上兩篇詳細的說明了驗證碼的識別，不過我們采用的是方法二，下面采用方法一。注意和方法二的區別。
驗證碼識別方法一：
把標簽轉為向量，向量長度為40。（4位數字驗證碼）
驗證碼的生成和tf.record的制作這部分代碼不變。
一、訓練識別模型
首先新建一個nets2文件夾，使用文件夾下的nets_factory.py、alexnet.py，我們使用的是原始的alexnet，不需要修改
nets_factory.py
from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import tensorflow as tf from nets2 import alexnet from nets2 import cifarnet from nets2 import inception from nets2 import lenet from nets2 import overfeat from nets2 import resnet_v1 from nets2 import resnet_v2 from nets2 import vgg slim = tf.contrib.slim networks_map = {'alexnet_v2': alexnet.alexnet_v2, 'cifarnet': cifarnet.cifarnet, 'overfeat': overfeat.overfeat, 'vgg_a': vgg.vgg_a, 'vgg_16': vgg.vgg_16, 'vgg_19': vgg.vgg_19, 'inception_v1': inception.inception_v1, 'inception_v2': inception.inception_v2, 'inception_v3': inception.inception_v3, 'inception_v4': inception.inception_v4, 'inception_resnet_v2': inception.inception_resnet_v2, 'lenet': lenet.lenet, 'resnet_v1_50': resnet_v1.resnet_v1_50, 'resnet_v1_101': resnet_v1.resnet_v1_101, 'resnet_v1_152': resnet_v1.resnet_v1_152, 'resnet_v1_200': resnet_v1.resnet_v1_200, 'resnet_v2_50': resnet_v2.resnet_v2_50, 'resnet_v2_101': resnet_v2.resnet_v2_101, 'resnet_v2_152': resnet_v2.resnet_v2_152, 'resnet_v2_200': resnet_v2.resnet_v2_200, } arg_scopes_map = {'alexnet_v2': alexnet.alexnet_v2_arg_scope, 'cifarnet': cifarnet.cifarnet_arg_scope, 'overfeat': overfeat.overfeat_arg_scope, 'vgg_a': vgg.vgg_arg_scope, 'vgg_16': vgg.vgg_arg_scope, 'vgg_19': vgg.vgg_arg_scope, 'inception_v1': inception.inception_v3_arg_scope, 'inception_v2': inception.inception_v3_arg_scope, 'inception_v3': inception.inception_v3_arg_scope, 'inception_v4': inception.inception_v4_arg_scope, 'inception_resnet_v2': inception.inception_resnet_v2_arg_scope, 'lenet': lenet.lenet_arg_scope, 'resnet_v1_50': resnet_v1.resnet_arg_scope, 'resnet_v1_101': resnet_v1.resnet_arg_scope, 'resnet_v1_152': resnet_v1.resnet_arg_scope, 'resnet_v1_200': resnet_v1.resnet_arg_scope, 'resnet_v2_50': resnet_v2.resnet_arg_scope, 'resnet_v2_101': resnet_v2.resnet_arg_scope, 'resnet_v2_152': resnet_v2.resnet_arg_scope, 'resnet_v2_200': resnet_v2.resnet_arg_scope, } def get_network_fn(name, num_classes, weight_decay=0.0, is_training=False): """Returns a network_fn such as `logits, end_points = network_fn(images)`. Args: name: The name of the network. num_classes: The number of classes to use for classification. weight_decay: The l2 coefficient for the model weights. is_training: `True` if the model is being used for training and `False` otherwise. Returns: network_fn: A function that applies the model to a batch of images. It has the following signature: logits, end_points = network_fn(images) Raises: ValueError: If network `name` is not recognized. """ if name not in networks_map: raise ValueError('Name of network unknown %s' % name) func = networks_map[name] @functools.wraps(func) def network_fn(images): arg_scope = arg_scopes_map[name](weight_decay=weight_decay) with slim.arg_scope(arg_scope): return func(images, num_classes, is_training=is_training) if hasattr(func, 'default_image_size'): network_fn.default_image_size = func.default_image_size return network_fn
主要就是將原來的nets改成nets2，在代碼中我已高亮顯示
alexnet.py
from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.contrib import layers from tensorflow.contrib.framework.python.ops import arg_scope from tensorflow.contrib.layers.python.layers import layers as layers_lib from tensorflow.contrib.layers.python.layers import regularizers from tensorflow.contrib.layers.python.layers import utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import variable_scope trunc_normal = lambda stddev: init_ops.truncated_normal_initializer(0.0, stddev) def alexnet_v2_arg_scope(weight_decay=0.0005): with arg_scope( [layers.conv2d, layers_lib.fully_connected], activation_fn=nn_ops.relu, biases_initializer=init_ops.constant_initializer(0.1), weights_regularizer=regularizers.l2_regularizer(weight_decay)): with arg_scope([layers.conv2d], padding='SAME'): with arg_scope([layers_lib.max_pool2d], padding='VALID') as arg_sc: return arg_sc def alexnet_v2(inputs, num_classes=1000, is_training=True, dropout_keep_prob=0.5, spatial_squeeze=True, scope='alexnet_v2'): """AlexNet version 2. Described in: http://arxiv.org/pdf/1404.5997v2.pdf Parameters from: github.com/akrizhevsky/cuda-convnet2/blob/master/layers/ layers-imagenet-1gpu.cfg Note: All the fully_connected layers have been transformed to conv2d layers. To use in classification mode, resize input to 224x224. To use in fully convolutional mode, set spatial_squeeze to false. The LRN layers have been removed and change the initializers from random_normal_initializer to xavier_initializer. Args: inputs: a tensor of size [batch_size, height, width, channels]. num_classes: number of predicted classes. is_training: whether or not the model is being trained. dropout_keep_prob: the probability that activations are kept in the dropout layers during training. spatial_squeeze: whether or not should squeeze the spatial dimensions of the outputs. Useful to remove unnecessary dimensions for classification. scope: Optional scope for the variables. Returns: the last op containing the log predictions and end_points dict. """ with variable_scope.variable_scope(scope, 'alexnet_v2', [inputs]) as sc: end_points_collection = sc.original_name_scope + '_end_points' # Collect outputs for conv2d, fully_connected and max_pool2d. with arg_scope( [layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d], outputs_collections=[end_points_collection]): net = layers.conv2d( inputs, 64, [11, 11], 4, padding='VALID', scope='conv1') net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool1') net = layers.conv2d(net, 192, [5, 5], scope='conv2') net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool2') net = layers.conv2d(net, 384, [3, 3], scope='conv3') net = layers.conv2d(net, 384, [3, 3], scope='conv4') net = layers.conv2d(net, 256, [3, 3], scope='conv5') net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool5') # Use conv2d instead of fully_connected layers. with arg_scope( [layers.conv2d], weights_initializer=trunc_normal(0.005), biases_initializer=init_ops.constant_initializer(0.1)): net = layers.conv2d(net, 4096, [5, 5], padding='VALID', scope='fc6') net = layers_lib.dropout( net, dropout_keep_prob, is_training=is_training, scope='dropout6') net = layers.conv2d(net, 4096, [1, 1], scope='fc7') net = layers_lib.dropout( net, dropout_keep_prob, is_training=is_training, scope='dropout7') net = layers.conv2d( net, num_classes, [1, 1], activation_fn=None, normalizer_fn=None, biases_initializer=init_ops.zeros_initializer(), scope='fc8') # Convert end_points_collection into a end_point dict. end_points = utils.convert_collection_to_dict(end_points_collection) if spatial_squeeze: net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed') end_points[sc.name + '/fc8'] = net return net, end_points alexnet_v2.default_image_size = 224
直接使用alexnet的源碼，不需任何修改。
train.py
import os import tensorflow as tf from PIL import Image from nets2 import nets_factory import numpy as np # 不同字符數量 CHAR_SET_LEN = 10 # 圖片高度 IMAGE_HEIGHT = 60 # 圖片寬度 IMAGE_WIDTH = 160 # 批次 BATCH_SIZE = 25 # tfrecord文件存放路徑 TFRECORD_FILE = "F:/PyCharm-projects/第十周/train.tfrecords" # placeholder x = tf.placeholder(tf.float32, [None, 224, 224]) y0 = tf.placeholder(tf.float32, [None]) y1 = tf.placeholder(tf.float32, [None]) y2 = tf.placeholder(tf.float32, [None]) y3 = tf.placeholder(tf.float32, [None]) # 學習率 lr = tf.Variable(0.003, dtype=tf.float32) # 從tfrecord讀出數據 def read_and_decode(filename): # 根據文件名生成一個隊列 filename_queue = tf.train.string_input_producer([filename]) reader = tf.TFRecordReader() # 返回文件名和文件 _, serialized_example = reader.read(filename_queue) features = tf.parse_single_example(serialized_example, features={ 'image': tf.FixedLenFeature([], tf.string), 'label0': tf.FixedLenFeature([], tf.int64), 'label1': tf.FixedLenFeature([], tf.int64), 'label2': tf.FixedLenFeature([], tf.int64), 'label3': tf.FixedLenFeature([], tf.int64), }) # 獲取圖片數據 image = tf.decode_raw(features['image'], tf.uint8) # tf.train.shuffle_batch必須確定shape image = tf.reshape(image, [224, 224]) # 圖片預處理 image = tf.cast(image, tf.float32) / 255.0 image = tf.subtract(image, 0.5) image = tf.multiply(image, 2.0) # 獲取label label0 = tf.cast(features['label0'], tf.int32) label1 = tf.cast(features['label1'], tf.int32) label2 = tf.cast(features['label2'], tf.int32) label3 = tf.cast(features['label3'], tf.int32) return image, label0, label1, label2, label3 # 獲取圖片數據和標簽 image, label0, label1, label2, label3 = read_and_decode(TFRECORD_FILE) # 使用shuffle_batch可以隨機打亂 image_batch, label_batch0, label_batch1, label_batch2, label_batch3 = tf.train.shuffle_batch( [image, label0, label1, label2, label3], batch_size=BATCH_SIZE, capacity=50000, min_after_dequeue=10000, num_threads=1) # 定義網絡結構 train_network_fn = nets_factory.get_network_fn( 'alexnet_v2', num_classes=CHAR_SET_LEN*4, weight_decay=0.0005, is_training=True) with tf.Session() as sess: # inputs: a tensor of size [batch_size, height, width, channels] X = tf.reshape(x, [BATCH_SIZE, 224, 224, 1]) # 數據輸入網絡得到輸出值 logits, end_points = train_network_fn(X) # 把標簽轉成one_hot的形式 one_hot_labels0 = tf.one_hot(indices=tf.cast(y0, tf.int32), depth=CHAR_SET_LEN) one_hot_labels1 = tf.one_hot(indices=tf.cast(y1, tf.int32), depth=CHAR_SET_LEN) one_hot_labels2 = tf.one_hot(indices=tf.cast(y2, tf.int32), depth=CHAR_SET_LEN) one_hot_labels3 = tf.one_hot(indices=tf.cast(y3, tf.int32), depth=CHAR_SET_LEN) # 把標簽轉成長度為40的向量 label_40 = tf.concat([one_hot_labels0, one_hot_labels1, one_hot_labels2, one_hot_labels3], 1) # 計算loss loss_40 = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=label_40)) # 優化total_loss optimizer = tf.train.AdamOptimizer(learning_rate=lr).minimize(loss_40) # 計算准確率 correct_prediction_40 = tf.equal(tf.argmax(one_hot_labels0, 1), tf.argmax(logits, 1)) accuracy_40 = tf.reduce_mean(tf.cast(correct_prediction_40, tf.float32)) # 用於保存模型 saver = tf.train.Saver() # 初始化 sess.run(tf.global_variables_initializer()) # 創建一個協調器，管理線程 coord = tf.train.Coordinator() # 啟動QueueRunner, 此時文件名隊列已經進隊 threads = tf.train.start_queue_runners(sess=sess, coord=coord) for i in range(4001): # 獲取一個批次的數據和標簽 b_image, b_label0, b_label1, b_label2, b_label3 = sess.run( [image_batch, label_batch0, label_batch1, label_batch2, label_batch3]) # 優化模型 sess.run(optimizer, feed_dict={x: b_image, y0: b_label0, y1: b_label1, y2: b_label2, y3: b_label3}) # 每迭代50次計算一次loss和准確率 if i % 50 == 0: # 每迭代2000次降低一次學習率 if i % 2000 == 0: sess.run(tf.assign(lr, lr / 3)) acc, loss_ = sess.run([accuracy_40, loss_40], feed_dict={x: b_image, y0: b_label0, y1: b_label1, y2: b_label2, y3: b_label3}) learning_rate = sess.run(lr) print("Iter:%d Loss:%.3f Accuracy:%.2f, Learning_rate:%.4f" % (i, loss_, acc, learning_rate)) # 保存模型 # if acc0 > 0.90 and acc1 > 0.90 and acc2 > 0.90 and acc3 > 0.90: if i == 4000: saver.save(sess, "./captcha/models/crack_captcha.model", global_step=i) break # 通知其他線程關閉 coord.request_stop() # 其他所有線程關閉之后，這一函數才能返回 coord.join(threads)
和原來的程序略有不同，主要就是在網絡結構那里，輸入需要乘4，CHAR_SET_LEN*4，標簽、loss、以及准確率都需要修改
二、測試模型
import os import tensorflow as tf from PIL import Image from nets2 import nets_factory import numpy as np import matplotlib.pyplot as plt # 不同字符數量 CHAR_SET_LEN = 10 # 圖片高度 IMAGE_HEIGHT = 60 # 圖片寬度 IMAGE_WIDTH = 160 # 批次 BATCH_SIZE = 1 # tfrecord文件存放路徑 TFRECORD_FILE = "F：/PyCharm-projects/第十周/test.tfrecord" # placeholder x = tf.placeholder(tf.float32, [None, 224, 224]) # 從tfrecord讀出數據 def read_and_decode(filename): # 根據文件名生成一個隊列 filename_queue = tf.train.string_input_producer([filename]) reader = tf.TFRecordReader() # 返回文件名和文件 _, serialized_example = reader.read(filename_queue) features = tf.parse_single_example(serialized_example, features={ 'image': tf.FixedLenFeature([], tf.string), 'label0': tf.FixedLenFeature([], tf.int64), 'label1': tf.FixedLenFeature([], tf.int64), 'label2': tf.FixedLenFeature([], tf.int64), 'label3': tf.FixedLenFeature([], tf.int64), }) # 獲取圖片數據 image = tf.decode_raw(features['image'], tf.uint8) # 沒有經過預處理的灰度圖 image_raw = tf.reshape(image, [224, 224]) # tf.train.shuffle_batch必須確定shape image = tf.reshape(image, [224, 224]) # 圖片預處理 image = tf.cast(image, tf.float32) / 255.0 image = tf.subtract(image, 0.5) image = tf.multiply(image, 2.0) # 獲取label label0 = tf.cast(features['label0'], tf.int32) label1 = tf.cast(features['label1'], tf.int32) label2 = tf.cast(features['label2'], tf.int32) label3 = tf.cast(features['label3'], tf.int32) return image, image_raw, label0, label1, label2, label3 # 獲取圖片數據和標簽 image, image_raw, label0, label1, label2, label3 = read_and_decode(TFRECORD_FILE) # 使用shuffle_batch可以隨機打亂 image_batch, image_raw_batch, label_batch0, label_batch1, label_batch2, label_batch3 = tf.train.shuffle_batch( [image, image_raw, label0, label1, label2, label3], batch_size=BATCH_SIZE, capacity=50000, min_after_dequeue=10000, num_threads=1) # 定義網絡結構 train_network_fn = nets_factory.get_network_fn( 'alexnet_v2', num_classes=CHAR_SET_LEN*4, weight_decay=0.0005, is_training=False) with tf.Session() as sess: # inputs: a tensor of size [batch_size, height, width, channels] X = tf.reshape(x, [BATCH_SIZE, 224, 224, 1]) # 數據輸入網絡得到輸出值 logits, end_points = train_network_fn(X) # 預測值 # 因為一次看40維的向量有點奇怪，因此，將這個40維的向量進行拆分 # 拆分的起始位置是第0行的位置0，取10個數 logits0 = tf.slice(logits, [0, 0], [-1, 10]) # 拆分的起始位置是第0行的位置10 logits1 = tf.slice(logits, [0, 10], [-1, 10]) logits2 = tf.slice(logits, [0, 20], [-1, 10]) logits3 = tf.slice(logits, [0, 30], [-1, 10]) # one-hot編碼，獲取最大的數（其實就是1）的位置 predict0 = tf.argmax(logits0, 1) predict1 = tf.argmax(logits1, 1) predict2 = tf.argmax(logits2, 1) predict3 = tf.argmax(logits3, 1) # 初始化 sess.run(tf.global_variables_initializer()) # 載入訓練好的模型 saver = tf.train.Saver() saver.restore(sess, './captcha/models/crack_captcha.model-4000') # 創建一個協調器，管理線程 coord = tf.train.Coordinator() # 啟動QueueRunner, 此時文件名隊列已經進隊 threads = tf.train.start_queue_runners(sess=sess, coord=coord) for i in range(10): # 獲取一個批次的數據和標簽 b_image, b_image_raw, b_label0, b_label1, b_label2, b_label3 = sess.run([image_batch, image_raw_batch, label_batch0, label_batch1, label_batch2, label_batch3]) # 顯示圖片 img = Image.fromarray(b_image_raw[0], 'L') plt.imshow(img) plt.axis('off') plt.show() # 打印標簽 print('label:', b_label0, b_label1, b_label2, b_label3) # 預測 label0, label1, label2, label3 = sess.run([predict0, predict1, predict2, predict3], feed_dict={x: b_image}) # 打印預測值 print('predict:', label0, label1, label2, label3) # 通知其他線程關閉 coord.request_stop() # 其他所有線程關閉之后，這一函數才能返回 coord.join(threads)
label: [4] [6] [3] [2] predict: [4] [6] [3] [1]
label: [6] [4] [9] [3] predict: [6] [4] [9] [3]
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