Use WeChat to Monitor Your Network

平时，大家自己的机器模型在训练期间（特别是深度网络），训练时间通常几小时到十几小时不等，甚至可能会花上好几天，那么在这段时间，你们又会干些什么事情呢？

作为程序员，这里提供一个「有趣的」方式，用你的微信来监控你的模型在训练期间的一举一动。

大概的效果是：

程序用到的主角是 Python 中的微信个人号接口 itchat。What’s itchat? （itchat 的介绍及安装过程）

这次，我们要监控的模型是先前提到过的 基于 MNIST 手写体数据集的「CNN」模型。

注意：

1. 文章要求读者事先下载安装好 itchat。
2. 文章不会详细介绍 TensorFlow 以及 Tensorboard 的知识。

Environment

OS: macOS Sierra 10.12.x

Python Version: 3.4.x

TensorFlow: 1.0

itchat: 1.2.3

Code

• Use WeChat to Monitor Your Network（tensorboard 绘图）

# 基于 MNIST 数据集 的 「CNN」（tensorboard 绘图）
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import numpy as np
import scipy

# Import itchat & threading
import itchat
import threading

# Create a running status flag
lock = threading.Lock()
running = False

# Parameters
learning_rate = 0.001
training_iters = 200000
batch_size = 128
display_step = 10

def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev = 0.1)
    return tf.Variable(initial)
    
def bias_variable(shape):
    initial = tf.constant(0.1, shape = shape)
    return tf.Variable(initial)

def conv2d(x, W, strides=1):
    return tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')

def max_pool_2x2(x, k=2):
    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')
    
def variable_summaries(var):
    """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
    with tf.name_scope('summaries'):
        mean = tf.reduce_mean(var)
        tf.summary.scalar('mean', mean)
        with tf.name_scope('stddev'):
            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
        tf.summary.scalar('stddev', stddev)
        tf.summary.scalar('max', tf.reduce_max(var))
        tf.summary.scalar('min', tf.reduce_min(var))
        tf.summary.histogram('histogram', var)

def add_layer(input_tensor, weights_shape, biases_shape, layer_name, act = tf.nn.relu, flag = 1):
    """Reusable code for making a simple neural net layer.

    It does a matrix multiply, bias add, and then uses relu to nonlinearize.
    It also sets up name scoping so that the resultant graph is easy to read,
    and adds a number of summary ops.
    """
    with tf.name_scope(layer_name):
        with tf.name_scope('weights'):
            weights = weight_variable(weights_shape)
            variable_summaries(weights)
        with tf.name_scope('biases'):
            biases = bias_variable(biases_shape)
            variable_summaries(biases)
        with tf.name_scope('Wx_plus_b'):
            if flag == 1:
                preactivate = tf.add(conv2d(input_tensor, weights), biases)
            else:
                preactivate = tf.add(tf.matmul(input_tensor, weights), biases)
            tf.summary.histogram('pre_activations', preactivate)
        if act == None:
            outputs = preactivate
        else:
            outputs = act(preactivate, name = 'activation')
            tf.summary.histogram('activation', outputs)
        return outputs

def nn_train(wechat_name, param):
    global lock, running
    # Lock
    with lock:
        running = True  
    # 参数
    learning_rate, training_iters, batch_size, display_step = param
    
    # Import data
    mnist_data_path = 'MNIST_data/'
    mnist = input_data.read_data_sets(mnist_data_path, one_hot = True)
    
    # Network Parameters
    n_input = 28*28 # MNIST data input (img shape: 28*28)
    n_classes = 10 # MNIST total classes (0-9 digits)
    dropout = 0.75 # Dropout, probability to keep units
    
    with tf.name_scope('Input'):
        x = tf.placeholder(tf.float32, [None, n_input], name = 'input_x')
        y_ = tf.placeholder(tf.float32, [None, n_classes], name = 'target_y')
        keep_prob = tf.placeholder(tf.float32, name = 'keep_prob') #dropout (keep probability)

    def cnn_net(x, weights, biases, dropout):
        # Reshape input picture
        x_image = tf.reshape(x, [-1, 28, 28 ,1])
        
        # First Convolutional Layer
        conv_1 = add_layer(x_image, weights['conv1_w'], biases['conv1_b'], 'First_Convolutional_Layer', flag = 1)
        
        # First Pooling Layer
        pool_1 = max_pool_2x2(conv_1)
        
        # Second Convolutional Layer 
        conv_2 = add_layer(pool_1, weights['conv2_w'], biases['conv2_b'], 'Second_Convolutional_Layer', flag = 1)

        # Second Pooling Layer 
        pool_2 = max_pool_2x2(conv_2)

        # Densely Connected Layer
        pool_2_flat = tf.reshape(pool_2, [-1, weight_variable(weights['dc1_w']).get_shape().as_list()[0]])
        dc_1 = add_layer(pool_2_flat, weights['dc1_w'], biases['dc1_b'], 'Densely_Connected_Layer', flag = 0) 
        
        # Dropout
        dc_1_drop = tf.nn.dropout(dc_1, keep_prob)  
        
        # Readout Layer
        y = add_layer(dc_1_drop, weights['out_w'], biases['out_b'], 'Readout_Layer', flag = 0)
        
        return y
    
    # Store layers weight & bias
    weights = {
        # 5x5 conv, 1 input, 32 outputs
        'conv1_w': [5, 5, 1, 32],
        # 5x5 conv, 32 inputs, 64 outputs
        'conv2_w': [5, 5, 32, 64],
        # fully connected, 7*7*64 inputs, 1024 outputs
        'dc1_w': [7*7*64, 1024],
        # 1024 inputs, 10 outputs (class prediction)
        'out_w': [1024, n_classes]
    }

    biases = {
        'conv1_b': [32],
        'conv2_b': [64],
        'dc1_b': [1024],
        'out_b': [n_classes]
    }
    
    y = cnn_net(x, weights, biases, dropout)
    
    # Optimizer
    with tf.name_scope('cost'):
        cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y_,
                        logits = y))
        tf.summary.scalar('cost', cost)
        tf.summary.histogram('cost', cost)
    
    # Train
    with tf.name_scope('train'):
        optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
    
    # Test
    with tf.name_scope('accuracy'):
        with tf.name_scope('correct_prediction'):
            correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
        with tf.name_scope('accuracy'):
            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        tf.summary.scalar('accuracy', accuracy)
        
    sess = tf.InteractiveSession()
    merged = tf.summary.merge_all()
    train_writer = tf.summary.FileWriter('train/', sess.graph)
    test_writer = tf.summary.FileWriter('test/')
    tf.global_variables_initializer().run()

    
    # Train the model, and also write summaries.
    # Every 10th step, measure test-set accuracy, and write test summaries
    # All other steps, run train_step on training data, & add training summaries
    
    # Keep training until reach max iterations
    print('Wait for lock')
    with lock:
        run_state = running
    print('Start')
    
    step = 1
    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:    # Record execution stats
            run_options = tf.RunOptions(trace_level = tf.RunOptions.FULL_TRACE)
            run_metadata = tf.RunMetadata()
            summary, _ = sess.run([merged, optimizer], feed_dict = 
                                    {x: batch_x, y_: batch_y, keep_prob: 1.}, 
                                    options = run_options, run_metadata = run_metadata)
            train_writer.add_run_metadata(run_metadata, 'step %d ' % step)
            train_writer.add_summary(summary, step)
            print('Adding run metadata for', step)

            summary, loss, acc = sess.run([merged, 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), 'filehelper')
        else:
            summary, _ = sess.run([merged, optimizer], feed_dict = {x: batch_x, y_: batch_y, keep_prob: 1.})
            train_writer.add_summary(summary, step)
        step += 1
        with lock:
            run_state = running
    print("Optimization Finished!")
    itchat.send("Optimization Finished!", 'filehelper')

    # Calculate accuracy for 256 mnist test images
    summary, acc = sess.run([merged, accuracy], feed_dict = 
                            {x: mnist.test.images[:256], y_: mnist.test.labels[:256], 
                            keep_prob: 1.} )
    text_writer.add_summary(summary)
    print("Testing Accuracy:", acc)
    itchat.send("Testing Accuracy: %s" % acc, wechat_name)

                
@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


if __name__ == '__main__':
    itchat.auto_login(hotReload=True)
    itchat.run()

大家可以看到，我对先前的代码进行了一些修改。

下面我会对代码中用到 itchat 的部分进行一些简短的说明。

• 代码部分截图：

说明：

1. 首先我导入了 itchat 和 threading。
2. 在原先所有 print 消息的地方，都添加了 itchat.send() 来输出我们的模型训练日志。
3. 加了一个带锁的状态量 running 用来做为发送微信消息的运行开关。
4. 写了一个 itchat 的 handler（就是上图）。其作用就是当程序运行，我们需要在微信中，对自己的微信号发送「开始」，模型才会开始训练，为了防止信息阻塞，所以要用到 threading 将其放在另一个线程当中。在训练的过程中，如果我们觉得结果已到达我们自己的预期，可以微信发送「停止」来停止模型的训练过程。

另外，脚本刚开始运行时，程序会弹出一个包含二维码的图片，我们需要通过微信来扫描该二维码，来登陆微信并启动 itchat 的服务。

程序是包含了 Tensorboard 绘图的，所以等模型训练好，我们依然是可以通过 Tensorboard 来更加详细地查看我们模型的训练过程。

如果看过 itchat 那个连接的读者，可以了解到 itchat 同样是可以发送图片信息的，所以我们可以写额外的脚本在训练的过程中每隔 100 次迭代， plot 到目前为止 loss，acc 等指标的趋势图。在此，我就不再进行拓展了。

至此，我们就可以一边通过微信来监控我们的模型训练过程，一边与身边的朋友们谈笑风生了。

关于各个模块的作用，以及各个变量的意义，我在此就不再赘述了。

如果有读者对于 CNN 卷积神经网络有些陌生或者是遗忘，可以参考我的另外一篇文章 CNN on TensorFlow。

如果读者对 Tensorboard 有所遗忘，可以参考我的另一篇文章 「TensorFlow 1.0」 Tensorboard。