TensorFlow自然语言处理的例子：利用tensorflow实现神经网络进行手写数字识别。
import struct import gzip import os from six.moves.urllib.request import urlretrieve import numpy as np import tensorflow as tf import matplotlib.pyplot as plt class ManualNN: def __init__(self): self.WEIGHTS_STRING = 'weights' self.BIAS_STRING = 'bias' self.batch_size = 100 img_width = 28 img_height = 28 self.input_size = img_height * img_width self.num_labels = 10 tf.reset_default_graph() #下载数据集 def maybe_download(self,url,filename,expected_bytes,force=False): if force or not os.path.exists(filename): print('Attempting to download:', filename) filename,_ = urlretrieve(url+filename,filename) print('\nDownload Complete!') statInfo = os.stat(filename) if statInfo.st_size == expected_bytes: print('Found and verified', filename) else: raise Exception( 'Failed to verify ' + filename + '. Can you get to it with a browser?') return filename #读取数据 def read_mnist(self,fname_img,fname_lbl): print('\nReading files %s and %s' % (fname_img, fname_lbl)) with gzip.open(fname_img) as fimg: #采用Big Endian的方式读取四个整数，magic为magic number用于表示文件格式 magic,num,rows,cols = struct.unpack(">IIII", fimg.read(16)) print(num,rows,cols) #frombuffer将data以流的形式读入转化成ndarray对象 img = (np.frombuffer(fimg.read(num * rows * cols), dtype=np.uint8).reshape(num, rows * cols)).astype( np.float32) img = (img - np.mean(img)) / np.std(img) print(img.shape) with gzip.open(fname_lbl) as flbl: # flbl.read(8) reads upto 8 bytes magic, num = struct.unpack(">II", flbl.read(8)) lbl = np.frombuffer(flbl.read(num), dtype=np.int8) print('(Labels) Returned a tensor of shape: %s' % lbl.shape) print('Sample labels: ', lbl[:10]) return img, lbl def train(self,train_inputs,train_labels,test_inputs,test_labels): tf_inputs = tf.placeholder(shape=[self.batch_size,self.input_size],dtype=tf.float32,name='inputs') tf_labels = tf.placeholder(shape=[self.batch_size,self.num_labels],dtype=tf.float32,name='labels') #定义作用域重用变量 self.define_net_param() tf_loss = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.inference(tf_inputs), labels=tf_labels)) tf_loss_mini = tf.train.MomentumOptimizer(momentum=0.9, learning_rate=0.01).minimize(tf_loss) tf_prediction = tf.nn.softmax(self.inference(tf_inputs)) NUM_EPOCHS = 50 session = tf.InteractiveSession() tf.global_variables_initializer().run() test_accuracy_over_time = [] train_loss_over_time = [] for epoch in range(NUM_EPOCHS): train_loss = [] #每次训练批量的数据，100条 for step in range(train_inputs.shape[0]//self.batch_size): #[100,10]的标签矩阵 labels_one_hot = np.zeros((self.batch_size,self.num_labels),dtype=np.float32) #设置每条数据的标签，即取出前100个标签，train_label数据格式[2,3,5,7,8]，则根据train_label索引设置每一行何处为1，从而设置标签 labels_one_hot[np.arange(self.batch_size),train_labels[step*self.batch_size:(step+1)*self.batch_size]] = 1.0 if epoch == 0 and step == 0: print('Sample labels (one-hot)') print(labels_one_hot[:10]) print() loss,_ = session.run([tf_loss,tf_loss_mini],feed_dict={ tf_inputs: train_inputs[step * self.batch_size: (step + 1) * self.batch_size, :], tf_labels: labels_one_hot }) train_loss.append(loss) test_accuracy = [] # Testing Phase for step in range(test_inputs.shape[0] // self.batch_size): test_predictions = session.run(tf_prediction, feed_dict={ tf_inputs: test_inputs[step * self.batch_size: (step + 1) * self.batch_size, :]}) batch_test_accuracy = self.accuracy(test_predictions, test_labels[step * self.batch_size: (step + 1) * self.batch_size]) test_accuracy.append(batch_test_accuracy) print('Average train loss for the %d epoch: %.3f\n' % (epoch + 1, np.mean(train_loss))) train_loss_over_time.append(np.mean(train_loss)) print('\tAverage test accuracy for the %d epoch: %.2f\n' % (epoch + 1, np.mean(test_accuracy) * 100.0)) test_accuracy_over_time.append(np.mean(test_accuracy) * 100) session.close() #可视化 x_axis = np.arange(len(train_loss_over_time)) fig, ax = plt.subplots(nrows=1, ncols=2) fig.set_size_inches(w=25, h=5) ax[0].plot(x_axis, train_loss_over_time) ax[0].set_xlabel('Epochs', fontsize=18) ax[0].set_ylabel('Average train loss', fontsize=18) ax[0].set_title('Training Loss over Time', fontsize=20) ax[1].plot(x_axis, test_accuracy_over_time) ax[1].set_xlabel('Epochs', fontsize=18) ax[1].set_ylabel('Test accuracy', fontsize=18) ax[1].set_title('Test Accuracy over Time', fontsize=20) fig.savefig('mnist_stats.jpg') def accuracy(self,predictions,labels): return np.sum(np.argmax(predictions,axis=1).flatten() == labels.flatten()) / self.batch_size #初始化作用域重用变量layer1/w、layer1/b def define_net_param(self): #输入到隐层1 with tf.variable_scope('layer1'): # tf.get_variable(self.WEIGHT_STRING,shape=[self.input_size,500],initializer=tf.random_normal_initializer(0,0.02)) # tf.get_variable(self.BIAS_STRING,shape=[500],initializer=tf.random_uniform_initializer(0,0.01)) tf.get_variable(self.WEIGHTS_STRING, shape=[self.input_size, 500], initializer=tf.random_normal_initializer(0, 0.02)) tf.get_variable(self.BIAS_STRING, shape=[500], initializer=tf.random_uniform_initializer(0, 0.01)) #隐层1到隐层2 with tf.variable_scope('layer2'): tf.get_variable(self.WEIGHTS_STRING,shape=[500,250],initializer=tf.random_normal_initializer(0,0.02)) tf.get_variable(self.BIAS_STRING,shape=[250],initializer=tf.random_uniform_initializer(0,0.01)) #隐层2到输出层 with tf.variable_scope('output'): tf.get_variable(self.WEIGHTS_STRING,shape=[250,10],initializer=tf.random_normal_initializer(0,0.02)) tf.get_variable(self.BIAS_STRING,shape=[10],initializer=tf.random_uniform_initializer(0,0.01)) def inference(self,x): with tf.variable_scope('layer1',reuse=True): w,b = tf.get_variable(self.WEIGHTS_STRING),tf.get_variable(self.BIAS_STRING) tf_h1 = tf.nn.relu(tf.matmul(x,w)+b,name='hidden1') with tf.variable_scope('layer2',reuse=True): w,b = tf.get_variable(self.WEIGHTS_STRING),tf.get_variable(self.BIAS_STRING) tf_h2 = tf.nn.relu(tf.matmul(tf_h1,w)+b,name='hidden1') with tf.variable_scope('output',reuse=True): w,b = tf.get_variable(self.WEIGHTS_STRING),tf.get_variable(self.BIAS_STRING) tf_logits = tf.nn.bias_add(tf.matmul(tf_h2,w),b,name='logits') return tf_logits if __name__ == '__main__': mn = ManualNN() # Download data if needed # url = 'http://yann.lecun.com/exdb/mnist/' # training data # mn.maybe_download(url, 'train-images-idx3-ubyte.gz', 9912422) # mn.maybe_download(url, 'train-labels-idx1-ubyte.gz', 28881) # testing data # mn.maybe_download(url, 't10k-images-idx3-ubyte.gz', 1648877) # mn.maybe_download(url, 't10k-labels-idx1-ubyte.gz', 4542) # Read the training and testing data train_inputs, train_labels = mn.read_mnist('train-images-idx3-ubyte.gz', 'train-labels-idx1-ubyte.gz') test_inputs, test_labels = mn.read_mnist('t10k-images-idx3-ubyte.gz', 't10k-labels-idx1-ubyte.gz') mn.train(train_inputs,train_labels,test_inputs,test_labels)