如何用微信监管你的TF训练?

作者 | Coldwings

来源 | Coldwings的知乎专栏

之前回答问题在机器学习模型的训练期间,大概几十分钟到几小时不等,大家都会在等实验的时候做什么?

- Coldwings 在知乎的回答 - 说到可以用微信来管着训练,完全不用守着。没想到这么受欢迎……

这里折腾一个例子。以TensorFlow的example中,利用CNN处理MNIST的程序为例,我们做一点点小小的修改。

首先这里放上写完的代码:

#!/usr/bin/env python
# coding: utf-8


'''
A Convolutional Network implementation example using TensorFlow library.
This example is using the MNIST database of handwritten digits
(http://yann.lecun.com/exdb/mnist/)
Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/

Add a itchat controller with multi thread
'''

from __future__ import print_function

import tensorflow as tf

# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data

# 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 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')
    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()

这段代码里面,我所做的修改主要是:

0.导入了itchat和threading

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

这里大部分是跟原本的代码一样的,不过首先所有print的地方都加了个itchat.send来输出日志,此外加了个带锁的状态量running用来做运行开关。此外,部分参数是通过函数参数传入的。

然后呢,写了个itchat的handler

@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')

作用是,如果收到微信消息,内容为『开始』,那就跑训练的函数(当然,为了防止阻塞,放在了另一个线程里)

最后再在脚本主流程里使用itchat登录微信并且启动itchat的服务,这样就实现了基本的控制。

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

但是我们不满足于此,我还希望可以对流程进行一些控制,对参数进行一些修改,于是乎:

@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

通过这个,我们可以在epoch中途停止(因为nn_train里通过检查running标志来确定是否需要停下来),也可以在训练开始前调整learning_rate等几个参数。

实在是很简单……

原文地址: https://zhuanlan.zhihu.com/p/25597975

原文发布于微信公众号 - 人工智能头条(AI_Thinker)

原文发表时间:2018-06-16

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