# 开发 | 如何利用微信监管你的TF训练

AI科技评论按：本文作者Coldwings，AI科技评论获其授权发布。

1. 把原本的脚本里网络构成和训练的部分甩到了一个函数nn_train里

def nn_train(wechat_name, param): global lock, running # Lock with lock: running = True # mnist data reading mnist = input_data.read_data_sets("data/", one_hot=True) # Parameters # learning_rate = 0.001 # training_iters = 200000 # batch_size = 128 # display_step = 10 learning_rate, training_iters, batch_size, display_step = param # Network Parameters n_input = 784 # MNIST data input (img shape: 28*28) n_classes = 10 # MNIST total classes (0-9 digits) dropout = 0.75 # Dropout, probability to keep units # tf Graph input x = tf.placeholder(tf.float32, [None, n_input]) y = tf.placeholder(tf.float32, [None, n_classes]) keep_prob = tf.placeholder(tf.float32) #dropout (keep probability) # Create some wrappers for simplicity def conv2d(x, W, b, strides=1): # Conv2D wrapper, with bias and relu activation x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME') x = tf.nn.bias_add(x, b) return tf.nn.relu(x) def maxpool2d(x, k=2): # MaxPool2D wrapper return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME') # Create model def conv_net(x, weights, biases, dropout): # Reshape input picture x = tf.reshape(x, shape=[-1, 28, 28, 1]) # Convolution Layer conv1 = conv2d(x, weights['wc1'], biases['bc1']) # Max Pooling (down-sampling) conv1 = maxpool2d(conv1, k=2) # Convolution Layer conv2 = conv2d(conv1, weights['wc2'], biases['bc2']) # Max Pooling (down-sampling) conv2 = maxpool2d(conv2, k=2) # Fully connected layer # Reshape conv2 output to fit fully connected layer input fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]]) fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1']) fc1 = tf.nn.relu(fc1) # Apply Dropout fc1 = tf.nn.dropout(fc1, dropout) # Output, class prediction out = tf.add(tf.matmul(fc1, weights['out']), biases['out']) return out # Store layers weight & bias weights = { # 5x5 conv, 1 input, 32 outputs 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), # 5x5 conv, 32 inputs, 64 outputs 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), # fully connected, 7*7*64 inputs, 1024 outputs 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])), # 1024 inputs, 10 outputs (class prediction) 'out': tf.Variable(tf.random_normal([1024, n_classes])) } biases = { 'bc1': tf.Variable(tf.random_normal([32])), 'bc2': tf.Variable(tf.random_normal([64])), 'bd1': tf.Variable(tf.random_normal([1024])), 'out': tf.Variable(tf.random_normal([n_classes])) } # Construct model pred = conv_net(x, weights, biases, keep_prob) # Define loss and optimizer cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Evaluate model correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Initializing the variables init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: sess.run(init) step = 1 # Keep training until reach max iterations print('Wait for lock') with lock: run_state = running print('Start') while step * batch_size < training_iters and run_state: batch_x, batch_y = mnist.train.next_batch(batch_size) # Run optimization op (backprop) sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, keep_prob: dropout}) if step % display_step == 0: # Calculate batch loss and accuracy loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x, y: batch_y, keep_prob: 1.}) print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ "{:.6f}".format(loss) + ", Training Accuracy= " + \ "{:.5f}".format(acc)) itchat.send("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ "{:.6f}".format(loss) + ", Training Accuracy= " + \ "{:.5f}".format(acc), wechat_name) step += 1 with lock: run_state = running print("Optimization Finished!") itchat.send("Optimization Finished!", wechat_name) # Calculate accuracy for 256 mnist test images print("Testing Accuracy:", \ sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.})) itchat.send("Testing Accuracy: %s" % sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.}), wechat_name) with lock: running = False

@itchat.msg_register([itchat.content.TEXT]) def chat_trigger(msg): global lock, running, learning_rate, training_iters, batch_size, display_step if msg['Text'] == u'开始': print('Starting') with lock: run_state = running if not run_state: try: threading.Thread(target=nn_train, args=(msg['FromUserName'], (learning_rate, training_iters, batch_size, display_step))).start() except: msg.reply('Running')

@itchat.msg_register([itchat.content.TEXT]) def chat_trigger(msg): global lock, running, learning_rate, training_iters, batch_size, display_step if msg['Text'] == u'开始': print('Starting') with lock: run_state = running if not run_state: try: threading.Thread(target=nn_train, args=(msg['FromUserName'], (learning_rate, training_iters, batch_size, display_step))).start() except: msg.reply('Running') elif msg['Text'] == u'停止': print('Stopping') with lock: running = False elif msg['Text'] == u'参数': itchat.send('lr=%f, ti=%d, bs=%d, ds=%d'%(learning_rate, training_iters, batch_size, display_step),msg['FromUserName']) else: try: param = msg['Text'].split() key, value = param print(key, value) if key == 'lr': learning_rate = float(value) elif key == 'ti': training_iters = int(value) elif key == 'bs': batch_size = int(value) elif key == 'ds': display_step = int(value) except: pass

1777 篇文章102 人订阅

0 条评论

## 相关文章

1101

1.4K6

3699

### matlab 数据预处理及常用操作

img_out = repmat(img,[10000 1]);%生成一个1万行的img矩阵 img=zeros(1,1024); %zeros生成为0的矩...

2139

### BZOJ1030: [JSOI2007]文本生成器(AC自动机)

JSOI交给队员ZYX一个任务，编制一个称之为“文本生成器”的电脑软件：该软件的使用者是一些低幼人群， 他们现在使用的是GW文本生成器v6版。该软件可以随机...

772

2006

4778

1182

### tensorflow的数据输入

tensorflow有两种数据输入方法，比较简单的一种是使用feed_dict，这种方法在画graph的时候使用placeholder来站位，在真正run的时候...

1255

4194