使用微信监管你的TF训练

以TensorFlow的example中，利用CNN处理MNIST的程序为例，我们做了下面一点点小小的修改。
1，首先导入了itchat和threading两个包分别用于微信和县线程（因为要有一条线程专门负责接收微信消息，另一个线程运行TF程序）；
2，写了个itchat的handler。作用是，如果收到微信消息，解析消息内容，然后执行相应的操作。（开始，停止，参数等）
3，将原本程序在console里输出的内容使用itchat发送到手机短的微信上。这样就可以方便监管，可以在程序运行过程中查看损失、准确度等信息，也可以实现早停。

这里放上写完的代码：

# coding: utf-8from __future__ import print_functionimport tensorflow as tf# Import MNIST datafrom tensorflow.examples.tutorials.mnist import input_data# Import itchat & threadingimport itchatimport threading# Create a running status flaglock = threading.Lock()running = False# Parameterslearning_rate = 0.001training_iters = 200000batch_size = 128display_step = 10def 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 = [e-mail]_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:            passif __name__ == '__main__':    itchat.auto_login(hotReload=True)    itchat.run()