环境装好了之后没有模型训练一下怎么行！
模型不能太大，太大好几天跑不完；模型也不能太小，太小体现不出来花大几千配的机器的性能。
推荐captcha验证码识别模型，大小正合适，下面贴完整代码，如果你的环境是按照我的说明搭的，可以直接跑。
第一个文件：capthca_model.py

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

import tensorflow.compat.v1 as tf
import math

class captchaModel():
    def __init__(self,
                 width = 160,
                 height = 60,
                 char_num = 4,
                 classes = 62):
        self.width = width
        self.height = height
        self.char_num = char_num
        self.classes = classes

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

    def max_pool_2x2(self,x):
        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                              strides=[1, 2, 2, 1], padding='SAME')

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

    def bias_variable(self,shape):
        initial = tf.constant(0.1, shape=shape)
        return tf.Variable(initial)

    def create_model(self,x_images,keep_prob):
        #first layer
        w_conv1 = self.weight_variable([5, 5, 1, 32])
        b_conv1 = self.bias_variable([32])
        h_conv1 = tf.nn.relu(tf.nn.bias_add(self.conv2d(x_images, w_conv1), b_conv1))
        h_pool1 = self.max_pool_2x2(h_conv1)
        h_dropout1 = tf.nn.dropout(h_pool1,keep_prob)
        conv_width = math.ceil(self.width/2)
        conv_height = math.ceil(self.height/2)

        #second layer
        w_conv2 = self.weight_variable([5, 5, 32, 64])
        b_conv2 = self.bias_variable([64])
        h_conv2 = tf.nn.relu(tf.nn.bias_add(self.conv2d(h_dropout1, w_conv2), b_conv2))
        h_pool2 = self.max_pool_2x2(h_conv2)
        h_dropout2 = tf.nn.dropout(h_pool2,keep_prob)
        conv_width = math.ceil(conv_width/2)
        conv_height = math.ceil(conv_height/2)

        #third layer
        w_conv3 = self.weight_variable([5, 5, 64, 64])
        b_conv3 = self.bias_variable([64])
        h_conv3 = tf.nn.relu(tf.nn.bias_add(self.conv2d(h_dropout2, w_conv3), b_conv3))
        h_pool3 = self.max_pool_2x2(h_conv3)
        h_dropout3 = tf.nn.dropout(h_pool3,keep_prob)
        conv_width = math.ceil(conv_width/2)
        conv_height = math.ceil(conv_height/2)

        #first fully layer
        conv_width = int(conv_width)
        conv_height = int(conv_height)
        w_fc1 = self.weight_variable([64*conv_width*conv_height,1024])
        b_fc1 = self.bias_variable([1024])
        h_dropout3_flat = tf.reshape(h_dropout3,[-1,64*conv_width*conv_height])
        h_fc1 = tf.nn.relu(tf.nn.bias_add(tf.matmul(h_dropout3_flat, w_fc1), b_fc1))
        h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

        #second fully layer
        w_fc2 = self.weight_variable([1024,self.char_num*self.classes])
        b_fc2 = self.bias_variable([self.char_num*self.classes])
        y_conv = tf.add(tf.matmul(h_fc1_drop, w_fc2), b_fc2)

        return y_conv

第二个文件，generate_captcha.py

# -*- coding: utf-8 -*

from captcha.image import ImageCaptcha
from PIL import Image
import numpy as np
import random
import string

class generateCaptcha():
    def __init__(self,
                 width = 160,#验证码图片的宽
                 height = 60,#验证码图片的高
                 char_num = 4,#验证码字符个数
                 characters = string.digits + string.ascii_uppercase + string.ascii_lowercase):#验证码组成，数字+大写字母+小写字母
        self.width = width
        self.height = height
        self.char_num = char_num
        self.characters = characters
        self.classes = len(characters)

    def gen_captcha(self,batch_size = 50):
        X = np.zeros([batch_size,self.height,self.width,1])
        img = np.zeros((self.height,self.width),dtype=np.uint8)
        Y = np.zeros([batch_size,self.char_num,self.classes])
        image = ImageCaptcha(width = self.width,height = self.height)

        while True:
            for i in range(batch_size):
                captcha_str = ''.join(random.sample(self.characters,self.char_num))
                img = image.generate_image(captcha_str).convert('L')
                img = np.array(img.getdata())
                X[i] = np.reshape(img,[self.height,self.width,1])/255.0
                for j,ch in enumerate(captcha_str):
                    Y[i,j,self.characters.find(ch)] = 1
            Y = np.reshape(Y,(batch_size,self.char_num*self.classes))
            yield X,Y

    def decode_captcha(self,y):
        y = np.reshape(y,(len(y),self.char_num,self.classes))
        return ''.join(self.characters[x] for x in np.argmax(y,axis = 2)[0,:])

    def get_parameter(self):
        return self.width,self.height,self.char_num,self.characters,self.classes

    def gen_test_captcha(self):
        image = ImageCaptcha(width = self.width,height = self.height)
        captcha_str = ''.join(random.sample(self.characters,self.char_num))
        img = image.generate_image(captcha_str)
        img.save(captcha_str + '.jpg')


if __name__ == '__main__':
    g = generateCaptcha()
    g.gen_test_captcha()

第三个文件：
train_captcha.py

#!/usr/bin/python
import tensorflow.compat.v1 as tf
tf.disable_eager_execution()
import numpy as np
import string
import os
import demo1.generate_captcha as generate_captcha
import demo1.captcha_model as captcha_model
tf.device('/GPU:0')
#os.environ['CUDA_VISIBLE_DEVICES'] = '0'
if __name__ == '__main__':
    from tensorflow.python.client import device_lib

    print(device_lib.list_local_devices())
    captcha = generate_captcha.generateCaptcha()
    width, height, char_num, characters, classes = captcha.get_parameter()

    x = tf.placeholder(tf.float32, [None, height, width, 1])
    y_ = tf.placeholder(tf.float32, [None, char_num * classes])
    keep_prob = tf.placeholder(tf.float32)

    model = captcha_model.captchaModel(width, height, char_num, classes)
    y_conv = model.create_model(x, keep_prob)
    cross_entropy = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y_, logits=y_conv))
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

    predict = tf.reshape(y_conv, [-1, char_num, classes])
    real = tf.reshape(y_, [-1, char_num, classes])
    correct_prediction = tf.equal(tf.argmax(predict, 2), tf.argmax(real, 2))
    correct_prediction = tf.cast(correct_prediction, tf.float32)
    accuracy = tf.reduce_mean(correct_prediction)

    saver = tf.train.Saver()
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        step = 0
        while True:
            batch_x, batch_y = next(captcha.gen_captcha(64))
            _, loss = sess.run([train_step, cross_entropy], feed_dict={x: batch_x, y_: batch_y, keep_prob: 0.75})
            print('step:%d,loss:%f' % (step, loss))
            if step % 100 == 0:
                batch_x_test, batch_y_test = next(captcha.gen_captcha(100))
                acc = sess.run(accuracy, feed_dict={x: batch_x_test, y_: batch_y_test, keep_prob: 1.})
                print('###############################################step:%d,accuracy:%f' % (step, acc))
                if acc > 0.99:
                    saver.save(sess, "capcha_model.ckpt")
                    break
            step += 1

对于这个模型，它调用的api完全是tensorflow1.*版本的api，所以导入包的时候有个细节：
import tensorflow.compat.v1 as tf
下一篇文章我将把模型用tensorflow2.0.0版本重新搭一个，感受感受新版本的api。

这个模型就是简单的CNN模型，三个卷积层，两个全连接层。除了最后一个全连接层，其他4个层都有dropout防止过拟合。
模型在跑的时候，gpu显存沾满了，但是gpu利用率超级低。我也不知道是配置问题，还是模型计算量并不大的原因。先留个坑，等我能解释了，回来再补充。

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