原文链接: tf 文字识别 多个文字 data 数据流

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两万次训练, 对于这种样式的文字, 已经有了几乎百分之八十的概率识别成功

三万步基本九成左右

使用adam学习率设为0.01 时不太容易收敛, 需要使用小学习率0.001

批次大小也很关键, 太小的话会不容易收敛, 但由于内存限制, 只能使用差不多大小....尽量使用能选择的最大批次在不OOM的情况下

全连接层也可以这样使用, 需要确定最后一个维度的信息

import tensorflow as tf
from tensorflow.contrib import slimx = tf.placeholder(tf.float32, (None, 32, 4))net = slim.fully_connected(x, 64)
print(net.shape)  # (?, 32, 64)for i in tf.global_variables():print(i)# <tf.Variable 'fully_connected/weights:0' shape=(4, 64) dtype=float32_ref>
# <tf.Variable 'fully_connected/biases:0' shape=(64,) dtype=float32_ref>

相比较先展开后接一个全连接再reshape的方式, 参数量少了几个数量级!!!!!

import tensorflow as tf
from tensorflow.contrib import slimx = tf.placeholder(tf.float32, (4, 32, 4))
x = slim.flatten(x)
print(x.shape)  # (4, 128)
net = slim.fully_connected(x, (32 * 64))
print(net.shape)  # (4, 2048)
net = tf.reshape(net, (4, 32, 64))
print(net.shape)  # (4, 32, 64)for i in tf.global_variables():print(i)# <tf.Variable 'fully_connected/weights:0' shape=(128, 2048) dtype=float32_ref>
# <tf.Variable 'fully_connected/biases:0' shape=(2048,) dtype=float32_ref>

完整代码

# encoding:utf-8
import tensorflow as tf
import numpy as np
from tensorflow.contrib import slim
from captcha.image import ImageCaptcha
from PIL import Image
import matplotlib.pyplot as plt
import timeTRAIN_STEP = 1000000
SHOW_STEP = 500
SAVE_STEP = 20
LR = 0.001
SAVE_PB_PATH = './pb'
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))
TEXT_INDEX = list(range(len(TEXT)))
N_OUTPUTS = len(TEXT)
BATCH_SIZE = 24
TEXT_LENGTH = 4
IMAGE_WIDTH = 180
IMAGE_HEIGHT = 64
IMAGE_SHAPE = (IMAGE_HEIGHT, IMAGE_WIDTH, 3)print("N_OUTPUTS", N_OUTPUTS)def random_captcha_text(text_length=1):index = np.random.choice(TEXT_INDEX, text_length)text = TEXT[index]return text, indexdef gen_single(random=True):while True:fonts = ["./font/ios10.ttf","./font/small.TTF",# "./font/f1.TTF","./font/STSONG.TTF"]image = ImageCaptcha(width=IMAGE_WIDTH, height=IMAGE_HEIGHT, fonts=fonts)captcha_text, index = random_captcha_text(text_length=TEXT_LENGTH)if random:#  获得随机生成的验证码#  把验证码列表转为字符串captcha_text = ''.join(captcha_text)#  生成验证码captcha = image.generate(captcha_text)img = Image.open(captcha)img = np.array(img)else:captcha = image.create_captcha_image(captcha_text, (0, 0, 255), (255, 255, 255))img = np.array(captcha)yield img, indexdef gen_batch(batch_size=BATCH_SIZE):images = []labels = []for _ in range(batch_size):image, label = gen_single()images.append(image)labels.append(label)return np.stack(images), np.stack(labels)def parse(img, label):img = tf.cast(img, tf.float32)return pre(img), labeldef pre(img):return img / 127.5 - 1def post(img):img = (img + 1.) * 127.5return np.clip(img, 0, 255).astype(np.uint8)# label (32,)
def label2text(label):labels = TEXT[label]return labelsdef get_iterator():# 需要使用元组,使用列表表示shapes时会报无法hash化的错误data = tf.data.Dataset.from_generator(gen_single, (tf.uint8, tf.int64), (IMAGE_SHAPE, (TEXT_LENGTH,)))data = data.map(parse, num_parallel_calls=6).batch(BATCH_SIZE)# data = data.batch(BATCH_SIZE)data = data.prefetch(BATCH_SIZE)return data.make_initializable_iterator()def get_block(in_x, num_outputs=32, kernel_size=1, stride=1):with slim.arg_scope([slim.conv2d,slim.separable_conv2d],activation_fn=tf.nn.leaky_relu# activation_fn=tf.nn.relu6):#  inceptionnet1 = slim.conv2d(in_x, num_outputs=num_outputs, kernel_size=1, stride=stride)net1 = slim.batch_norm(net1)net3 = slim.conv2d(in_x, num_outputs=num_outputs, kernel_size=3, stride=stride)net3 = slim.batch_norm(net3)net5 = slim.conv2d(in_x, num_outputs=num_outputs, kernel_size=5, stride=stride)net5 = slim.batch_norm(net5)net = tf.concat([net1, net3, net5], axis=3)net = slim.conv2d(net, num_outputs=num_outputs, kernel_size=1, stride=2)net = slim.batch_norm(net)# # net = slim.max_pool2d(net, 3)# net = slim.conv2d(net, num_outputs, kernel_size, 2)# res# net1 = slim.conv2d(in_x, num_outputs, kernel_size=3, stride=stride)# net2 = slim.conv2d(net1, num_outputs, kernel_size=3, stride=stride)# net3 = slim.conv2d(net2, num_outputs, kernel_size=3, stride=stride)# net = net3 + net1# net = slim.conv2d(net, num_outputs, kernel_size, 2)return netdef build_net(in_x):net = get_block(in_x, 128)print(net.shape)net = get_block(net, 128)print(net.shape)net = get_block(net, 256)print(net.shape)net = get_block(net, 256)print(net.shape)net = slim.flatten(net)net = slim.batch_norm(net)print(net.shape)with slim.arg_scope([slim.conv2d,slim.fully_connected],activation_fn=tf.nn.leaky_relu# activation_fn=tf.nn.relu6):net = slim.fully_connected(net, 1024)net = slim.batch_norm(net)net = slim.fully_connected(net, TEXT_LENGTH * 1024)net = tf.reshape(net, (BATCH_SIZE, TEXT_LENGTH, 1024))net = slim.batch_norm(net)net = slim.fully_connected(net, N_OUTPUTS)print('net', net.shape)return netdef train():iterator = get_iterator()images, labels = iterator.get_next()net = build_net(images)print('labels,net', labels.shape, net.shape)loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=net))train_op = tf.train.AdamOptimizer(LR).minimize(loss)# train_op = tf.train.RMSPropOptimizer(LR).minimize(loss)output = tf.argmax(net, axis=2)print('output', output.shape)prediction = tf.equal(output, labels)print('prediction', prediction.shape)accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32))with tf.Session() as sess:sess.run(tf.global_variables_initializer())sess.run(iterator.initializer)st = time.time()loss_list = []accuracy_list = []for step in range(1, 1 + TRAIN_STEP):_, accuracy_val, loss_val = sess.run([train_op, accuracy, loss])if not step % SHOW_STEP:print("show", step, sess.run([accuracy, loss]))ed = time.time()print('time', ed - st)  # 25sst = edimages_val, labels_val, prediction_val, output_val = sess.run([images, labels, prediction, output])images_val = post(images_val)rows = [np.concatenate(line, 1)for line in np.array_split(images_val, 8)]images_val = np.concatenate(rows, 0)# print(labels_val.shape, prediction_val.shape)plt.imshow(images_val)plt.show()# print(label2text(labels_val))# print(label2text(output_val))plt.plot(accuracy_list, label='accuracy')plt.legend()  # 显示图例plt.show()plt.plot(loss_list, label='loss')plt.legend()  # 显示图例plt.show()if not step % SAVE_STEP:loss_list.append(loss_val)accuracy_list.append(accuracy_val)def show_image():iterator = get_iterator()batch = iterator.get_next()with tf.Session() as sess:sess.run(iterator.initializer)for _ in range(10):image, label = sess.run(batch)print(image.shape, label.shape)print(label2text(label))rows = [np.concatenate(row, 1)for row in np.split(image, 8)]image = np.concatenate(rows, 0)print(image.shape)image = post(image)plt.imshow(image)plt.show()def main():train()# show_image()if __name__ == '__main__':main()