用tensorflow跑RNN实现手写体的识别,下面上两个RNN的结构图
import tensorflow as tf from tensorflow.contrib import rnn import input_data import numpy as np import matplotlib.pyplot as plt print ("Packages imported") mnist = input_data.read_data_sets("data/", one_hot=True) trainimgs, trainlabels, testimgs, testlabels \ = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels ntrain, ntest, dim, nclasses \ = trainimgs.shape[0], testimgs.shape[0], trainimgs.shape[1], trainlabels.shape[1] print ("MNIST loaded") diminput = 28 dimhidden = 128 dimoutput = nclasses nsteps = 28 weights = { 'hidden': tf.Variable(tf.random_normal([diminput, dimhidden])), 'out': tf.Variable(tf.random_normal([dimhidden, dimoutput])) } biases = { 'hidden': tf.Variable(tf.random_normal([dimhidden])), 'out': tf.Variable(tf.random_normal([dimoutput])) } def _RNN(_X,_W,_b,_nsteps,_name): #第一步：转换输入，输入_X是还有batchSize=5的5张28*28图片，需要将输入从 #[batchSize,nsteps,diminput]==>[nsteps,batchSize,diminput] _X = tf.transpose(_X,[1,0,2]) #第二步：reshape _X为[nsteps*batchSize,diminput] _X = tf.reshape(_X,[-1,diminput]) #第三步：input layer -> hidden layer _H = tf.matmul(_X,_W['hidden'])+_b['hidden'] #第四步：将数据切分为‘nsteps’个切片，第i个切片为第i个batch data #tensoflow >0.12 _Hsplit = tf.split(_H,_nsteps,0) #tensoflow <0.12 _Hsplit = tf.split(0,_nsteps,_H) #第五步：计算LSTM final output(_LSTM_O) 和 state(_LSTM_S) #_LSTM_O和_LSTM_S都有‘batchSize’个元素 #_LSTM_O用于预测输出 with tf.variable_scope(_name) as scope: #scope.reuse_variables() # forget_bias = 1.0不忘记数据 ###tensorflow <1.0 # lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(dimhidden,forget_bias = 1.0) #_LSTM_O,_SLTM_S = tf.nn.rnn(lstm_cell,_Hsplit,dtype=tf.float32) ###tensorflow 1.0 lstm_cell = rnn.BasicLSTMCell(dimhidden) _LSTM_O,_LSTM_S = rnn.static_rnn(lstm_cell,_Hsplit,dtype=tf.float32) #第六步：输出,需要最后一个RNN单元作为预测输出所以取_LSTM_O[-1] _O = tf.matmul(_LSTM_O[-1],_W['out'])+_b['out'] return { 'X':_X, 'H':_H, '_Hsplit':_Hsplit, 'LSTM_O':_LSTM_O, 'LSTM_S':_LSTM_S, 'O':_O } print("Network Ready!") learning_rate = 0.001 x = tf.placeholder("float",[None,nsteps,diminput]) y = tf.placeholder("float",[None,dimoutput]) myrnn = _RNN(x,weights,biases,nsteps,'basic1') pred = myrnn['O'] cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y)) optm = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)#Adam accr = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(pred,1),tf.argmax(y,1)),tf.float32)) init = tf.global_variables_initializer() print("Network Ready!") training_epochs = 5 batch_size = 16 display_step = 1 sess = tf.Session() sess.run(init) print ("Start optimization") for epoch in range(training_epochs): avg_cost = 0. #total_batch = int(mnist.train.num_examples/batch_size) total_batch = 100 # Loop over all batches for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) batch_xs = batch_xs.reshape((batch_size, nsteps, diminput)) #print(batch_xs.shape) #print(batch_ys.shape) #batch_ys = batch_ys.reshape((batch_size, dimoutput)) # Fit training using batch data feeds = {x: batch_xs, y: batch_ys} sess.run(optm, feed_dict=feeds) # Compute average loss avg_cost += sess.run(cost, feed_dict=feeds)/total_batch # Display logs per epoch step if epoch % display_step == 0: print ("Epoch: %03d/%03d cost: %.9f" % (epoch, training_epochs, avg_cost)) feeds = {x: batch_xs, y: batch_ys} train_acc = sess.run(accr, feed_dict=feeds) print (" Training accuracy: %.3f" % (train_acc)) testimgs = testimgs.reshape((ntest, nsteps, diminput)) feeds = {x: testimgs, y: testlabels} test_acc = sess.run(accr, feed_dict=feeds) print (" Test accuracy: %.3f" % (test_acc)) print ("Optimization Finished.") 打印结果：
Start optimization Epoch: 000/005 cost: 1.964034666 Training accuracy: 0.438 Test accuracy: 0.410 Epoch: 001/005 cost: 1.420921708 Training accuracy: 0.562 Test accuracy: 0.475 Epoch: 002/005 cost: 1.189191381 Training accuracy: 0.750 Test accuracy: 0.482 Epoch: 003/005 cost: 1.054024348 Training accuracy: 0.875 Test accuracy: 0.597 Epoch: 004/005 cost: 0.930983863 Training accuracy: 0.562 Test accuracy: 0.606 Optimization Finished.
文章来源: RNN识别mnist数据集