLSTMCell(lstm_size)
     42     # final_state[0]是cell state

AttributeError: module 'tensorflow.contrib.rnn' has no attribute 'core_rnn_cell'

解决：

#1.0版本改了很多
            #原代码是这样的：
            lstm_cell = tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(lstm_size)
            #应该改为：
            from tensorflow.contrib import rnn

lstm_cell = rnn.BasicLSTMCell(lstm_size)


import tensorflow as tf
from tensorflow.contrib import rnn
from tensorflow.examples.tutorials.mnist import input_data
#载入数据
mnist = input_data.read_data_sets("/home/mj/MINIST_data", one_hot=True)
#输入图片是28*28
n_inputs=28#
max_time=28
lstm_size=100
n_classes=10
batch_size=50
n_batch=mnist.train.num_examples
x=tf.placeholder(tf.float32,[None,784])
y=tf.placeholder(tf.float32,[None,10])

weights=tf.Variable(tf.truncated_normal([lstm_size,n_classes],stddev=0.1))

biases=tf.Variable(tf.constant(0.1,shape=[n_classes]))

def Rnn(X,weights,biases):
    inputs=tf.reshape(X,[-1,max_time,n_inputs])
    #lstm_cell=tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(lstm_size)
    lstm_cell = rnn.BasicLSTMCell(lstm_size)
    outputs,final_state=tf.nn.dynamic_rnn(lstm_cell,inputs,dtype=tf.float32)
    results=tf.nn.softmax(tf.matmul(final_state[1],weights)+biases)
    return results

prediction=Rnn(x,weights,biases)
cross_entropy=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=prediction,labels=y))
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
init=tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for epoch in range(6):
        for batch in range(n_batch):
            batch_xs,batch_ys=mnist.train.next_batch(batch_size)
            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
        acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        print("iter"+str(epoch)+", testing accuracy="+str(acc))