From e8963943ed9070f34da1cc839d706c174c1218e3 Mon Sep 17 00:00:00 2001 From: ypwhs <915505626@qq.com> Date: Sun, 16 Jun 2019 22:20:27 +0800 Subject: [PATCH] update --- README.md | 402 ++++++++----- README_2017.md | 380 ++++++++++++ cnn.ipynb => cnn_2017.ipynb | 0 cnn_2019.ipynb | 838 ++++++++++++++++++++++++++ model.h5 => ctc_2017.h5 | Bin ctc.ipynb => ctc_2017.ipynb | 0 ctc_2019.ipynb | 1106 +++++++++++++++++++++++++++++++++++ imgs/cnn.png | Bin 0 -> 360904 bytes imgs/ctc.png | Bin 0 -> 470708 bytes imgs/vis.png | Bin 0 -> 85081 bytes 10 files changed, 2594 insertions(+), 132 deletions(-) create mode 100644 README_2017.md rename cnn.ipynb => cnn_2017.ipynb (100%) create mode 100644 cnn_2019.ipynb rename model.h5 => ctc_2017.h5 (100%) rename ctc.ipynb => ctc_2017.ipynb (100%) create mode 100644 ctc_2019.ipynb create mode 100644 imgs/cnn.png create mode 100644 imgs/ctc.png create mode 100644 imgs/vis.png diff --git a/README.md b/README.md index 4532fb6..18e9a66 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,30 @@ 本项目会通过 Keras 搭建一个深度卷积神经网络来识别 captcha 验证码，建议使用显卡来运行该项目。 -下面的可视化代码都是在 `jupyter notebook` 中完成的，如果你希望写成 python 脚本，稍加修改即可正常运行，当然也可以去掉这些可视化代码。Keras 版本：1.2.2。 +下面的可视化代码都是在 `jupyter notebook` 中完成的，如果你希望写成 python 脚本，稍加修改即可正常运行，当然也可以去掉这些可视化代码。 + +2019 年更新了： + +* 适配了新版 API +* 提高了数据生成器的效率 +* 使用了 CuDNNGRU 提高了训练和预测效率 +* 更新了文档 + +# 环境 + +本项目使用的环境如下： + +* captcha 0.3 +* tensorflow-gpu 1.13.1 +* numpy 1.16.4 +* tqdm 4.28.1 + +下面几个包是用于可视化的： + +* matplotlib 2.2.2 +* pandas 0.23.0 +* pydot 1.4.1 +* graphviz 2.38.0-12ubuntu2.1 # captcha @@ -36,6 +59,20 @@ plt.title(random_str) ![](https://raw.githubusercontent.com/ypwhs/resources/master/captcha/captcha.png) +# 防止 tensorflow 占用所有显存 + +众所周知 tensorflow 默认占用所有显存，这样不利于我们同时进行多项实验，因此我们可以使用下面的代码当 tensorflow 使用它需要的显存，而不是直接占用所有显存。 + +```py +import tensorflow as tf +import tensorflow.keras.backend as K + +config = tf.ConfigProto() +config.gpu_options.allow_growth=True +sess = tf.Session(config=config) +K.set_session(sess) +``` + # 数据生成器 训练模型的时候，我们可以选择两种方式来生成我们的训练数据，一种是一次性生成几万张图，然后开始训练，一种是定义一个数据生成器，然后利用 `fit_generator` 函数来训练。 @@ -46,39 +83,60 @@ plt.title(random_str) ## X -X 的形状是 `(batch_size, height, width, 3)`，比如一批生成32个样本，图片宽度为170，高度为80，那么形状就是 `(32, 80, 170, 3)`，取第一张图就是 `X[0]`。 +X 的形状是 `(batch_size, height, width, 3)`，比如一批生成 128 个样本，图片宽度为170，高度为80，那么 X 的形状就是 `(128, 64, 128, 3)`，如果你想取第一张图，代码可以这样写 `X[0]`。 ## y -y 的形状是四个 `(batch_size, n_class)`，如果转换成 numpy 的格式，则是 `(n_len, batch_size, n_class)`，比如一批生成32个样本，验证码的字符有36种，长度是4位，那么它的形状就是4个 `(32, 36)`，也可以说是 `(4, 32, 36)`，解码函数在下个代码块。 +y 的形状是四个 `(batch_size, n_class)`，如果转换成 numpy 的格式，则是 `(n_len, batch_size, n_class)`，比如一批生成 128 个样本，验证码的字符有 36 种，长度是 4 位，那么它的形状就是 4 个 `(128, 36)` 的矩阵，也可以说是 `(4, 32, 36)`。 + +## 数据生成器 + +为了让 Keras 能够使用多进程并行生成数据，我们需要使用 Keras 的 Sequence 类实现一个我们自己的数据类。 + +在 `__init__` 初始化函数里，我们定义数据所需的参数，然后这个数据的长度就是 steps 数。在 `__getitem__` 里，我们不用理会索引号，直接随机生成一批样本送去训练即可。 ```py -def gen(batch_size=32): - X = np.zeros((batch_size, height, width, 3), dtype=np.uint8) - y = [np.zeros((batch_size, n_class), dtype=np.uint8) for i in range(n_len)] - generator = ImageCaptcha(width=width, height=height) - while True: - for i in range(batch_size): - random_str = ''.join([random.choice(characters) for j in range(4)]) - X[i] = generator.generate_image(random_str) +from tensorflow.keras.utils import Sequence + +class CaptchaSequence(Sequence): + def __init__(self, characters, batch_size, steps, n_len=4, width=128, height=64): + self.characters = characters + self.batch_size = batch_size + self.steps = steps + self.n_len = n_len + self.width = width + self.height = height + self.n_class = len(characters) + self.generator = ImageCaptcha(width=width, height=height) + + def __len__(self): + return self.steps + + def __getitem__(self, idx): + X = np.zeros((self.batch_size, self.height, self.width, 3), dtype=np.float32) + y = [np.zeros((self.batch_size, self.n_class), dtype=np.uint8) for i in range(self.n_len)] + for i in range(self.batch_size): + random_str = ''.join([random.choice(self.characters) for j in range(self.n_len)]) + X[i] = np.array(self.generator.generate_image(random_str)) / 255.0 for j, ch in enumerate(random_str): y[j][i, :] = 0 - y[j][i, characters.find(ch)] = 1 - yield X, y + y[j][i, self.characters.find(ch)] = 1 + return X, y ``` -上面就是一个可以无限生成数据的例子，我们将使用这个生成器来训练我们的模型。 - # 使用生成器 -生成器的使用方法很简单，只需要用 next 函数即可。下面是一个例子，生成32个数据，然后显示第一个数据。当然，在这里我们还对生成的 One-Hot 编码后的数据进行了解码，首先将它转为 numpy 数组，然后取36个字符中最大的数字的位置，因为神经网络会输出36个字符的概率，然后将概率最大的四个字符的编号转换为字符串。 +生成器的使用方法很简单，只需要用对它取第一个 batch 即可。下面是一个例子，初始化一个数据集，设置 batch_size 和 steps 都为 1，然后取出来第一个数据，对它可视化。 + +在这里我们对生成的 One-Hot 编码后的标签进行了解码，首先将它转为 numpy 数组，然后取36个字符中最大的数字的位置（axis=2代表字符的轴），实际上神经网络会输出36个字符的概率，我们需要将概率最大的四个字符的编号取出来，转换为字符串。 ```py def decode(y): y = np.argmax(np.array(y), axis=2)[:,0] return ''.join([characters[x] for x in y]) -X, y = next(gen(1)) +data = CaptchaSequence(characters, batch_size=1, steps=1) +X, y = data[0] plt.imshow(X[0]) plt.title(decode(y)) ``` @@ -86,38 +144,35 @@ plt.title(decode(y)) # 构建深度卷积神经网络 ```py -from keras.models import * -from keras.layers import * +from tensorflow.keras.models import * +from tensorflow.keras.layers import * input_tensor = Input((height, width, 3)) x = input_tensor -for i in range(4): - x = Convolution2D(32*2**i, 3, 3, activation='relu')(x) - x = Convolution2D(32*2**i, 3, 3, activation='relu')(x) - x = MaxPooling2D((2, 2))(x) +for i, n_cnn in enumerate([2, 2, 2, 2, 2]): + for j in range(n_cnn): + x = Conv2D(32*2**min(i, 3), kernel_size=3, padding='same', kernel_initializer='he_uniform')(x) + x = BatchNormalization()(x) + x = Activation('relu')(x) + x = MaxPooling2D(2)(x) x = Flatten()(x) -x = Dropout(0.25)(x) -x = [Dense(n_class, activation='softmax', name='c%d'%(i+1))(x) for i in range(4)] -model = Model(input=input_tensor, output=x) - -model.compile(loss='categorical_crossentropy', - optimizer='adadelta', - metrics=['accuracy']) +x = [Dense(n_class, activation='softmax', name='c%d'%(i+1))(x) for i in range(n_len)] +model = Model(inputs=input_tensor, outputs=x) ``` -模型结构很简单，特征提取部分使用的是两个卷积，一个池化的结构，这个结构是学的 VGG16 的结构。之后我们将它 Flatten，然后添加 Dropout ，尽量避免过拟合问题，最后连接四个分类器，每个分类器是36个神经元，输出36个字符的概率。 +模型结构很简单，特征提取部分使用的是两个卷积，一个池化的结构，这个结构是学的 VGG16 的结构。我们重复五个 block，然后我们将它 Flatten，连接四个分类器，每个分类器是36个神经元，输出36个字符的概率。 # 模型可视化 得益于 Keras 自带的可视化，我们可以使用几句代码来可视化模型的结构： ```py -from keras.utils.visualize_util import plot +from tensorflow.keras.utils import plot_model from IPython.display import Image -plot(model, to_file="model.png", show_shapes=True) -Image('model.png') +plot_model(model, to_file='cnn.png', show_shapes=True) +Image('cnn.png') ``` 这里需要使用 pydot 这个库，以及 graphviz 这个库，在 macOS 系统上安装方法如下： @@ -127,18 +182,54 @@ brew install graphviz pip install pydot-ng ``` -![](https://raw.githubusercontent.com/ypwhs/resources/master/captcha/model.png) +![](imgs/cnn.png) 我们可以看到最后一层卷积层输出的形状是 `(1, 6, 256)`，已经不能再加卷积层了。 # 训练模型 -训练模型反而是所有步骤里面最简单的一个，直接使用 `model.fit_generator` 即可，这里的验证集使用了同样的生成器，由于数据是通过生成器随机生成的，所以我们不用考虑数据是否会重复。注意，这段代码在笔记本上可能要耗费一下午时间。如果你想让模型预测得更准确，可以将 `nb_epoch` 改为 10 或者 20，但它也将耗费成倍的时间。注意我们这里使用了一个小技巧，添加 `nb_worker=2` 参数让 Keras 自动实现多进程生成数据，摆脱 python 单线程效率低的缺点。 +训练模型反而是所有步骤里面最简单的一个，直接使用 `model.fit_generator` 即可，这里的验证集使用了同样的生成器，由于数据是通过生成器随机生成的，所以我们不用考虑数据是否会重复。 + +为了避免手动调参，我们使用了 Adam 优化器，它的学习率是自动设置的，我们只需要给一个较好的初始学习率即可。 + +EarlyStopping 是一个 Keras 的 Callback，它可以在 loss 超过多少个 epoch 没有下降以后，就自动终止训练，避免浪费时间。 + +ModelCheckpoint 是另一个好用的 Callback，它可以保存训练过程中最好的模型。 + +CSVLogger 可以记录 loss 为 CSV 文件，这样我们就可以在训练完成以后绘制训练过程中的 loss 曲线。 + +注意，这段代码在笔记本电脑上可能要较长时间，建议使用带有 NVIDIA 显卡的机器运行。注意我们这里使用了一个小技巧，添加 `workers=4` 参数让 Keras 自动实现多进程生成数据，摆脱 python 单线程效率低的缺点。 + +```py +from tensorflow.keras.callbacks import EarlyStopping, CSVLogger, ModelCheckpoint +from tensorflow.keras.optimizers import * + +train_data = CaptchaSequence(characters, batch_size=128, steps=1000) +valid_data = CaptchaSequence(characters, batch_size=128, steps=100) +callbacks = [EarlyStopping(patience=3), CSVLogger('cnn.csv'), ModelCheckpoint('cnn_best.h5', save_best_only=True)] + +model.compile(loss='categorical_crossentropy', + optimizer=Adam(1e-3, amsgrad=True), + metrics=['accuracy']) +model.fit_generator(train_data, epochs=100, validation_data=valid_data, workers=4, use_multiprocessing=True, + callbacks=callbacks) +``` + +### 载入最好的模型继续训练一会 + +为了让模型充分训练，我们可以载入之前最好的模型权值，然后降低学习率为原来的十分之一，继续训练，这样可以让模型收敛得更好。 ```py -model.fit_generator(gen(), samples_per_epoch=51200, nb_epoch=5, - nb_worker=2, pickle_safe=True, - validation_data=gen(), nb_val_samples=1280) +model.load_weights('cnn_best.h5') + +callbacks = [EarlyStopping(patience=3), CSVLogger('cnn.csv', append=True), + ModelCheckpoint('cnn_best.h5', save_best_only=True)] + +model.compile(loss='categorical_crossentropy', + optimizer=Adam(1e-4, amsgrad=True), + metrics=['accuracy']) +model.fit_generator(train_data, epochs=100, validation_data=valid_data, workers=4, use_multiprocessing=True, + callbacks=callbacks) ``` # 测试模型 @@ -146,10 +237,11 @@ model.fit_generator(gen(), samples_per_epoch=51200, nb_epoch=5, 当我们训练完成以后，可以识别一个验证码试试看： ```py -X, y = next(gen(1)) +X, y = data[0] y_pred = model.predict(X) plt.title('real: %s\npred:%s'%(decode(y), decode(y_pred))) plt.imshow(X[0], cmap='gray') +plt.axis('off') ``` ![](https://raw.githubusercontent.com/ypwhs/resources/master/captcha/test_model.png) @@ -160,25 +252,25 @@ plt.imshow(X[0], cmap='gray') ```py from tqdm import tqdm -def evaluate(model, batch_num=20): +def evaluate(model, batch_num=100): batch_acc = 0 - generator = gen() - for i in tqdm(range(batch_num)): - X, y = next(generator) - y_pred = model.predict(X) - y_pred = np.argmax(y_pred, axis=2).T - y_true = np.argmax(y, axis=2).T - batch_acc += np.mean(map(np.array_equal, y_true, y_pred)) + with tqdm(CaptchaSequence(characters, batch_size=128, steps=100)) as pbar: + for X, y in pbar: + y_pred = model.predict(X) + y_pred = np.argmax(y_pred, axis=-1).T + y_true = np.argmax(y, axis=-1).T + + batch_acc += (y_true == y_pred).all(axis=-1).mean() return batch_acc / batch_num evaluate(model) ``` -这里用到了一个库叫做 tqdm，它是一个进度条的库，为的是能够实时反馈进度。然后我们通过一些 numpy 计算去统计我们的准确率，这里计算规则是只要有一个错，那么就不算它对。经过计算，我们的模型的总体准确率在经过五代训练就可以达到 90%，继续训练还可以达到更高的准确率。 +这里用到了一个库叫做 tqdm，它是一个进度条的库，为的是能够实时反馈进度。然后我们通过一些 numpy 计算去统计我们的准确率，这里计算规则是只要有一个错，那么就不算它对。经过计算，我们的模型的总体准确率在经过充分训练以后，可以达到 98.26% 的总体准确率。 # 模型总结 -模型的大小是16MB，在我的笔记本上跑1000张验证码需要用20秒，当然，显卡会更快。对于验证码识别的问题来说，哪怕是10%的准确率也已经称得上破解，毕竟假设100%识别率破解要一个小时，那么10%的识别率也只用十个小时，还算等得起，而我们的识别率有90%，已经可以称得上完全破解了这类验证码。 +模型的大小是10.7MB，总体准确率是 98.26%，基本上可以确定破解了此类验证码。 # 改进 @@ -186,110 +278,132 @@ evaluate(model) # CTC Loss -这个 loss 是一个特别神奇的 loss，它可以在只知道序列的顺序，不知道具体位置的情况下，让模型收敛。在这方面百度似乎做得很不错，利用它来识别音频信号。（[warp-ctc](https://github.com/baidu-research/warp-ctc)） +这个 loss 是一个特别神奇的 loss，它可以在只知道序列的顺序，不知道具体位置的情况下，让模型收敛。这里有一个非常好的文章介绍了 CTC Loss: [Sequence Modeling +With CTC](https://distill.pub/2017/ctc/) ![](https://raw.githubusercontent.com/ypwhs/resources/master/captcha/deep-speech-ctc-small.png) -那么在 Keras 里面，CTC Loss 已经内置了，我们直接定义这样一个函数，即可实现 CTC Loss，由于我们使用的是循环神经网络，所以默认丢掉前面两个输出，因为它们通常无意义，且会影响模型的输出。 +在 Keras 里面已经内置了 CTC Loss ，我们实现下面的代码即可在模型里使用 CTC Loss。 -* y\_pred 是模型的输出，是按顺序输出的37个字符的概率，因为我们这里用到了循环神经网络，所以需要一个空白字符的概念； -* labels 是验证码，是四个数字； -* input\_length 表示 y\_pred 的长度，我们这里是15； -* label\_length 表示 labels 的长度，我们这里是4。 +* `y_pred` 是模型的输出，是按顺序输出的37个字符的概率，因为我们这里用到了循环神经网络，所以需要一个空白字符的概念； +* `labels` 是验证码，是四个数字，每个数字代表字符在字符集里的位置 +* `input_length` 表示 `y_pred` 的长度，我们这里是16 +* `label_length` 表示 `labels` 的长度，我们这里是4 ```py -from keras import backend as K +import tensorflow.keras.backend as K def ctc_lambda_func(args): y_pred, labels, input_length, label_length = args - y_pred = y_pred[:, 2:, :] return K.ctc_batch_cost(labels, y_pred, input_length, label_length) ``` # 模型结构 -我们的模型结构是这样设计的，首先通过卷积神经网络去识别特征，然后经过一个全连接降维，再按水平顺序输入到一种特殊的循环神经网络，叫 GRU，它具有一些特殊的性质，为什么用 GRU 而不用 LSTM 呢？总的来说就是它的效果比 LSTM 好，所以我们用它。 +我们的模型结构是这样设计的，首先通过卷积神经网络去识别特征，然后按水平顺序输入到 GRU 进行序列建模，最后使用一个分类器对每个时刻输出的特征进行分类。 ```py -from keras.models import * -from keras.layers import * -rnn_size = 128 +from tensorflow.keras.models import * +from tensorflow.keras.layers import * -input_tensor = Input((width, height, 3)) +input_tensor = Input((height, width, 3)) x = input_tensor -for i in range(3): - x = Convolution2D(32, 3, 3, activation='relu')(x) - x = Convolution2D(32, 3, 3, activation='relu')(x) - x = MaxPooling2D(pool_size=(2, 2))(x) +for i, n_cnn in enumerate([2, 2, 2, 2, 2]): + for j in range(n_cnn): + x = Conv2D(32*2**min(i, 3), kernel_size=3, padding='same', kernel_initializer='he_uniform')(x) + x = BatchNormalization()(x) + x = Activation('relu')(x) + x = MaxPooling2D(2 if i < 3 else (2, 1))(x) -conv_shape = x.get_shape() -x = Reshape(target_shape=(int(conv_shape[1]), int(conv_shape[2]*conv_shape[3])))(x) +x = Permute((2, 1, 3))(x) +x = TimeDistributed(Flatten())(x) -x = Dense(32, activation='relu')(x) +rnn_size = 128 +x = Bidirectional(CuDNNGRU(rnn_size, return_sequences=True))(x) +x = Bidirectional(CuDNNGRU(rnn_size, return_sequences=True))(x) +x = Dense(n_class, activation='softmax')(x) -gru_1 = GRU(rnn_size, return_sequences=True, init='he_normal', name='gru1')(x) -gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, - init='he_normal', name='gru1_b')(x) -gru1_merged = merge([gru_1, gru_1b], mode='sum') +base_model = Model(inputs=input_tensor, outputs=x) +``` -gru_2 = GRU(rnn_size, return_sequences=True, init='he_normal', name='gru2')(gru1_merged) -gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, - init='he_normal', name='gru2_b')(gru1_merged) -x = merge([gru_2, gru_2b], mode='concat') -x = Dropout(0.25)(x) -x = Dense(n_class, init='he_normal', activation='softmax')(x) -base_model = Model(input=input_tensor, output=x) +为了训练这个模型，我们还需要搭建一个 loss 计算网络，代码如下： +```py labels = Input(name='the_labels', shape=[n_len], dtype='float32') input_length = Input(name='input_length', shape=[1], dtype='int64') label_length = Input(name='label_length', shape=[1], dtype='int64') -loss_out = Lambda(ctc_lambda_func, output_shape=(1,), - name='ctc')([x, labels, input_length, label_length]) +loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([x, labels, input_length, label_length]) -model = Model(input=[input_tensor, labels, input_length, label_length], output=[loss_out]) -model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer='adadelta') +model = Model(inputs=[input_tensor, labels, input_length, label_length], outputs=loss_out) ``` +真正训练出来的模型是 `base_model`，由于 Keras 的限制，我们没办法直接使用 `base_model` 搭建 CTCLoss，所以我们只能按照上面的方法，让模型直接输出 loss。 + # 模型可视化 可视化的代码同上，这里只贴图。 -![](https://raw.githubusercontent.com/ypwhs/resources/master/captcha/model2.png) +![](imgs/ctc.png) + +可以看到模型比上一个模型复杂了许多，但实际上只是因为输入比较多，所以它显得很大。 + +首先模型输入一个 `(height, width, 3)` 维度的图片，然后经过一系列的层降维到了 `(2, 16, 256)`，之后我们使用 Permute 把 width 轴调整到第一个维度以适配 RNN 的输入格式。调整以后的维度是 `(16, 2, 256)`，然后使用 `TimeDistributed(Flatten())` 把后两个维度压成一维，也就是 `(16, 512)`，之后经过 2 层双向的 GRU 对序列横向建模，最后经过 Dense 分类器输出水平方向上每个字符的概率分布。 -可以看到模型比上一个模型复杂了许多，但实际上只是因为输入比较多，所以它显得很大。还有一个值得注意的地方，我们的图片在输入的时候是经过了旋转的，这是因为我们希望以水平方向输入，而图片在 numpy 里默认是这样的形状：(height, width, 3)，因此我们使用了 `transpose` 函数将图片转为了(width, height, 3)的格式，然后经过各种卷积和降维，变成了 (17, 32)，这里的每个长度为32的向量都代表一个竖条的图片的特征，从左到右，一共有17条。然后我们兵分两路，一路从左到右输入到 GRU，一路从右到左输入到 GRU，然后将他们输出的结果加起来。再兵分两路，还是一路正方向，一路反方向，只不过第二次我们直接将它们的输出连起来，然后经过一个全连接，输出每个字符的概率。 +使用 CuDNNGRU 是因为它在 NVIDIA 显卡上可以加速非常多倍，如果你使用的是 CPU，改为 GRU 即可。 + +使用 RNN 的原因是，如果你看到一句话是 `今天我*了一个非常好吃的苹果`，有一个字看不清，你很容易猜到这个字是“吃”，但是使用 CNN，你就很难有这么大的感受野，从苹果推测出前面的字是吃。 # 数据生成器 +数据生成器和 CNN 的差不多，这里需要多几个矩阵，一个是 input_length，代表序列长度，一个是 label_length，代表验证码长度，还有一个 np.ones，没有意义，只是为了适配 Keras 训练需要的矩阵输入。 + ```py -def gen(batch_size=128): - X = np.zeros((batch_size, width, height, 3), dtype=np.uint8) - y = np.zeros((batch_size, n_len), dtype=np.uint8) - while True: - generator = ImageCaptcha(width=width, height=height) - for i in range(batch_size): - random_str = ''.join([random.choice(characters) for j in range(4)]) - X[i] = np.array(generator.generate_image(random_str)).transpose(1, 0, 2) - y[i] = [characters.find(x) for x in random_str] - yield [X, y, np.ones(batch_size)*int(conv_shape[1]-2), - np.ones(batch_size)*n_len], np.ones(batch_size) +from tensorflow.keras.utils import Sequence + +class CaptchaSequence(Sequence): + def __init__(self, characters, batch_size, steps, n_len=4, width=128, height=64, + input_length=16, label_length=4): + self.characters = characters + self.batch_size = batch_size + self.steps = steps + self.n_len = n_len + self.width = width + self.height = height + self.input_length = input_length + self.label_length = label_length + self.n_class = len(characters) + self.generator = ImageCaptcha(width=width, height=height) + + def __len__(self): + return self.steps + + def __getitem__(self, idx): + X = np.zeros((self.batch_size, self.height, self.width, 3), dtype=np.float32) + y = np.zeros((self.batch_size, self.n_len), dtype=np.uint8) + input_length = np.ones(self.batch_size)*self.input_length + label_length = np.ones(self.batch_size)*self.label_length + for i in range(self.batch_size): + random_str = ''.join([random.choice(self.characters) for j in range(self.n_len)]) + X[i] = np.array(self.generator.generate_image(random_str)) / 255.0 + y[i] = [self.characters.find(x) for x in random_str] + return [X, y, input_length, label_length], np.ones(self.batch_size) ``` # 评估模型 ```py -def evaluate(model, batch_num=10): +from tqdm import tqdm + +def evaluate(model, batch_size=128, steps=20): batch_acc = 0 - generator = gen() - for i in range(batch_num): - [X_test, y_test, _, _], _ = next(generator) + valid_data = CaptchaSequence(characters, batch_size, steps) + for [X_test, y_test, _, _], _ in valid_data: y_pred = base_model.predict(X_test) - shape = y_pred[:,2:,:].shape - ctc_decode = K.ctc_decode(y_pred[:,2:,:], - input_length=np.ones(shape[0])*shape[1])[0][0] - out = K.get_value(ctc_decode)[:, :4] + shape = y_pred.shape + out = K.get_value(K.ctc_decode(y_pred, input_length=np.ones(shape[0])*shape[1])[0][0])[:, :4] if out.shape[1] == 4: - batch_acc += ((y_test == out).sum(axis=1) == 4).mean() - return batch_acc / batch_num + batch_acc += (y_test == out).all(axis=1).mean() + return batch_acc / steps ``` 我们会通过这个函数来评估我们的模型，和上面的评估标准一样，只有全部正确，我们才算预测正确，中间有个坑，就是模型最开始训练的时候，并不一定会输出四个字符，所以我们如果遇到所有的字符都不到四个的时候，就不计算了，相当于加0，遇到多于4个字符的时候，只取前四个。 @@ -299,45 +413,66 @@ def evaluate(model, batch_num=10): 因为 Keras 没有针对这种输出计算准确率的选项，因此我们需要自定义一个回调函数，它会在每一代训练完成的时候计算模型的准确率。 ```py -from keras.callbacks import * +from tensorflow.keras.callbacks import Callback class Evaluate(Callback): def __init__(self): self.accs = [] def on_epoch_end(self, epoch, logs=None): - acc = evaluate(base_model)*100 + logs = logs or {} + acc = evaluate(base_model) + logs['val_acc'] = acc self.accs.append(acc) - print - print 'acc: %f%%'%acc - -evaluator = Evaluate() + print(f'\nacc: {acc*100:.4f}') ``` # 训练模型 -由于 CTC Loss 收敛很慢，所以我们需要设置比较大的代数，这里我们设置了100代，然后添加了一个早期停止的回调和我们上面定义的回调，但是第一次训练只训练37代就停了，测试准确率才95%，我又在这个基础上继续训练了一次，停在了25代，得到了98%的准确率，所以一共训练了62代。 +我们还是按照之前的训练策略，先训练 100 代，等 loss 不降低以后，降低学习率，再训练 100 代，代码如下： ```py -model.fit_generator(gen(128), samples_per_epoch=51200, nb_epoch=200, - callbacks=[EarlyStopping(patience=10), evaluator], - validation_data=gen(), nb_val_samples=1280) +from tensorflow.keras.callbacks import EarlyStopping, CSVLogger, ModelCheckpoint +from tensorflow.keras.optimizers import * + +train_data = CaptchaSequence(characters, batch_size=128, steps=1000) +valid_data = CaptchaSequence(characters, batch_size=128, steps=100) +callbacks = [EarlyStopping(patience=5), Evaluate(), + CSVLogger('ctc.csv'), ModelCheckpoint('ctc_best.h5', save_best_only=True)] + +model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=Adam(1e-3, amsgrad=True)) +model.fit_generator(train_data, epochs=100, validation_data=valid_data, workers=4, use_multiprocessing=True, + callbacks=callbacks) ``` -![](https://raw.githubusercontent.com/ypwhs/resources/master/captcha/loss.png) +```py +model.load_weights('ctc_best.h5') + +callbacks = [EarlyStopping(patience=5), Evaluate(), + CSVLogger('ctc.csv', append=True), ModelCheckpoint('ctc_best.h5', save_best_only=True)] + +model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=Adam(1e-4, amsgrad=True)) +model.fit_generator(train_data, epochs=100, validation_data=valid_data, workers=4, use_multiprocessing=True, + callbacks=callbacks) +``` + +![](imgs/vis.png) + +可以看到 loss 一开始下降很快，后面就很平了，但是我们把在对数尺度下绘制 loss 图的话，还是能看到 loss 一直在下降的。acc 上升得也很快，虽然前期训练的时候 acc 很抖动，但是后期学习率降下来以后就不会再跌下来了。 + +最终模型的准确率达到了 99.21%，训练过程中的准确率最高达到了 99.49%。 # 测试模型 ```py characters2 = characters + ' ' -[X_test, y_test, _, _], _ = next(gen(1)) +[X_test, y_test, _, _], _ = data[0] y_pred = base_model.predict(X_test) -y_pred = y_pred[:,2:,:] out = K.get_value(K.ctc_decode(y_pred, input_length=np.ones(y_pred.shape[0])*y_pred.shape[1], )[0][0])[:, :4] out = ''.join([characters[x] for x in out[0]]) y_true = ''.join([characters[x] for x in y_test[0]]) -plt.imshow(X_test[0].transpose(1, 0, 2)) +plt.imshow(X_test[0]) plt.title('pred:' + str(out) + '\ntrue: ' + str(y_true)) argmax = np.argmax(y_pred, axis=2)[0] @@ -356,10 +491,13 @@ list(zip(argmax, ''.join([characters2[x] for x in argmax]))) generator = ImageCaptcha(width=width, height=height) random_str = 'O0O0' X = generator.generate_image(random_str) -X = np.expand_dims(X, 0) +X = np.expand_dims(X, 0) / 255.0 -y_pred = model.predict(X) -plt.title('real: %s\npred:%s'%(random_str, decode(y_pred))) +y_pred = base_model.predict(X) +out = K.get_value(K.ctc_decode(y_pred, input_length=np.ones(y_pred.shape[0])*y_pred.shape[1], )[0][0])[:, :4] +out = ''.join([characters[x] for x in out[0]]) + +plt.title('real: %s\npred:%s'%(random_str, out)) plt.imshow(X[0], cmap='gray') ``` @@ -367,14 +505,14 @@ plt.imshow(X[0], cmap='gray') # 总结 -模型的大小是4.7MB，在我的笔记本上跑1000张验证码需要用14秒，平均一秒识别71张，估计可以拼过网速。 - -最后附上一张本模型识别 HACK 。 +模型的大小是12.8MB，准确率达到了惊人的 99.21%，即使连 0 和 O 都能精准区分，非常成功。 -![](https://raw.githubusercontent.com/ypwhs/resources/master/captcha/HACK.png) +如果你想查看更多经验，可以看看我在百度云魅族深度学习应用大赛的代码和思路：[https://github.com/ypwhs/baiduyun_deeplearning_competition](https://github.com/ypwhs/baiduyun_deeplearning_competition) # 参考链接 -* [http://keras-cn.readthedocs.io/en/latest/getting_started/functional_API/](http://keras-cn.readthedocs.io/en/latest/getting_started/functional_API/) -* [https://github.com/fchollet/keras/blob/master/examples/image_ocr.py](https://github.com/fchollet/keras/blob/master/examples/image_ocr.py) -* [http://cs231n.github.io/convolutional-networks/](http://cs231n.github.io/convolutional-networks/) +* [https://keras.io/getting-started/functional-api-guide/](https://keras.io/getting-started/functional-api-guide/) +* [https://www.tensorflow.org/api_docs/python/tf/nn/ctc_loss](https://www.tensorflow.org/api_docs/python/tf/nn/ctc_loss) +* [https://github.com/keras-team/keras/blob/master/examples/image_ocr.py](https://github.com/keras-team/keras/blob/master/examples/image_ocr.py) +* [https://cs231n.github.io/convolutional-networks/](https://cs231n.github.io/convolutional-networks/) +* [https://distill.pub/2017/ctc/](https://distill.pub/2017/ctc/) diff --git a/README_2017.md b/README_2017.md new file mode 100644 index 0000000..4532fb6 --- /dev/null +++ b/README_2017.md @@ -0,0 +1,380 @@ +# 使用深度学习来破解 captcha 验证码 + +本项目会通过 Keras 搭建一个深度卷积神经网络来识别 captcha 验证码，建议使用显卡来运行该项目。 + +下面的可视化代码都是在 `jupyter notebook` 中完成的，如果你希望写成 python 脚本，稍加修改即可正常运行，当然也可以去掉这些可视化代码。Keras 版本：1.2.2。 + +# captcha + +captcha 是用 python 写的生成验证码的库，它支持图片验证码和语音验证码，我们使用的是它生成图片验证码的功能。 + +首先我们设置我们的验证码格式为数字加大写字母，生成一串验证码试试看： + +```py +from captcha.image import ImageCaptcha +import matplotlib.pyplot as plt +import numpy as np +import random + +%matplotlib inline +%config InlineBackend.figure_format = 'retina' + +import string +characters = string.digits + string.ascii_uppercase +print(characters) + +width, height, n_len, n_class = 170, 80, 4, len(characters) + +generator = ImageCaptcha(width=width, height=height) +random_str = ''.join([random.choice(characters) for j in range(4)]) +img = generator.generate_image(random_str) + +plt.imshow(img) +plt.title(random_str) + +``` + +![](https://raw.githubusercontent.com/ypwhs/resources/master/captcha/captcha.png) + +# 数据生成器 + +训练模型的时候，我们可以选择两种方式来生成我们的训练数据，一种是一次性生成几万张图，然后开始训练，一种是定义一个数据生成器，然后利用 `fit_generator` 函数来训练。 + +第一种方式的好处是训练的时候显卡利用率高，如果你需要经常调参，可以一次生成，多次使用；第二种方式的好处是你不需要生成大量数据，训练过程中可以利用 CPU 生成数据，而且还有一个好处是你可以无限生成数据。 + +我们的数据格式如下： + +## X + +X 的形状是 `(batch_size, height, width, 3)`，比如一批生成32个样本，图片宽度为170，高度为80，那么形状就是 `(32, 80, 170, 3)`，取第一张图就是 `X[0]`。 + +## y + +y 的形状是四个 `(batch_size, n_class)`，如果转换成 numpy 的格式，则是 `(n_len, batch_size, n_class)`，比如一批生成32个样本，验证码的字符有36种，长度是4位，那么它的形状就是4个 `(32, 36)`，也可以说是 `(4, 32, 36)`，解码函数在下个代码块。 + +```py +def gen(batch_size=32): + X = np.zeros((batch_size, height, width, 3), dtype=np.uint8) + y = [np.zeros((batch_size, n_class), dtype=np.uint8) for i in range(n_len)] + generator = ImageCaptcha(width=width, height=height) + while True: + for i in range(batch_size): + random_str = ''.join([random.choice(characters) for j in range(4)]) + X[i] = generator.generate_image(random_str) + for j, ch in enumerate(random_str): + y[j][i, :] = 0 + y[j][i, characters.find(ch)] = 1 + yield X, y +``` + +上面就是一个可以无限生成数据的例子，我们将使用这个生成器来训练我们的模型。 + +# 使用生成器 + +生成器的使用方法很简单，只需要用 next 函数即可。下面是一个例子，生成32个数据，然后显示第一个数据。当然，在这里我们还对生成的 One-Hot 编码后的数据进行了解码，首先将它转为 numpy 数组，然后取36个字符中最大的数字的位置，因为神经网络会输出36个字符的概率，然后将概率最大的四个字符的编号转换为字符串。 + +```py +def decode(y): + y = np.argmax(np.array(y), axis=2)[:,0] + return ''.join([characters[x] for x in y]) + +X, y = next(gen(1)) +plt.imshow(X[0]) +plt.title(decode(y)) +``` + +# 构建深度卷积神经网络 + +```py +from keras.models import * +from keras.layers import * + +input_tensor = Input((height, width, 3)) +x = input_tensor +for i in range(4): + x = Convolution2D(32*2**i, 3, 3, activation='relu')(x) + x = Convolution2D(32*2**i, 3, 3, activation='relu')(x) + x = MaxPooling2D((2, 2))(x) + +x = Flatten()(x) +x = Dropout(0.25)(x) +x = [Dense(n_class, activation='softmax', name='c%d'%(i+1))(x) for i in range(4)] +model = Model(input=input_tensor, output=x) + +model.compile(loss='categorical_crossentropy', + optimizer='adadelta', + metrics=['accuracy']) +``` + +模型结构很简单，特征提取部分使用的是两个卷积，一个池化的结构，这个结构是学的 VGG16 的结构。之后我们将它 Flatten，然后添加 Dropout ，尽量避免过拟合问题，最后连接四个分类器，每个分类器是36个神经元，输出36个字符的概率。 + +# 模型可视化 + +得益于 Keras 自带的可视化，我们可以使用几句代码来可视化模型的结构： + +```py +from keras.utils.visualize_util import plot +from IPython.display import Image + +plot(model, to_file="model.png", show_shapes=True) +Image('model.png') +``` + +这里需要使用 pydot 这个库，以及 graphviz 这个库，在 macOS 系统上安装方法如下： + +```sh +brew install graphviz +pip install pydot-ng +``` + +![](https://raw.githubusercontent.com/ypwhs/resources/master/captcha/model.png) + +我们可以看到最后一层卷积层输出的形状是 `(1, 6, 256)`，已经不能再加卷积层了。 + +# 训练模型 + +训练模型反而是所有步骤里面最简单的一个，直接使用 `model.fit_generator` 即可，这里的验证集使用了同样的生成器，由于数据是通过生成器随机生成的，所以我们不用考虑数据是否会重复。注意，这段代码在笔记本上可能要耗费一下午时间。如果你想让模型预测得更准确，可以将 `nb_epoch` 改为 10 或者 20，但它也将耗费成倍的时间。注意我们这里使用了一个小技巧，添加 `nb_worker=2` 参数让 Keras 自动实现多进程生成数据，摆脱 python 单线程效率低的缺点。 + +```py +model.fit_generator(gen(), samples_per_epoch=51200, nb_epoch=5, + nb_worker=2, pickle_safe=True, + validation_data=gen(), nb_val_samples=1280) +``` + +# 测试模型 + +当我们训练完成以后，可以识别一个验证码试试看： + +```py +X, y = next(gen(1)) +y_pred = model.predict(X) +plt.title('real: %s\npred:%s'%(decode(y), decode(y_pred))) +plt.imshow(X[0], cmap='gray') +``` + +![](https://raw.githubusercontent.com/ypwhs/resources/master/captcha/test_model.png) + +# 计算模型总体准确率 + +模型在训练的时候只会显示每一个字符的准确率，为了统计模型的总体准确率，我们可以写下面的函数： + +```py +from tqdm import tqdm +def evaluate(model, batch_num=20): + batch_acc = 0 + generator = gen() + for i in tqdm(range(batch_num)): + X, y = next(generator) + y_pred = model.predict(X) + y_pred = np.argmax(y_pred, axis=2).T + y_true = np.argmax(y, axis=2).T + batch_acc += np.mean(map(np.array_equal, y_true, y_pred)) + return batch_acc / batch_num + +evaluate(model) +``` + +这里用到了一个库叫做 tqdm，它是一个进度条的库，为的是能够实时反馈进度。然后我们通过一些 numpy 计算去统计我们的准确率，这里计算规则是只要有一个错，那么就不算它对。经过计算，我们的模型的总体准确率在经过五代训练就可以达到 90%，继续训练还可以达到更高的准确率。 + +# 模型总结 + +模型的大小是16MB，在我的笔记本上跑1000张验证码需要用20秒，当然，显卡会更快。对于验证码识别的问题来说，哪怕是10%的准确率也已经称得上破解，毕竟假设100%识别率破解要一个小时，那么10%的识别率也只用十个小时，还算等得起，而我们的识别率有90%，已经可以称得上完全破解了这类验证码。 + +# 改进 + +对于这种按顺序书写的文字，我们还有一种方法可以使用，那就是循环神经网络来识别序列。下面我们来了解一下如何使用循环神经网络来识别这类验证码。 + +# CTC Loss + +这个 loss 是一个特别神奇的 loss，它可以在只知道序列的顺序，不知道具体位置的情况下，让模型收敛。在这方面百度似乎做得很不错，利用它来识别音频信号。（[warp-ctc](https://github.com/baidu-research/warp-ctc)） + +![](https://raw.githubusercontent.com/ypwhs/resources/master/captcha/deep-speech-ctc-small.png) + +那么在 Keras 里面，CTC Loss 已经内置了，我们直接定义这样一个函数，即可实现 CTC Loss，由于我们使用的是循环神经网络，所以默认丢掉前面两个输出，因为它们通常无意义，且会影响模型的输出。 + +* y\_pred 是模型的输出，是按顺序输出的37个字符的概率，因为我们这里用到了循环神经网络，所以需要一个空白字符的概念； +* labels 是验证码，是四个数字； +* input\_length 表示 y\_pred 的长度，我们这里是15； +* label\_length 表示 labels 的长度，我们这里是4。 + +```py +from keras import backend as K + +def ctc_lambda_func(args): + y_pred, labels, input_length, label_length = args + y_pred = y_pred[:, 2:, :] + return K.ctc_batch_cost(labels, y_pred, input_length, label_length) +``` + +# 模型结构 + +我们的模型结构是这样设计的，首先通过卷积神经网络去识别特征，然后经过一个全连接降维，再按水平顺序输入到一种特殊的循环神经网络，叫 GRU，它具有一些特殊的性质，为什么用 GRU 而不用 LSTM 呢？总的来说就是它的效果比 LSTM 好，所以我们用它。 + +```py +from keras.models import * +from keras.layers import * +rnn_size = 128 + +input_tensor = Input((width, height, 3)) +x = input_tensor +for i in range(3): + x = Convolution2D(32, 3, 3, activation='relu')(x) + x = Convolution2D(32, 3, 3, activation='relu')(x) + x = MaxPooling2D(pool_size=(2, 2))(x) + +conv_shape = x.get_shape() +x = Reshape(target_shape=(int(conv_shape[1]), int(conv_shape[2]*conv_shape[3])))(x) + +x = Dense(32, activation='relu')(x) + +gru_1 = GRU(rnn_size, return_sequences=True, init='he_normal', name='gru1')(x) +gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, + init='he_normal', name='gru1_b')(x) +gru1_merged = merge([gru_1, gru_1b], mode='sum') + +gru_2 = GRU(rnn_size, return_sequences=True, init='he_normal', name='gru2')(gru1_merged) +gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, + init='he_normal', name='gru2_b')(gru1_merged) +x = merge([gru_2, gru_2b], mode='concat') +x = Dropout(0.25)(x) +x = Dense(n_class, init='he_normal', activation='softmax')(x) +base_model = Model(input=input_tensor, output=x) + +labels = Input(name='the_labels', shape=[n_len], dtype='float32') +input_length = Input(name='input_length', shape=[1], dtype='int64') +label_length = Input(name='label_length', shape=[1], dtype='int64') +loss_out = Lambda(ctc_lambda_func, output_shape=(1,), + name='ctc')([x, labels, input_length, label_length]) + +model = Model(input=[input_tensor, labels, input_length, label_length], output=[loss_out]) +model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer='adadelta') +``` + +# 模型可视化 + +可视化的代码同上，这里只贴图。 + +![](https://raw.githubusercontent.com/ypwhs/resources/master/captcha/model2.png) + +可以看到模型比上一个模型复杂了许多，但实际上只是因为输入比较多，所以它显得很大。还有一个值得注意的地方，我们的图片在输入的时候是经过了旋转的，这是因为我们希望以水平方向输入，而图片在 numpy 里默认是这样的形状：(height, width, 3)，因此我们使用了 `transpose` 函数将图片转为了(width, height, 3)的格式，然后经过各种卷积和降维，变成了 (17, 32)，这里的每个长度为32的向量都代表一个竖条的图片的特征，从左到右，一共有17条。然后我们兵分两路，一路从左到右输入到 GRU，一路从右到左输入到 GRU，然后将他们输出的结果加起来。再兵分两路，还是一路正方向，一路反方向，只不过第二次我们直接将它们的输出连起来，然后经过一个全连接，输出每个字符的概率。 + +# 数据生成器 + +```py +def gen(batch_size=128): + X = np.zeros((batch_size, width, height, 3), dtype=np.uint8) + y = np.zeros((batch_size, n_len), dtype=np.uint8) + while True: + generator = ImageCaptcha(width=width, height=height) + for i in range(batch_size): + random_str = ''.join([random.choice(characters) for j in range(4)]) + X[i] = np.array(generator.generate_image(random_str)).transpose(1, 0, 2) + y[i] = [characters.find(x) for x in random_str] + yield [X, y, np.ones(batch_size)*int(conv_shape[1]-2), + np.ones(batch_size)*n_len], np.ones(batch_size) +``` + +# 评估模型 + +```py +def evaluate(model, batch_num=10): + batch_acc = 0 + generator = gen() + for i in range(batch_num): + [X_test, y_test, _, _], _ = next(generator) + y_pred = base_model.predict(X_test) + shape = y_pred[:,2:,:].shape + ctc_decode = K.ctc_decode(y_pred[:,2:,:], + input_length=np.ones(shape[0])*shape[1])[0][0] + out = K.get_value(ctc_decode)[:, :4] + if out.shape[1] == 4: + batch_acc += ((y_test == out).sum(axis=1) == 4).mean() + return batch_acc / batch_num +``` + +我们会通过这个函数来评估我们的模型，和上面的评估标准一样，只有全部正确，我们才算预测正确，中间有个坑，就是模型最开始训练的时候，并不一定会输出四个字符，所以我们如果遇到所有的字符都不到四个的时候，就不计算了，相当于加0，遇到多于4个字符的时候，只取前四个。 + +# 评估回调 + +因为 Keras 没有针对这种输出计算准确率的选项，因此我们需要自定义一个回调函数，它会在每一代训练完成的时候计算模型的准确率。 + +```py +from keras.callbacks import * + +class Evaluate(Callback): + def __init__(self): + self.accs = [] + + def on_epoch_end(self, epoch, logs=None): + acc = evaluate(base_model)*100 + self.accs.append(acc) + print + print 'acc: %f%%'%acc + +evaluator = Evaluate() +``` + +# 训练模型 + +由于 CTC Loss 收敛很慢，所以我们需要设置比较大的代数，这里我们设置了100代，然后添加了一个早期停止的回调和我们上面定义的回调，但是第一次训练只训练37代就停了，测试准确率才95%，我又在这个基础上继续训练了一次，停在了25代，得到了98%的准确率，所以一共训练了62代。 + +```py +model.fit_generator(gen(128), samples_per_epoch=51200, nb_epoch=200, + callbacks=[EarlyStopping(patience=10), evaluator], + validation_data=gen(), nb_val_samples=1280) +``` + +![](https://raw.githubusercontent.com/ypwhs/resources/master/captcha/loss.png) + +# 测试模型 + +```py +characters2 = characters + ' ' +[X_test, y_test, _, _], _ = next(gen(1)) +y_pred = base_model.predict(X_test) +y_pred = y_pred[:,2:,:] +out = K.get_value(K.ctc_decode(y_pred, input_length=np.ones(y_pred.shape[0])*y_pred.shape[1], )[0][0])[:, :4] +out = ''.join([characters[x] for x in out[0]]) +y_true = ''.join([characters[x] for x in y_test[0]]) + +plt.imshow(X_test[0].transpose(1, 0, 2)) +plt.title('pred:' + str(out) + '\ntrue: ' + str(y_true)) + +argmax = np.argmax(y_pred, axis=2)[0] +list(zip(argmax, ''.join([characters2[x] for x in argmax]))) +``` + +这里随机出来的验证码很厉害，是`O0OP`，不过更厉害的是模型认出来了。 + +![](https://raw.githubusercontent.com/ypwhs/resources/master/captcha/O0OP.png) + +# 有趣的问题 + +我又用之前的模型做了个测试，对于 `O0O0` 这样丧心病狂的验证码，模型偶尔也能正确识别，这让我非常惊讶，它是真的能识别 O 与 0 的差别呢，还是猜出来的呢？这很难说。 + +```py +generator = ImageCaptcha(width=width, height=height) +random_str = 'O0O0' +X = generator.generate_image(random_str) +X = np.expand_dims(X, 0) + +y_pred = model.predict(X) +plt.title('real: %s\npred:%s'%(random_str, decode(y_pred))) +plt.imshow(X[0], cmap='gray') +``` + +![](https://raw.githubusercontent.com/ypwhs/resources/master/captcha/O0O0.png) + +# 总结 + +模型的大小是4.7MB，在我的笔记本上跑1000张验证码需要用14秒，平均一秒识别71张，估计可以拼过网速。 + +最后附上一张本模型识别 HACK 。 + +![](https://raw.githubusercontent.com/ypwhs/resources/master/captcha/HACK.png) + +# 参考链接 + +* [http://keras-cn.readthedocs.io/en/latest/getting_started/functional_API/](http://keras-cn.readthedocs.io/en/latest/getting_started/functional_API/) +* [https://github.com/fchollet/keras/blob/master/examples/image_ocr.py](https://github.com/fchollet/keras/blob/master/examples/image_ocr.py) +* [http://cs231n.github.io/convolutional-networks/](http://cs231n.github.io/convolutional-networks/) diff --git a/cnn.ipynb b/cnn_2017.ipynb similarity index 100% rename from cnn.ipynb rename to cnn_2017.ipynb diff --git a/cnn_2019.ipynb b/cnn_2019.ipynb new file mode 100644 index 0000000..1b49abe --- /dev/null +++ b/cnn_2019.ipynb @@ -0,0 +1,838 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 导入必要的库\n", + "\n", + "我们需要导入一个叫 [captcha](https://github.com/lepture/captcha/) 的库来生成验证码。\n", + "\n", + "我们生成验证码的字符由数字和大写字母组成。\n", + "\n", + "```sh\n", + "pip install captcha numpy matplotlib tensorflow-gpu\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T06:50:53.934028Z", + "start_time": "2019-06-16T06:50:53.690430Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ\n" + ] + } + ], + "source": [ + "from captcha.image import ImageCaptcha\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import random\n", + "\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "import string\n", + "characters = string.digits + string.ascii_uppercase\n", + "print(characters)\n", + "\n", + "width, height, n_len, n_class = 128, 64, 4, len(characters)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-15T16:05:54.140766Z", + "start_time": "2019-06-15T16:05:54.137935Z" + } + }, + "source": [ + "# 防止 tensorflow 占用所有显存" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T06:50:55.074201Z", + "start_time": "2019-06-16T06:50:53.935176Z" + } + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import tensorflow.keras.backend as K\n", + "\n", + "config = tf.ConfigProto()\n", + "config.gpu_options.allow_growth=True\n", + "sess = tf.Session(config=config)\n", + "K.set_session(sess)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 定义数据生成器" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T06:50:55.086492Z", + "start_time": "2019-06-16T06:50:55.077459Z" + }, + "code_folding": [] + }, + "outputs": [], + "source": [ + "from tensorflow.keras.utils import Sequence\n", + "\n", + "class CaptchaSequence(Sequence):\n", + " def __init__(self, characters, batch_size, steps, n_len=4, width=128, height=64):\n", + " self.characters = characters\n", + " self.batch_size = batch_size\n", + " self.steps = steps\n", + " self.n_len = n_len\n", + " self.width = width\n", + " self.height = height\n", + " self.n_class = len(characters)\n", + " self.generator = ImageCaptcha(width=width, height=height)\n", + " \n", + " def __len__(self):\n", + " return self.steps\n", + "\n", + " def __getitem__(self, idx):\n", + " X = np.zeros((self.batch_size, self.height, self.width, 3), dtype=np.float32)\n", + " y = [np.zeros((self.batch_size, self.n_class), dtype=np.uint8) for i in range(self.n_len)]\n", + " for i in range(self.batch_size):\n", + " random_str = ''.join([random.choice(self.characters) for j in range(self.n_len)])\n", + " X[i] = np.array(self.generator.generate_image(random_str)) / 255.0\n", + " for j, ch in enumerate(random_str):\n", + " y[j][i, :] = 0\n", + " y[j][i, self.characters.find(ch)] = 1\n", + " return X, y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 测试生成器" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T06:50:55.270137Z", + "start_time": "2019-06-16T06:50:55.087727Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5,1,'RJI4')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 213, + "width": 370 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "def decode(y):\n", + " y = np.argmax(np.array(y), axis=2)[:,0]\n", + " return ''.join([characters[x] for x in y])\n", + "\n", + "data = CaptchaSequence(characters, batch_size=1, steps=1)\n", + "X, y = data[0]\n", + "plt.imshow(X[0])\n", + "plt.title(decode(y))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 定义网络结构" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T06:50:55.858663Z", + "start_time": "2019-06-16T06:50:55.271265Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/ypw/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Colocations handled automatically by placer.\n" + ] + } + ], + "source": [ + "from tensorflow.keras.models import *\n", + "from tensorflow.keras.layers import *\n", + "\n", + "input_tensor = Input((height, width, 3))\n", + "x = input_tensor\n", + "for i, n_cnn in enumerate([2, 2, 2, 2, 2]):\n", + " for j in range(n_cnn):\n", + " x = Conv2D(32*2**min(i, 3), kernel_size=3, padding='same', kernel_initializer='he_uniform')(x)\n", + " x = BatchNormalization()(x)\n", + " x = Activation('relu')(x)\n", + " x = MaxPooling2D(2)(x)\n", + "\n", + "x = Flatten()(x)\n", + "x = [Dense(n_class, activation='softmax', name='c%d'%(i+1))(x) for i in range(n_len)]\n", + "model = Model(inputs=input_tensor, outputs=x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 网络结构可视化\n", + "\n", + "可视化需要安装下面的包：\n", + "\n", + "```sh\n", + "pip install pydot\n", + "sudo apt install graphviz\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T06:50:56.142853Z", + "start_time": "2019-06-16T06:50:55.860085Z" + }, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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ne3t76+vrFxYWvvvuu2pqahMnToyJiSGiY8eOsYcViaiysjIwMFBYPHXqVGpqamFh4ebNm/9OnImJibq6uklJSZ2e2UdGkZGR8d577/n4+Dg5Oc2aNeu///0vEX3zzTdDhw7l8Xj79+9vbm4mooiICAUFBbYKt66u7sCBAy4uLtOmTbOysvrtt99aWlp+/PFHDw8PfX39/Pz82bNnjxkzRvwVxQAAACBlHADAgBMVFSXOz7ecnBwiGj9+vEAgyM3NVVZWJqK9e/dmZ2efOXOGiCwsLJqbm+Pi4tgznG5ubklJSRERESoqKkR08+ZNjuMMDQ1F+xItssa7FHnbr5w/f37IkCGxsbHtfWX8+PGsx14bRcfj+uc//2loaMhxXGNj44gRI8zMzFj9jh07iOj3339nxZycnOXLl7PP69ev/+OPP9hna2trTU3N4uLiW7dusdW5/v7+V69edXFxqaqq6uDS2dnZ2dnZdXBC/yXm/Qz9DhFFRUVJOwoAgB6Bf7cAYAAS8/dygUAgmjKNGzdO+C2BQKCpqamgoMCKxsbGRFRdXc2KQUFBROTg4MCJpHmMaFEieSbHcU1NTR18pVUAvTCKjscVGBgYGRnJcVxLS4uhoaGcnByrLykpUVFRcXFxYUV/f/+4uDiO4+7cudP2/0DZITaW0tLSDoYvZGdnJ7H/ggXoLcgzAWCgwvOZADB4saWhryzyeDxVVdXi4mJWlJGRISI2VUhEy5Yt8/T0fPz4ce/EKSfXhZ/VUh+Fl5dXdXV1aGhoWVlZQ0MDWyVLROrq6lu2bAkICPjkk09GjRp1/fp19kaZe/fumZqaPnr0qL2xqKmpidn19OnTPT09JTSOPiQ5OTkoKCg6OlragYCE2dvbSzsEAICegjwTAKDLRo0aRUT9/W3sEh9FcXGxqqrqw4cPHRwcvvjiiw8//DAiIkL0BC8vr6NHjwYFBa1cudLCwoLlz6WlpVlZWTU1NcIEmIhaWlpkZWW7GoCuru6AnNXkOI6IBuTQAABgoMI+QAAAXVZaWkpEVlZW9HLOraGhgYgEAkFFRQW9TAyISDib93dIpJG2JDsKjuP+53/+R1ZWdu3atU1NTQsXLmRNibajoaHh6up67Nixo0ePfvDBB6zSxMSE7QMkbCo1NTUkJERi4wQAAIBehzwTAAavqqoqIqqsrGTF+vp6EkmK2NGmpibh+cJ06/r161OnTt20aRMRmZiYENHevXsfP3585MgRlqpdvny5paXFyMjo2bNnT58+FTOe2tpaImr1EpH4+PiRI0deunSp42/V1NT0zigKCgpYsyyHZCoqKjZt2qSkpCQjI/Ps2bP8/PyrV69GRES8ePGCiO7evZubm8vO9Pb2bmxsfPr06T//+U9WY2tra2Bg4Ofn5+LiEhERsXPnTg8Pj3Xr1gnHUl1dLeYFBAAAgD4CeSYADFI1NTX+/v5E9OzZs0OHDn322Wds+9l9+/ZVVFQcPnyYJVS7du2qq6tjXzl8+HBJSUlxcXFBQUFSUpK8vDwRHThwwMLC4tChQx9++OHixYvNzMwcHR1fvHjR3NxsZ2c3fPjwe/fuiRPPjRs3PDw8iCg7O/vzzz//9ddfWb2iouLw4cMVFRXbfuXnn3/28fFhKdzGjRsvXLgQGhrao6NITEx0dXUlooKCAlNT0zlz5syZM2f8+PGampphYWHW1tZE5O/vP3z48B07dhgZGe3YsWPkyJH+/v5Dhw5lvWtra1tbW7u4uAhHoaSklJiYuHTp0h9++MHb27u4uDgiIkJWVvbTTz9lY/Hy8nr48GFX/mwBAABAynjC//MGABgwoqOj2TaqkmrQxMQkPT29v//A7AujqKmpMTc3/+9//yvMPCWFbakyIDfLkfj9DH0Ej8eLiorCbkAAMCBhPhMAoDfw2peWlibt6HpPaGjoli1bJJ5kAgAAQJ+C/WYBADrHnn6srq5WUVHpXgt9YTLq74+i2+7cubNx48ba2tqWlpZBlVdDBzIzM+Pi4hoaGlasWMFe7goAAAMG5jMBADpSXV29ffv2vLw8InJ3d09OTpZ2RN0h9VEoKytXVlbKyMhERka+8llTkKCMjIyAgICoqChzc3Mej2dmZsY2i2KuXbtmY2PD4/GmTZsWFRUllQgrKyvd3Nysra0nT57M5/PbJpnBwcGtXm/bsfz8/PDwcHt7++nTp4vWcxx34sSJKVOmqKiomJubh4eHC//Hh+O406dPL126dNu2bXPmzHF1dS0vLxezu0ePHtna2qqrq2toaKxcuZI9Bd3c3Lx161b2twwAAIgDABhw2G/P0o4CepWdnZ2dnV3Ptf/06VNpNdKl+/nGjRurVq1qaGjgOI69n4aINmzYIHpOVlYWEaWlpXUjmL+vqKjotddeMzY2Li4ufuUJd+/eHTJkSFf/CrNdo8aPHy9a6ePjs2bNmpCQEHd3dyUlJSI6evQoO/Tll18SUUJCAsdxv//+OxHZ2tqK09GjR4+WL19+7ty5Bw8eODo6EtHcuXPZoZKSkhUrVjx58kTMmIkoKipK3BECAPQrmM8EAADoRFZW1urVq/tCIx1LTU1du3ZtcHCwgoICEQ0fPpyIZs2aFRYWJjp1OXr0aCIyMDDo0WBeieO4999//z//+c/p06f/8Y9/tD2hvLz8woULenp6XW15zJgxrWpyc3Nzc3O/+eabDz/88MiRI+fPnyeiI0eOsKOnT58mojfeeIOITE1NNTQ0rl+/Lk5H7J09K1aseO2118LDw0eMGJGSksIOqaur79mzZ9myZXgZDwAA8kwAAICO5OXlLVmy5Pnz51JvpGMCgcDR0XHdunXq6uqi9VFRUdra2hs3bszMzGQ17GU2LBftZfHx8ZcuXbKxsXnrrbfaHuU4zs/Pj8/nd2nRbHtycnICAwOFxfnz52toaBQXF7OimpoaEf34449EVFNTU1ZWNnfuXHGa/eijj0Q3smpubhZ9T4+5ubmRkRGfz//78QMA9GvIMwEAYBCpqKjg8/m+vr5eXl7z58/38vJiT+UdO3aMbf9LRJWVlYGBgcLiqVOnUlNTCwsLN2/ezHFccnKyt7e3vr5+YWHhu+++q6amNnHixJiYGPEbYZEkJibq6uomJSVJamixsbEPHz5csGBBq3odHZ3o6OiampqVK1c2NjaKeU04jrtw4cLGjRt1dXXLy8udnZ3V1dUnTpx4//599q26uroDBw64uLhMmzbNysrqt99+EyfIr7/+mojGjBljaWmpoqIyderU+Ph44dHg4GAHB4cRI0Z08xL81cyZM7W1tUVrGhsbZ82axT4HBQUZGhp6eHjk5OSEhITw+fzIyMgutS8QCHbv3n348OHDhw+L1tvY2ISFhT158uRvxg8A0L9Jd9kuAEBPwPOZg5A4z2dWVlYaGxvv2bOHFYuKioyNjQ0MDFhaZWhoKHrbiBbp5YN/zc3NcXFx7Ek/Nze3pKSkiIgItn/vzZs3xWyEOX/+/JAhQ2JjYzsdmpj388qVK4mosbFRtFL4xUOHDhGRt7d3q/r2rklZWVlubq6ysjIR7d27Nzs7+8yZM0RkYWHBzly/fv0ff/zBPltbW2tqalZUVHQa5NixY4koICCgoKAgOTlZV1eXiFJSUjiOu337dmBgIDtt/Pjx3fgrTG2ezxR18+ZNJSWlX375RVhTXFw8Y8aM0aNHe3p6drWvc+fOsZRVX18/LCxMIBAIDz148ICI/P39xQkYz2cCwECF38MAYABCnjkIiZNnbt++nYgKCgqENWx6jc/nc21yG9FiqwSG7Y9aXV3NikFBQUTk4ODQpUY4jmtqahJnaGLez2PHjh0xYkSrSuEXBQLBe++9Ry93vhHWd3xNxo0bJ9qCpqamgoICx3F37txp+z/XcXFxnQapqKiora0tLLLcdc2aNSUlJR988EFLSwurl3ie2dTUZGlpGRkZKVqZnZ29ePFiNgP88ccfC3sXR1lZ2aNHj4KDg9mWRSdPnhQeys/PJ6KFCxeKEzDyTAAYqLBuFgAABotbt24R0bBhw4Q1lpaWRHT79u0utSMjI0NEbK6PiJYtW0ZEjx8/7mo8cnKSfIt1YWGhqqpqe0d5PF54eLiJiYmzszN7DwfT8TURfU6Sx+Opqqqylbf37t0zNTVt9SvFkiVLOg1SW1ubPR3KzJkzh4jS09NdXV0dHR0zMjLS0tLS0tIaGhqIKC0tTVKrTz/55JN58+atWrVKWJOSkvL66687OzufP39+xowZAQEBu3fvFr9BVVVVU1NTNze3Y8eO0ctdhZiRI0cSUVFRkUQiBwDop5BnAgDAYMHyw+zsbGGNlpYWEf3NBwJHjRpFRN3YIlWyZGVlW1paOjhh2LBhMTExdXV1a9asEVZ275qUlpZmZWXV1NSIVnbcOyN8nQkramhoEJGamlpsbOzcuXMnvMTimTBhgo2NTadtdiouLk5ZWblVGrlt27bS0tJ33nlHUVHx7NmzRHT8+PFuNG5ra0t/3VSJJefCMQIADE7IMwEAYLBgM3UJCQnCmtzcXCKysrKil+kBm0kTCATs5ZPCbKG5ubm9ZktLS7vXSAdtdoOOjs6LFy9Ea1jiJ5r+mZqahoeHs01WmY6vSXtMTEzYPkDCmtTU1JCQkE6DXL16dUNDw6+//sqKJSUlRGRhYVFfXy86NSpcN/vnn3922mbHrly5kp+f7+vrK6xhU7VsYpblh3p6epqamt3b5PbZs2dEtGjRImEN21lKR0fn7wUOANC/Ic8EAIDBYuvWrWZmZsHBwSw3IKLQ0NAZM2a4ubkRkYmJCRHt3bv38ePHR44cYbni5cuXW1pajIyMnj179vTpU9HWhFni9evXp06dumnTpi41Eh8fP3LkyEuXLklqdLNnz66qqqqqqhLWsHd4tFrAaW9v7+HhIeY1qa+vJ5E8mTXe1NRka2trYGDg5+fn4uISERGxc+dODw+PdevWEVFAQICpqem33377yiCdnJzMzMwOHjzI2vzhhx+0tLS8vLw6HlrHbTK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GrKwsMzOz7Ozsbrqi1NTUW7duLVy4sFW7qalpcnJyfX29h4dHc3Nz2wNramr4fH5oaGhgYKCTk1NgYGB1dYrV7FIAACAASURBVLWce8KOamho2Llzp4+Pz9SpU+fNm/fbb791KluJRPL5559HR0dHR0cT0YwZM0xMTGQ7NDc3z5w5U5FQ33zzDRGZm5s7Ojrq6upOmTIlPT1dujc2Ntbd3X3YsGGdSu9VYi5YsODgwYP37t3r1BkBADqBAwDoJCJKSkpSdRYAfU8XvndKSkqIaMKECRKJRCgUDhkyhIi2b99eXFx87NgxInJwcBCLxWlpaWwOp5+fX3Z2dmJiInskMicnh+M4NhNPGlN2kwWX7jpz5sygQYNSU1M7e2murq6urq4ddvPw8CCi5uZm2UZpMlFRUUQUFBTUqr22ttbS0nLbtm1ss7Ky0tLS0sLC4unTp+3dE9bzww8//OOPP9jX8+fPNzIyqqmpUfCKUlJSWA05ZsyYgwcPSiSSVh1ycnJ0dHR+/vlnRaKNHj2aiPbs2VNeXi4QCMzMzIgoLy+P47jc3NzIyEjWjU1GVTDDV4l58+ZNIoqIiJB/CrYIk4L5AADIws8OAOg01JkAXdOF7x2JRCJbCo4fP176d79EIjEyMtLS0mKbbKHRuro6trl3714icnd359pUGrKbrepMjuNEIlGnL0zhOnP06NHDhg1r1Sh7RcuXLyeijIwM2fYtW7YQUXl5ufQQNpTH5/O59u/JtWvX2v7zelpamoJX9PTp09u3b8fGxg4aNIiIDh8+LLtXJBI5OjqeOHFCwWja2tomJibSTVYPr1q1qqqq6oMPPmhpaWHtnaozXyVmWVkZES1atEj+KVBnAkCX4blZAACA3qvV5DrZTR6Pp6+vL33KVE1NjYjY4B4ROTs7E9Hdu3c7e0YNjW5cI7CiokJfX7+9vTweLyEhwcrKytvbW/oqESK6evUqEQ0dOlTa4ujoSES5ubnU/j25fv26tbV1q797lixZomCq+vr61tbWfn5++/fvJ6KjR4/K7v3iiy/mzp27YsUKBaOZmJiwGafM7NmziejOnTvr16/39PQsLCwsKCgoKChoamoiooKCAkWeaH2VmMOHDyeiyspKBfMHAOgsrDcLAADQD73++utE1MMvLOmQurp6S0uLnA5Dhw49deqUg4PDqlWrpI2shC4uLra1tWUtxsbGRCR/QuOTJ0/u379fX18vrb2JqKWlRV1dvVM5u7i40H+vSJSWljZkyJDQ0FDFg1haWl65coXjOFYVGxoaEtGIESNSU1NPnjzZqvObb745bty4P//8s/tiskO4zrz8EwCgUzCeCQAA0A89efKEiObNm0d/FRVsXEsikdTU1JBMjSEWi2UPbLWpXKamps+ePZNtYWWnbPFpbW2dkJBw+fJlaQsbvczIyJC2CIVC+uvq2mNlZcXWAZK25Ofnx8XFdTbnhw8fEtHixYvZ5vnz58vKymSLTDasKt/KlSubmpp++eUXtllVVUVEDg4OjY2NssOt0mdcOywyXzFmdXU1EZmamipyBwAAugB1JgAAQO/1/PlzIqqtrWWbjY2NJFMisr0ikUjaX1olXrx4ccqUKR999BERWVlZEdH27dvv3r0bExPDCs4ff/yxpaVl3LhxDx8+fPDgATsqPT19+PDh586d66bLmTVr1vPnz1naDHsvSKsHON3c3DZu3CjdDA4OtrGxiY2NZSUfEcXHx0+fPt3Pz4/avycuLi4WFhZhYWE+Pj6JiYlbt27duHHj2rVriWjPnj3W1tbffvvtS5OMior6+uuvWT3c2NgYEhLi5ubGzpWZmbljxw6xWBwXFxcXFxcbGxsQEPDDDz90GHP16tU2Nja7d+9meZ4+fdrY2DgwMFD+7eqOmAwrSmfMmKFIZwCALkCdCQAA0EvV19dHREQQ0cOHD6Oior788ku2/Gx4eHhNTU10dDSbxPjZZ581NDSwQ6Kjo6uqqh49elReXp6dnc3m7+3cudPBwSEqKuqTTz559913bWxsPD09nz17JhaLXV1d9fT0rl+/zg7X1tbW09PT1tbupivy8vIiIoFAwDZTUlLYqyl9fX2vXLki23PXrl1/+9vf2NeDBw8WCAQrV6709vYOCgoKDg42NDTMysrS1NSMj49v755wHJeVlbV06dLTp08HBQU9evQoMTFRT0+PiIqKigoKCjZt2vTSJGtra7/88ksLC4v169eHhIT4+fl99913ampqubm5zs7Oly5d2vAXf3//6OjoDz74oMOYGhoaV65c0dHR8fb23rp167Vr127cuCFnqirTHTGZq1evqqmpubu7K9IZAKALeHg0HwA6i8fjJSUlubm5qToRgD6mW793rKys7ty5o6pf6+yikpOTO+y5ePHi8ePHszdSqlBhYeHq1avz8vIGYEwiWrp0qYmJycGDB+V3S05OZksWK/fsADAQYDwTAAAAes7hw4czMjIqKipUmEN9ff2+ffsOHTo0AGMSkUAgKCwsjIyMVG5YAABZqDMBADpQWVmZnJwcHh6uqgRarZsCjMo/l96mvr6eiOrq6lSdSAeMjY1PnToVEBDAElaJoqKiiIgIOzu7ARizrKwsPDw8MzOTPUIMANBNUGcCAMjzxx9//Otf/3J3d2fvQGfefvttPp//KmE5jjt06NCkSZN0dXXt7e0TEhLaPpnW2NgYHh4+bdo0AwMDRWKWlZUlJCS4ublNmzZNwTRSUlJcXV15PB6Px7t06VLbDlevXmV733///Zd2UEROTs6CBQt4PJ6amtr8+fNnz549c+ZMPz+/V3l3Xw9/LhcuXFi0aBG7FbNnz549e/bUqVOdnZ0PHTrE1tRRrbq6ui1btpSWlhKRv7+/dPZjrzVx4sTw8PD4+HhVJWBnZ6f0KqtPxBSJRMeOHTtx4kRve+ENAPQ/mJ8JAJ020OZnNjY2Dho0aMKECQUFBazFw8PD0tIyLCysyzFDQ0NLS0unTZtWWFh44MCBxsbGffv2bdiwoVW3hoaGkSNHVldXK/iz+sGDB6NHj5ZNtUMvXrxgbxdcunRpampqq70rVqw4e/ZsQ0PDw4cPTUxMFIzZVllZmZmZ2RtvvHH37l0iqqysXLly5c8//5yZmTl16tSuxezhz4VdwpgxY+7fv09EEokkPT09ICBATU3tzJkzNjY2isTvx987is/PhD4E8zMBoMs0VJ0AAEBvp6Oj06rlu+++e5WAQqFQKBQmJiayzcWLFy9cuDAmJqZtnTlo0CAjIyP2pjtFmJubdzaZwYMHE9H06dPT09Pv3r1raWkp3fXw4cOnT5+am5vfuXPnVYpMIho5ciQRqaurs01jY+OYmBg7O7uIiIiUlJSuxezhz4VdgnQhVjU1NWdn57feemvKlCkuLi6//fbboEGDXuXsAAAA/QmemwUA6GklJSWyK3A4OTkZGhqytwiqysaNGzmOi4mJkW08cODA+vXru+mMo0ePJqKysrJuit8FXfhcTE1Nw8LC7t27hyVVAAAAZKHOBADlq6+vP378+IoVK6ZPny4QCCZPnjx69OicnJw7d+689957hoaGVlZWN27ckPYvLCxcvnx5SEjI6tWrZ86c+Z///IeIfv311/nz5/N4vKVLlz558oTP548aNero0aNyzstxnEAgCAoKGjNmTEVFxfvvvz9ixAhbW9tTp06xDjU1NXw+PzQ0NDAw0MnJKTAwUDpUKGeXrJaWluTkZG9vb0dHR47jzp496+vra2ZmVl1d7e3tbWBgYGtrK700juNiY2M9PT3Xr1+vra3N+8uMGTNaDQ82NzfPnDmTff3ixYvAwEBfX9+tW7du3rz51ddKycrKMjMzy87OltNn2bJl5ubmhw8fll51c3Pzjz/+uHTp0radlfJ5/fTTT0TEXpDYJz6X9ixfvlxNTe38+fPyuwEAAAwsHABAJxFRUlKSnA4tLS1sGp6enl56evrt27eJaPTo0bt27Xr27NnNmzeJaNasWdL+b7zxxtixYzmOa25uHjZsmI2NDWuvq6t78803LSwsGhsbly5dyt4NKIdYLE5LS2OPU/r5+WVnZycmJurq6hJRTk5ObW2tpaXltm3bWOfKykpLS0sLC4vq6mo5u6SXPGHCBPY1eyn8hAkTJBKJUChkkxu3b99eXFzM1qRxcHBgPfft26emplZVVcVxXEREBBEFBga2TTsnJ0dHR+fnn3/mOE4kEjk4OHz44YcSiYTjuD///JM9ayr/wmXJpsqcOXNm0KBBqampcg7hOG737t1EtHPnTtb47bff7t69m+O4CRMmtEqga58XEVlaWorF4qqqqtOnT5ubmw8dOvSPP/7oE59Le/eWMTExGTFiRHu3t9VNkP+903e5urq6urqqOgtQsqSkJPytCABdg3WAAKDTFFnLhOM4NTU16RotI0eOLC8v5/4qaYyMjEQikfR1HVFRUaampitWrJBIJJaWlg8ePBCJRGzX9evXp02b9tZbb/n6+q5du1aR9MaPH3/37t26ujpWaURHRwcEBLi7u48bNy4iIqK8vNzU1JT1PHr0qLe3N5/P19TUbG/Xrl272CVLr6XVpU2YMKGwsFB6aSYmJs+ePWNrkDo7O6enpzc2Nmppaf3+++92dnZvv/32tWvXZLMVi8Vz5879+OOPV6xYQURxcXEbNmy4ffu2tbW17OUo/rNaNlXZs2hotDshn8fjcRz37NkzMzMzfX39oqIiTU1NJyenpKQkfX19KysrVjFK+3ft8+LxeOwLbW1tIyMjJyen4ODg8ePH/7//9/96/+ci594S0ahRo1paWsrLy+V/NCzCO++80y+X+mSL3Cq+1jH0CUKh8Nq1a/hbEQC6AM/NAkC3kBYVzNChQ2V3GRgY1NTUSFsCAwOXLl0aHx8fHh7e1NQkFoulu956662QkJC8vLxJkyYpeGo1NTUiYkUmETk7OxPR3bt3r1692ioTR0dHIsrNzZWzq8NLk93k8Xj6+vrNzc1sc/78+RzHZWRk0F+L1syZM6dVtC+++GLu3LnSYoY9fmlhYdHqcl6RnCJTavjw4WvXri0tLT116tStW7fGjh2rr6//0p5d/rzYYGBjY+ODBw8OHTo0fvx4IuoTn4sczc3NlZWViv//CQAAMBBgvVkAUL2ffvrJ3d39q6+++uSTT6SrfTISieTevXujRo3y8vK6ceOGdLVPxb3++utENGrUqNraWiIqLi62tbVlu4yNjYlo2LBhDQ0N7e16levy8/MbNGiQj4/P1atX7969+8UXX2zZskW2Q1pa2pAhQ0JDQ6UtbF2cJ0+emJmZvcqpu8bf3z8+Pn7v3r12dnaBgYHtdVPu58UK6V7+uciRlZUlEonmzp2r4NkDAgLwXhPoK9h7TVSdBQD0SRjPBADV8/LyEolEixYtIiKJREJE0se0du3a9Y9//CMhIeH333/ftm1bF4I/efKEiObNm8eGwtgoFiMUCjvc1dVrIiJqaWn5/fffr127tmfPnrNnz37++eey44rnz58vKyuTLWZyc3OtrKxaZaIUskOObZOU/tfS0nLJkiU//fRTWVmZ9MHdto/MdeHzkvPcXZ/4XNoL1dTUtGXLlkmTJvn7+79KSgAAAP0MxjMBoFuwkShpdcHm7z1//pw9A9nY2EhELS0tbJGbhw8f1tbWXrhw4dGjR2zS5k8//fT666+XlZX9+uuv7C/+9evX7969e9GiRbNmzVIkAemMxIsXL06ZMuWjjz4SiUTff/99bGysl5cXm+8XHx8/ffp0Pz8/ObuI6MWLF9Kc2VUQERsdlbZzHMce1GR7RSIRm1uYlpZmZ2dXVFSkp6dnaGg4duxYllVmZuaOHTv+8Y9/xMXFscOLioqGDBnC5/OTkpK2bNkyevToWbNmCQQCNuvv/v37sg/TtoelyopGqfT0dA8Pj5MnT7LKsBX23o7Kyko28BsQEJCWlvY///M/0g5swduGhgbp+yG78Hk9ePCA/vq/opXg4ODe/7lMnz691emI6ObNmxs3bqyurs7IyNDU1Ozw0wEAABhAemrBIQDoP6ijNTMrKioCAgKISEtL68KFC//+979ZPblhw4aqqqp9+/axnz87d+58/Pgxx3FxcXF6enpvvfWWQCCIjo4ePny4s7Pz/v37DQ0NP/74YxZz8+bNRDRs2LCEhAT56bH1UXfv3v348ePKysovv/zy+fPnbFdtbS2fz58/f35gYCCfz//Xv/7V2Ngof9e9e/c2bNjAEt67d69QKJSOdEVGRrLVSokoLCzs2bNne/fuZZshISEvXrw4f/68kZGR7I9cQ0PD77///urVq9KaTda9e/c4jsvOzp4+fbquru7YsWO//PLLmTNnfvTRR5mZmWKxWP6FZ2VlrVu3jog0NDR27tx569Yt1n7+/HlTU9OLFy+2PeTMmTNLliwhonfffTczM5PjOIlEsmzZMnau27dvSx8odXV1zcrKYkd19vO6du2aq6sri/M///M/AoGgVRq9/3O5cuXKBx98wDZnzZrl5OS0dOlSVo5K/+9SRIffO30X1pvtl7DeLAB0GdabBYBOU2S9WRVquz6qSnAcd/jw4cePH4eEhBARW4/00qVLmzZtYkOIoBKq/Vx6+ffOq8D8zH6Jzc9U+Y9TAOiL8NwsAPQ9rVYWlfXHH3/0ZCZy7Ny5c/PmzVVVVWxTXV191KhRM2bMGDlyZNcCyr9qNrETOqT0zwUAAADaQp0JAH2P/H9cZ/MJ6+rqdHV1eyqjl8jJySGi//u///voo48MDQ2J6ObNmzt27Dh+/HjXAmJIQSmU/rnAgFVUVJSWltbU1LRs2TJLS0skAwAgC+vNAkD/UVdXt2XLltLSUiLy9/dnL45XlW+++cbPz+/rr782MzObPn26q6vrzz//fPz4cRsbGxVmBfhceonCwsI9e/YkJSXZ29vzeDwbGxu20hKTmZm5YMECHo83depUNkWwh5WVlSUkJLi5uU2bNq3t3traWj8/v/nz50+cOJHP5ytY192+fdvFxcXAwMDQ0NDDw4Mt8UVEHMcdOnRo0qRJurq69vb2bAq64ql2mExsbKz0aQixWBwcHMx+SAIAdC+VzQwFgD6L+u9aJgDdqlu/dx48eKDCIJ1aB+jSpUsrVqxoamriOK6mpob9QbJu3TrZPvfv3yeigoKCruXz6kpKSohowoQJrdorKysnT55saWn56NEjxaPdvn37vffeS0lJuXnzpqenJxHNmTOH7QoJCVm1alVcXJy/v7+Ojg4R7du3T8GwHSbz008/sdWtpC1VVVXLli1jq451COsAAUCXYTwTAACgz7t///7KlSt7Q5AO5efne3l5xcbGamlpEZGenh4RzZw58+DBg7JDl2zGrCKv8+km5ubmbRs5jluzZs2vv/569OjR1157TfFoFy5cSExMXLZs2eTJkxMSEoYNG5aXl0dEQqFQKBQeP378k08+iYmJOXPmDBHFxMQoErPDZKqrq8+ePTtq1CjZRgMDg23btjk7O9fV1SmePwBAZ6HOBAAA6NtKS0uXLFny+PFjlQfpkEQi8fT0XLt2rYGBgWx7UlKSiYmJr69vUVERa2GvJGW1aO+Rnp5+7ty5BQsWvPPOO5068NNPPx08eLB0UywW+/j4EFFJSUlkZKS03cnJydDQUMGlj+Unw3FcWFgYn89vu4SYvb39uHHj+Hx+py4BAKBTUGcCAAD0IjU1NXw+PzQ0NDAw0MnJKTAwsLq6moj279/P4/FYzVBbWxsZGSndPHLkSH5+fkVFBXt/qUAgCAoKGjNmTEVFxfvvvz9ixAhbW9tTp04pHoRlkpWVZWZmlp2drcSrS01NvXXr1sKFC1u1m5qaJicn19fXe3h4NDc3K3hbOI47e/asr6+vmZlZdXW1t7e3gYGBra3tjRs32FENDQ07d+708fGZOnXqvHnzfvvtt1fM/5tvviEic3NzR0dHXV3dKVOmpKendyqCRCL5/PPPo6Ojo6OjiWjGjBkmJiayHZqbm2fOnPnqycTGxrq7uw8bNuylxy5YsODgwYP37t3rVPIAAJ2g0qd2AaBPIszPBOiSDr93amtrLS0tt23bxjYrKystLS0tLCxYTTV27FjZX9yym/TXTEKxWJyWlsam+fn5+WVnZycmJrK1l3NychQMwpw5c2bQoEGpqamKXJqC8zM9PDyIqLm5WbZRmkBUVBQRBQUFtWpv77Y8ffpUKBQOGTKEiLZv315cXHzs2DEicnBwYD0//PDDP/74g309f/58IyOjmpoaRS5HmkCr+ZmjR48moj179pSXlwsEAjMzMyLKy8tTMGBKSgqrIceMGXPw4EGJRNKqQ05Ojo6Ozs8//6xINDnJ5ObmRkZGsm4TJkxo+/fezZs3iSgiIkL+KTA/EwC6DD87AKDTUGcCdE2H3ztbtmwhovLycmkLG7Pi8/lcm4JBdrNVRcQWHa2rq2Obe/fuJSJ3d/dOBeE4TiQSKXhpCtaZo0ePHjZsWKtGaQISiWT58uVElJGRIdsu/7aMHz9eNoKRkZGWlhbHcdeuXWv7z+tpaWkKXhH3shuira1tYmIi3WRl7apVqxQM+PTp09u3b8fGxrK1eQ4fPiy7VyQSOTo6njhxQsFo7SVTVVX1wQcftLS0sPaX1pllZWVEtGjRIvmnQJ0JAF2G52YBAAB6i6tXrxLR0KFDpS2Ojo5ElJub26k4ampqRMQG+ojI2dmZiO7evdvZfDQ0lPye7YqKCn19/fb28ni8hIQEKysrb29v6Ws/qKPbIjv/kMfj6evrsydvr1+/bm1t3ervniVLlrxK/iYmJmziKDN79mwiunPnjoKH6+vrW1tb+/n57d+/n4iOHj0qu/eLL76YO3fuihUrXjGZ9evXe3p6FhYWFhQUFBQUNDU1EVFBQYHsU7LDhw8nosrKSgXPBQDQWagzAQAAegtWHxYXF0tbjI2Niai9WXYKev3114mo1bqjKqGurt7S0iKnw9ChQ0+dOtXQ0LBq1SppY9duy5MnT+7fv19fXy/bKP/sHZK+QYRtGhoaEtGIESM6G8fFxYX+e5WjtLS0IUOGfP7556+eTGpq6pw5c978C7tvb7755oIFC6THsuKc68yLOgEAOgV1JgAAQG/BhukyMjKkLUKhkIjmzZtHf9UGbHhKIpGwN09KSwWxWNxe2CdPnnQtiJyYXWNqavrs2TPZFlb4yZZ/1tbWCQkJly9flrbIvy3tsbKyYusASVvy8/Pj4uJeJf+VK1c2NTX98ssvbLOqqoqIHBwcOhvn4cOHRLR48WK2ef78+bKystDQUGkHRUaw20umsbFRdghX+tzsn3/+KT2WLS5lamra2cwBABSEOhMAAKC3CA4OtrGxiY2NZXUIEcXHx0+fPt3Pz4+IrKysiGj79u13796NiYlhteKPP/7Y0tIybty4hw8fPnjwQDaatEq8ePHilClTPvroo04FSU9PHz58+Llz55R4gbNmzXr+/Pnz58+lLewdHq0e4HRzc9u4caOCt6WxsZFkSmUWXCQSubi4WFhYhIWF+fj4JCYmbt26dePGjWvXriWiPXv2WFtbf/vtt3JSffHiBbUZ/1y9erWNjc3u3bvZ6U6fPm1sbBwYGNhhzKioqK+//prV2I2NjSEhIW5ubiz/zMzMHTt2iMXiuLi4uLi42NjYgICAH374ocOYcpLpECtKZ8yYoUhnAIAuUPK8CwAAAOiywYMHCwSCsLAwb29vOzs7dXV1Q0PDrKwsNg1v586d5eXlUVFReXl5cXFxKSkpY8aMefbsmVgsdnV1PXLkyPXr183NzaXRoqOj16xZI5FIysvLs7OzOxtEW1tbT09PW1tbiRfo5eWVkJAgEAicnJyIKCUl5dChQ0Tk6+sbEhIi+z6PXbt2Xb9+vcPbEh8fX1JSQkTh4eEbNmw4fPgwm9j52Wefbdu2LSsry9/f//Tp0xkZGc7OzomJiXp6ekRUVFRUUFCwadOm9iZDXrp0iVV3xcXFu3btcnJymjRpEhFpaGhcuXIlKCjI29vb3Ny8uLj4xo0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Io6NjSEhIZWWllBvCWtXX10dERPj6+o4fP97e3v7XX3+VMc6YmBhPT09tbe0OjS46OtrMzCwoKKiwsDA2NpbP5584caLdVlOmTDE0NJQsaWxsnDp1KhHV1NQ4OTlNmjQpKytr27ZtFhYWW7duFVf7+uuviWjIkCF2dnaamprjxo1LTU2V7MfJyenQoUN5eXkdGgUAQO/HAQC8A9gyJ4qOArpC5z7rwsJCIho1apRIJBIIBP369SOi7du3FxQUHD9+nIgmTpzY1NSUkpLC3uEMCAjIyMhISEhgEyavX7/OcRx7T0/cp+Qh61x86uzZs3369Dl//nxH4ySixMRE6XUWLlxIRI2NjS0asi/27NlDRGvWrGlRXlNTY25uvnnzZnZYXl5ubm5uamr64sWLtm4Iq7lixYoHDx6wrx0cHPT19aurq9sdyI0bN6KiotjX7O3QdpuIVVRU2NraDh48ODg4WPZWkq5fv66hofHzzz9LFlZVVW3fvl1ZWZmIDh8+zAqHDh1KRJGRkSUlJVlZWcbGxkR08+ZNcas7d+4QUXh4eLsXleWzAwDoNfBXFwC8E5Bnvjs691mLRCLJVJDtaSE+pa+vr6amxg7Zhha1tbXs8P+yd+dxURzp/8Cf4fZEjnAoqKAoggY1aiKreIsnJhsBDw6NSsKuEg5HwfVYRYhGEBVIjBo0GlQweIDgxlUiGxGI5tokyBERwiGggCDIMTD9+6PW/s4PZBjMwAz6ef/hi67uqn6qp18Dj9VVHRYWRkTOzs5cm3xJcrNVnslxnEgk6myQnGy5ypAhQ7S1tdtW5LuzdOlSIkpMTJQs37JlCxGVlJTwVdhQnlAo5Nq/IOnp6W3/CzshIUF6hI8ePXrvvfdaWlrYZmfzzPz8/IULF7IB240bN/LtyEgkEtnZ2Z0+ffq5ew8fPkxE48aNY5uamppGRkb8XpZjr1y5ki8pLi4movnz53d4XuSZAPBKwXOzAAAA1GqKoOSmQCDQ0dHhHzRVUVEhIja+R0QODg5ElJub29kzqql11VJ8paWlOjo67e0VCARRUVGWlpbu7u4lJSV8eWpqKhH169ePL7GzsyOiW7duUfsX5Pbt21ZWVq3+tli0aJH0CD09PV1cXHJycrKysrKyshobG4koKytLlqdPMzIy3njjDXd394sXL9ra2oaEhGzfvr3DWpJ27tw5a9as5cuXP3fv2rVrtbS0+HWJjIyM2CxWZsaMGUSUnZ3NlwwYMICIysrKOhUDAMBLD3kmAADAixs4cCARdecLSzqkqqra0tIi5YB+/frFxcXV19evXLmSL2T5c35+Pl9iaGhIRNLnT1ZUVNy/f7+urk6yUPrZiSg+Pn7mzJmjnmEnHTVqlL29vfSKRBQQEFBRUTF9+nRNTU22RDD/phZZJCQk9OnTR0pqqqqqqqurO3z4cLZpYWFRXl7OPXvTpr6+Pj1bi4hhGTgn8SpOAAAg5JkAAAB/RkVFBRHNnj2bnqUcbHROLBZXV1eTRAbS3NwsWbHVphwZGxs/fvxYsoQlfpLpn5WVVVRUFFu1lWGjl4mJiXxJYWEhPetaeywtLdk6QHxJZmZmRESE9AgbGhokxz/552Z///33DnvHxlHZGrmmpqYGBgayL1d79erV4uJif39/voSN1koqLi4uKSlxdHRkmytWrGhsbPzpp5/Y5qNHj4ho0qRJ/PFVVVVEZGxsLGMMAACvCOSZAAAA9OTJEyKqqalhmw0NDSSRIrK9IpGIP57PEq9fvz5+/Pj333+fiCwtLYlo9+7dubm5Bw8eZAnn119/3dLSMmzYsAcPHvzxxx+s1uXLlwcMGHDlypWu6Mu0adOePHnCYmbYCzxaPdvp5OTk7e3Nb27atMna2jo8PPzBgwesJDIy0tbWdv369dT+BVmyZImZmVlgYOCaNWuio6O3bt3q7e29evVqIgoJCbGysjpz5kxn45deccWKFUSUlJRERAUFBeXl5Wzdow4rXrt2bc+ePc3NzREREREREeHh4T4+PklJSTt37vTy8rp79y4R1dfXe3p6vv3223wu6urqam1tvW/fPtb3CxcuGBoa+vr68s2yzHPKlCmd7SYAwEuuOyaBAgAoGtYBenW8wGddW1vL5xWhoaHBwcHs58DAwMePH7OVfoho8+bNT58+ZYNv+/bte/jwYVlZ2UcfffTkyRPWTnZ29qRJk3r37j1nzpzs7OwpU6a4uLicOXOmoaHB39/fyMjoq6++YkdevXrV2Nj4+vXrne0dybCWDBul/Prrr9lmXFzc/PnziWjhwoX/+c9/JI9samr6y1/+wm/W1NQIhcI5c+b4+voKhcJdu3axgUd+fPK5F+T+/fuLFy/W0dExNDRct24de8qU4zhPT0+BQDBw4MAOO9VqHSDpFcVicURExMSJE319fd9+++1t27bV19d3WDE1NbVXr15t/wq6d+9eVFSUjY1N7969ly9fvnr16vj4eLFYLFm3srJy9erVrq6u//jHP1auXFlYWCi595NPPlFRUbl3716H3ZTlswMAeGkIOMwoAIBXQGxsLFsRVNGBQJfr6s/a0tIyOztbUfeSQCCIiYlxcnKSftiCBQtGjBhx4MCB7omqPTk5Oa6urhkZGcpf8YUtXrzYyMjo6NGjHR4p42cHAPBywHOzAAAAL5vjx48nJiaWlpYqMIa6urpDhw4dO3ZM+Su+sLS0tJycnNDQ0G47IwBAT4E8EwBA2bVa0wUUi62tWltbq+hApDE0NIyLi/Px8Wm1Emx3ysvLCw4OHjNmjPJXfDHFxcVBQUHXrl3r379/95wRAKAHQZ4JAKAAHMcdO3Zs7Nixffv2tbGxiYqKavscZkNDQ1BQ0OTJk/X09GRp8OTJk4sXLw4ICJgxY4anpydbBlPGYGJjYxctWjRu3Li5c+c6ODj8/e9/37Nnj5+fX6c7JvMZn9v9f//73/PnzxcIBAKBYMaMGTNmzJgwYYKDg8OxY8fYmjqKVVtbu2XLlqKiIiLy8vJKS0tTdETSvP7660FBQZGRkYoKYMyYMS+WgHV/xRcgEolOnTp1+vRppXqlDQCAElHUxFAAgO6kbOsAbd68eeXKlREREV5eXlpaWkR06NChtoc9ffpUR0dHlsg//fRTIkpMTOQ47tdffyWiJUuWyBJJeXn59OnThw0blp6ezpY/aWlpOXXqlK6u7nvvvdfJbslKSvdZFjd06FC22dLScunSJXNz8+HDh//666+yNK5sn7V8EdaS6bHw2QHAK0VNUfktAMArq7CwsLCwMDo6mm0uWLBg3rx5Bw8e3LBhQ6sje/XqZWBgIMvI5MmTJ4lo4sSJRGRlZaWvr3/9+vUOa4nF4iVLlmRlZeXm5vKjpioqKi4uLiYmJocPH+5Uv2QkvfuDBg0iIk1NTT4YBweHiRMnjh8/fsmSJb/88stzVw0FAAAApYLnZgEAultBQYHkwiFz587V19dnbzh8Ybq6ukTEXmhRV1dXWVk5c+bMDmudP38+LS3N39+/7aO506dP519VL18v0H1jY+PAwMB79+5hwRUAAIAeAXkmAMD/qa2tDQwMdHFx8fLymjZt2oEDBziOI6Lq6mqhUOjv7+/r6zt37lxfX9+qqiqO4y5duuTh4WFiYlJVVeXu7q6npzd69Og7d+4Q0blz53R1dQUCwdatW1nj7D17R44cmTJlipGRkeR5m5qapk6dyn5++vSpr6+vh4fH1q1bAwICZFzHJSwszNzc3Nvbu6CgICIiQigUnj59mu1KTk42MTFJSUlpW+v8+fNENGvWrOe2+e6777IfurP77Vm6dKmKisrVq1dluRoAAACgYAp+bhcAoFvIMmevqalp2rRpLi4uLS0tHMdFRUURUXx8fE1NjYWFxY4dO9hhZWVlFhYWZmZmlZWVhYWFffr0IaLdu3fn5+efOnWKiCZNmsSOPHToEBElJSWxzYKCguXLl7c9782bN7W0tL7//nuO40Qi0aRJk9auXcumSv7++++qqqoyfleXl5fb2toOGjTIx8dHsvzixYu9evWKj49vW2XChAlE9PjxYynNdmf3GSIaOXJk2yONjIx0dXWlhMpgfiYoJ3x2APBKwfxMAID/CQ8PT0lJycrKUlFRISJXV1cimjJlyp49e3Jzc99//312mIGBwdatW93d3T/66KOPP/540KBBOTk5//jHP4ho8ODBfn5+P/30Ezvy/fff37dv36effjp//nwiOnr0qFAobHXS5ubmLVu2REVFjR8/nogOHz783XffHT9+XCAQENGwYcPMzc1zc3NliZ8tGtS/f/+wsDBVVdW9e/eyjixZsqSmpkZN7Tlf+CyJffr0qba2dnvNdmf3pVNTU2OXRRZOTk4yHtnjhIWFffXVV4qOAgAAQBo8NwsA8D9scqOJiQnbVFNTW716tY6OTmpqKhH169ePP9LOzo6Ibt26RUSSmY9AINDR0WlqamKbGhoaH3744eXLl+/du9fU1JSdnT1u3LhWJ925c+esWbOWL1/ONtlzoWZmZvwBLFfsUEZGxhtvvOHu7n7x4kVbW9uQkJDt27fze5+bZBKRlZUVEd29e1dKy93ZfSmamprKysrGjh3b4ZEAAACgcBjPBAD4n7KyMiLKzc1tlcywTC8/P3/06NGsxNDQkIikjAHy1q5d+89//jMiImLy5MlLly5ttTchIaFPnz7+/v58SXFxMRFVVFTw6a6MAgICKioqpk+frqmpefbs2cGDBx85cmT37t3Sa02bNu348ePp6elSFg3qzu5LkZycLBKJ2ptK2lZsbKyMR/YsAoHAx8fnJR6tfYnJPhoPAPASwHgmAMD/2NjYEFFQUJBYLGYl+fn5SUlJbPguMTGRP7KwsJCIZs+e3WGb2traa9eujYqKiomJeeeddyR3Xb16tbi4WDLLunXrlqWlZatzyYiNImpoaBCRqampgYGB5B+1zc3Nz63l4uIyfvz4gwcPlpSUtNrV0NBw4sQJejZ62T3db6+dxsbGLVu2jB071svLq8O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+ "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from tensorflow.keras.utils import plot_model\n", + "from IPython.display import Image\n", + "\n", + "plot_model(model, to_file='cnn.png', show_shapes=True)\n", + "Image('cnn.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T06:50:56.164154Z", + "start_time": "2019-06-16T06:50:56.145970Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_1 (InputLayer) (None, 64, 128, 3) 0 \n", + "__________________________________________________________________________________________________\n", + "conv2d (Conv2D) (None, 64, 128, 32) 896 input_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_v1 (BatchNo (None, 64, 128, 32) 128 conv2d[0][0] \n", + "__________________________________________________________________________________________________\n", + "activation (Activation) (None, 64, 128, 32) 0 batch_normalization_v1[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_1 (Conv2D) (None, 64, 128, 32) 9248 activation[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_v1_1 (Batch (None, 64, 128, 32) 128 conv2d_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "activation_1 (Activation) (None, 64, 128, 32) 0 batch_normalization_v1_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d (MaxPooling2D) (None, 32, 64, 32) 0 activation_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_2 (Conv2D) (None, 32, 64, 64) 18496 max_pooling2d[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_v1_2 (Batch (None, 32, 64, 64) 256 conv2d_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "activation_2 (Activation) (None, 32, 64, 64) 0 batch_normalization_v1_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_3 (Conv2D) (None, 32, 64, 64) 36928 activation_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_v1_3 (Batch (None, 32, 64, 64) 256 conv2d_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "activation_3 (Activation) (None, 32, 64, 64) 0 batch_normalization_v1_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d_1 (MaxPooling2D) (None, 16, 32, 64) 0 activation_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_4 (Conv2D) (None, 16, 32, 128) 73856 max_pooling2d_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_v1_4 (Batch (None, 16, 32, 128) 512 conv2d_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "activation_4 (Activation) (None, 16, 32, 128) 0 batch_normalization_v1_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_5 (Conv2D) (None, 16, 32, 128) 147584 activation_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_v1_5 (Batch (None, 16, 32, 128) 512 conv2d_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "activation_5 (Activation) (None, 16, 32, 128) 0 batch_normalization_v1_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d_2 (MaxPooling2D) (None, 8, 16, 128) 0 activation_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_6 (Conv2D) (None, 8, 16, 256) 295168 max_pooling2d_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_v1_6 (Batch (None, 8, 16, 256) 1024 conv2d_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "activation_6 (Activation) (None, 8, 16, 256) 0 batch_normalization_v1_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_7 (Conv2D) (None, 8, 16, 256) 590080 activation_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_v1_7 (Batch (None, 8, 16, 256) 1024 conv2d_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "activation_7 (Activation) (None, 8, 16, 256) 0 batch_normalization_v1_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d_3 (MaxPooling2D) (None, 4, 8, 256) 0 activation_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_8 (Conv2D) (None, 4, 8, 256) 590080 max_pooling2d_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_v1_8 (Batch (None, 4, 8, 256) 1024 conv2d_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "activation_8 (Activation) (None, 4, 8, 256) 0 batch_normalization_v1_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_9 (Conv2D) (None, 4, 8, 256) 590080 activation_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_v1_9 (Batch (None, 4, 8, 256) 1024 conv2d_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "activation_9 (Activation) (None, 4, 8, 256) 0 batch_normalization_v1_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d_4 (MaxPooling2D) (None, 2, 4, 256) 0 activation_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "flatten (Flatten) (None, 2048) 0 max_pooling2d_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "c1 (Dense) (None, 36) 73764 flatten[0][0] \n", + "__________________________________________________________________________________________________\n", + "c2 (Dense) (None, 36) 73764 flatten[0][0] \n", + "__________________________________________________________________________________________________\n", + "c3 (Dense) (None, 36) 73764 flatten[0][0] \n", + "__________________________________________________________________________________________________\n", + "c4 (Dense) (None, 36) 73764 flatten[0][0] \n", + "==================================================================================================\n", + "Total params: 2,653,360\n", + "Trainable params: 2,650,416\n", + "Non-trainable params: 2,944\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 训练模型" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T07:23:32.907096Z", + "start_time": "2019-06-16T06:50:56.167239Z" + }, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/ypw/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.cast instead.\n", + "Epoch 1/100\n", + "100/100 [==============================] - 11s 112ms/step - loss: 0.5755 - c1_loss: 0.1017 - c2_loss: 0.1806 - c3_loss: 0.1910 - c4_loss: 0.1022 - c1_acc: 0.9613 - c2_acc: 0.9398 - c3_acc: 0.9373 - c4_acc: 0.96260s - loss: 0.5744 - c1_loss: 0.1027 - c2_loss: 0.1813 - c3_loss: 0.1893 - c4_loss: 0.1010 - c1_acc: 0.9607 - c2_acc: 0.9397 - c3_acc: 0.9375 - c4_acc: \n", + "1000/1000 [==============================] - 117s 117ms/step - loss: 5.1894 - c1_loss: 1.2421 - c2_loss: 1.3584 - c3_loss: 1.3576 - c4_loss: 1.2314 - c1_acc: 0.6572 - c2_acc: 0.6260 - c3_acc: 0.6268 - c4_acc: 0.6603 - val_loss: 0.5755 - val_c1_loss: 0.1017 - val_c2_loss: 0.1806 - val_c3_loss: 0.1910 - val_c4_loss: 0.1022 - val_c1_acc: 0.9613 - val_c2_acc: 0.9398 - val_c3_acc: 0.9373 - val_c4_acc: 0.9626\n", + "Epoch 2/100\n", + "100/100 [==============================] - 11s 113ms/step - loss: 0.3528 - c1_loss: 0.0927 - c2_loss: 0.0852 - c3_loss: 0.1172 - c4_loss: 0.0577 - c1_acc: 0.9692 - c2_acc: 0.9684 - c3_acc: 0.9626 - c4_acc: 0.9774\n", + "1000/1000 [==============================] - 124s 124ms/step - loss: 0.3010 - c1_loss: 0.0589 - c2_loss: 0.0892 - c3_loss: 0.0908 - c4_loss: 0.0621 - c1_acc: 0.9769 - c2_acc: 0.9674 - c3_acc: 0.9669 - c4_acc: 0.9763 - val_loss: 0.3528 - val_c1_loss: 0.0927 - val_c2_loss: 0.0852 - val_c3_loss: 0.1172 - val_c4_loss: 0.0577 - val_c1_acc: 0.9692 - val_c2_acc: 0.9684 - val_c3_acc: 0.9626 - val_c4_acc: 0.9774\n", + "Epoch 3/100\n", + "100/100 [==============================] - 12s 116ms/step - loss: 0.1761 - c1_loss: 0.0352 - c2_loss: 0.0542 - c3_loss: 0.0526 - c4_loss: 0.0342 - c1_acc: 0.9862 - c2_acc: 0.9795 - c3_acc: 0.9806 - c4_acc: 0.9864\n", + "1000/1000 [==============================] - 124s 124ms/step - loss: 0.2053 - c1_loss: 0.0435 - c2_loss: 0.0592 - c3_loss: 0.0581 - c4_loss: 0.0444 - c1_acc: 0.9823 - c2_acc: 0.9772 - c3_acc: 0.9776 - c4_acc: 0.9824 - val_loss: 0.1761 - val_c1_loss: 0.0352 - val_c2_loss: 0.0542 - val_c3_loss: 0.0526 - val_c4_loss: 0.0342 - val_c1_acc: 0.9862 - val_c2_acc: 0.9795 - val_c3_acc: 0.9806 - val_c4_acc: 0.9864\n", + "Epoch 4/100\n", + "100/100 [==============================] - ETA: 0s - loss: 0.1907 - c1_loss: 0.0484 - c2_loss: 0.0540 - c3_loss: 0.0497 - c4_loss: 0.0386 - c1_acc: 0.9817 - c2_acc: 0.9806 - c3_acc: 0.9809 - c4_acc: 0.985 - 11s 112ms/step - loss: 0.1907 - c1_loss: 0.0481 - c2_loss: 0.0546 - c3_loss: 0.0498 - c4_loss: 0.0381 - c1_acc: 0.9818 - c2_acc: 0.9804 - c3_acc: 0.9809 - c4_acc: 0.9853\n", + "1000/1000 [==============================] - 116s 116ms/step - loss: 0.1715 - c1_loss: 0.0369 - c2_loss: 0.0497 - c3_loss: 0.0478 - c4_loss: 0.0371 - c1_acc: 0.9855 - c2_acc: 0.9811 - c3_acc: 0.9816 - c4_acc: 0.9857 - val_loss: 0.1907 - val_c1_loss: 0.0481 - val_c2_loss: 0.0546 - val_c3_loss: 0.0498 - val_c4_loss: 0.0381 - val_c1_acc: 0.9818 - val_c2_acc: 0.9804 - val_c3_acc: 0.9809 - val_c4_acc: 0.9853\n", + "Epoch 5/100\n", + "100/100 [==============================] - 11s 114ms/step - loss: 0.1973 - c1_loss: 0.0330 - c2_loss: 0.0600 - c3_loss: 0.0652 - c4_loss: 0.0391 - c1_acc: 0.9865 - c2_acc: 0.9770 - c3_acc: 0.9773 - c4_acc: 0.9852\n", + "1000/1000 [==============================] - 125s 125ms/step - loss: 0.1435 - c1_loss: 0.0312 - c2_loss: 0.0395 - c3_loss: 0.0401 - c4_loss: 0.0327 - c1_acc: 0.9873 - c2_acc: 0.9846 - c3_acc: 0.9843 - c4_acc: 0.9874 - val_loss: 0.1973 - val_c1_loss: 0.0330 - val_c2_loss: 0.0600 - val_c3_loss: 0.0652 - val_c4_loss: 0.0391 - val_c1_acc: 0.9865 - val_c2_acc: 0.9770 - val_c3_acc: 0.9773 - val_c4_acc: 0.9852\n", + "Epoch 6/100\n", + "100/100 [==============================] - 11s 111ms/step - loss: 0.1199 - c1_loss: 0.0286 - c2_loss: 0.0317 - c3_loss: 0.0359 - c4_loss: 0.0236 - c1_acc: 0.9874 - c2_acc: 0.9873 - c3_acc: 0.9874 - c4_acc: 0.9918\n", + "1000/1000 [==============================] - 125s 125ms/step - loss: 0.1323 - c1_loss: 0.0289 - c2_loss: 0.0378 - c3_loss: 0.0362 - c4_loss: 0.0293 - c1_acc: 0.9883 - c2_acc: 0.9851 - c3_acc: 0.9858 - c4_acc: 0.9882 - val_loss: 0.1199 - val_c1_loss: 0.0286 - val_c2_loss: 0.0317 - val_c3_loss: 0.0359 - val_c4_loss: 0.0236 - val_c1_acc: 0.9874 - val_c2_acc: 0.9873 - val_c3_acc: 0.9874 - val_c4_acc: 0.9918\n", + "Epoch 7/100\n", + "100/100 [==============================] - 11s 111ms/step - loss: 0.1114 - c1_loss: 0.0277 - c2_loss: 0.0327 - c3_loss: 0.0263 - c4_loss: 0.0247 - c1_acc: 0.9881 - c2_acc: 0.9876 - c3_acc: 0.9898 - c4_acc: 0.9896\n", + "1000/1000 [==============================] - 118s 118ms/step - loss: 0.1151 - c1_loss: 0.0256 - c2_loss: 0.0313 - c3_loss: 0.0326 - c4_loss: 0.0256 - c1_acc: 0.9896 - c2_acc: 0.9879 - c3_acc: 0.9878 - c4_acc: 0.9898 - val_loss: 0.1114 - val_c1_loss: 0.0277 - val_c2_loss: 0.0327 - val_c3_loss: 0.0263 - val_c4_loss: 0.0247 - val_c1_acc: 0.9881 - val_c2_acc: 0.9876 - val_c3_acc: 0.9898 - val_c4_acc: 0.9896\n", + "Epoch 8/100\n", + "100/100 [==============================] - 11s 113ms/step - loss: 0.2318 - c1_loss: 0.0401 - c2_loss: 0.0547 - c3_loss: 0.0624 - c4_loss: 0.0746 - c1_acc: 0.9855 - c2_acc: 0.9816 - c3_acc: 0.9793 - c4_acc: 0.9766\n", + "1000/1000 [==============================] - 124s 124ms/step - loss: 0.1075 - c1_loss: 0.0234 - c2_loss: 0.0302 - c3_loss: 0.0292 - c4_loss: 0.0246 - c1_acc: 0.9908 - c2_acc: 0.9884 - c3_acc: 0.9888 - c4_acc: 0.9902 - val_loss: 0.2318 - val_c1_loss: 0.0401 - val_c2_loss: 0.0547 - val_c3_loss: 0.0624 - val_c4_loss: 0.0746 - val_c1_acc: 0.9855 - val_c2_acc: 0.9816 - val_c3_acc: 0.9793 - val_c4_acc: 0.9766\n", + "Epoch 9/100\n", + "100/100 [==============================] - 12s 116ms/step - loss: 0.1007 - c1_loss: 0.0221 - c2_loss: 0.0269 - c3_loss: 0.0262 - c4_loss: 0.0255 - c1_acc: 0.9910 - c2_acc: 0.9898 - c3_acc: 0.9898 - c4_acc: 0.9900\n", + "1000/1000 [==============================] - 125s 125ms/step - loss: 0.1025 - c1_loss: 0.0233 - c2_loss: 0.0270 - c3_loss: 0.0288 - c4_loss: 0.0235 - c1_acc: 0.9905 - c2_acc: 0.9896 - c3_acc: 0.9892 - c4_acc: 0.9909 - val_loss: 0.1007 - val_c1_loss: 0.0221 - val_c2_loss: 0.0269 - val_c3_loss: 0.0262 - val_c4_loss: 0.0255 - val_c1_acc: 0.9910 - val_c2_acc: 0.9898 - val_c3_acc: 0.9898 - val_c4_acc: 0.9900\n", + "Epoch 10/100\n", + "100/100 [==============================] - 11s 113ms/step - loss: 0.0988 - c1_loss: 0.0233 - c2_loss: 0.0255 - c3_loss: 0.0324 - c4_loss: 0.0176 - c1_acc: 0.9898 - c2_acc: 0.9892 - c3_acc: 0.9874 - c4_acc: 0.9936\n", + "1000/1000 [==============================] - 118s 118ms/step - loss: 0.0957 - c1_loss: 0.0210 - c2_loss: 0.0264 - c3_loss: 0.0271 - c4_loss: 0.0213 - c1_acc: 0.9918 - c2_acc: 0.9899 - c3_acc: 0.9895 - c4_acc: 0.9918 - val_loss: 0.0988 - val_c1_loss: 0.0233 - val_c2_loss: 0.0255 - val_c3_loss: 0.0324 - val_c4_loss: 0.0176 - val_c1_acc: 0.9898 - val_c2_acc: 0.9892 - val_c3_acc: 0.9874 - val_c4_acc: 0.9936\n", + "Epoch 11/100\n", + "100/100 [==============================] - 11s 114ms/step - loss: 0.1169 - c1_loss: 0.0254 - c2_loss: 0.0365 - c3_loss: 0.0310 - c4_loss: 0.0241 - c1_acc: 0.9899 - c2_acc: 0.9868 - c3_acc: 0.9882 - c4_acc: 0.9912\n", + "1000/1000 [==============================] - 125s 125ms/step - loss: 0.0869 - c1_loss: 0.0196 - c2_loss: 0.0240 - c3_loss: 0.0237 - c4_loss: 0.0195 - c1_acc: 0.9923 - c2_acc: 0.9907 - c3_acc: 0.9908 - c4_acc: 0.9924 - val_loss: 0.1169 - val_c1_loss: 0.0254 - val_c2_loss: 0.0365 - val_c3_loss: 0.0310 - val_c4_loss: 0.0241 - val_c1_acc: 0.9899 - val_c2_acc: 0.9868 - val_c3_acc: 0.9882 - val_c4_acc: 0.9912\n", + "Epoch 12/100\n", + "100/100 [==============================] - 11s 113ms/step - loss: 0.0882 - c1_loss: 0.0177 - c2_loss: 0.0246 - c3_loss: 0.0274 - c4_loss: 0.0185 - c1_acc: 0.9930 - c2_acc: 0.9902 - c3_acc: 0.9902 - c4_acc: 0.993110s - loss: 0.0898 - c1_loss: 0.0173 - c2_loss: 0.0231 - c3_loss: 0.0292 - c4_loss: 0.0202 - c1_\n", + "1000/1000 [==============================] - 124s 124ms/step - loss: 0.0821 - c1_loss: 0.0189 - c2_loss: 0.0218 - c3_loss: 0.0231 - c4_loss: 0.0183 - c1_acc: 0.9925 - c2_acc: 0.9915 - c3_acc: 0.9912 - c4_acc: 0.9928 - val_loss: 0.0882 - val_c1_loss: 0.0177 - val_c2_loss: 0.0246 - val_c3_loss: 0.0274 - val_c4_loss: 0.0185 - val_c1_acc: 0.9930 - val_c2_acc: 0.9902 - val_c3_acc: 0.9902 - val_c4_acc: 0.9931\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 13/100\n", + "100/100 [==============================] - 11s 113ms/step - loss: 0.0880 - c1_loss: 0.0201 - c2_loss: 0.0258 - c3_loss: 0.0234 - c4_loss: 0.0188 - c1_acc: 0.9929 - c2_acc: 0.9903 - c3_acc: 0.9911 - c4_acc: 0.9927\n", + "1000/1000 [==============================] - 119s 119ms/step - loss: 0.0767 - c1_loss: 0.0165 - c2_loss: 0.0208 - c3_loss: 0.0224 - c4_loss: 0.0169 - c1_acc: 0.9936 - c2_acc: 0.9921 - c3_acc: 0.9914 - c4_acc: 0.9931 - val_loss: 0.0880 - val_c1_loss: 0.0201 - val_c2_loss: 0.0258 - val_c3_loss: 0.0234 - val_c4_loss: 0.0188 - val_c1_acc: 0.9929 - val_c2_acc: 0.9903 - val_c3_acc: 0.9911 - val_c4_acc: 0.9927\n", + "Epoch 14/100\n", + "100/100 [==============================] - 11s 114ms/step - loss: 0.1016 - c1_loss: 0.0195 - c2_loss: 0.0286 - c3_loss: 0.0259 - c4_loss: 0.0276 - c1_acc: 0.9925 - c2_acc: 0.9895 - c3_acc: 0.9897 - c4_acc: 0.9899\n", + "1000/1000 [==============================] - 125s 125ms/step - loss: 0.0760 - c1_loss: 0.0174 - c2_loss: 0.0196 - c3_loss: 0.0217 - c4_loss: 0.0173 - c1_acc: 0.9930 - c2_acc: 0.9928 - c3_acc: 0.9915 - c4_acc: 0.9934 - val_loss: 0.1016 - val_c1_loss: 0.0195 - val_c2_loss: 0.0286 - val_c3_loss: 0.0259 - val_c4_loss: 0.0276 - val_c1_acc: 0.9925 - val_c2_acc: 0.9895 - val_c3_acc: 0.9897 - val_c4_acc: 0.9899\n", + "Epoch 15/100\n", + "100/100 [==============================] - 11s 113ms/step - loss: 0.1207 - c1_loss: 0.0227 - c2_loss: 0.0433 - c3_loss: 0.0292 - c4_loss: 0.0254 - c1_acc: 0.9917 - c2_acc: 0.9857 - c3_acc: 0.9898 - c4_acc: 0.9908\n", + "1000/1000 [==============================] - 124s 124ms/step - loss: 0.0700 - c1_loss: 0.0151 - c2_loss: 0.0192 - c3_loss: 0.0191 - c4_loss: 0.0167 - c1_acc: 0.9942 - c2_acc: 0.9927 - c3_acc: 0.9928 - c4_acc: 0.9936 - val_loss: 0.1207 - val_c1_loss: 0.0227 - val_c2_loss: 0.0433 - val_c3_loss: 0.0292 - val_c4_loss: 0.0254 - val_c1_acc: 0.9917 - val_c2_acc: 0.9857 - val_c3_acc: 0.9898 - val_c4_acc: 0.9908\n", + "Epoch 16/100\n", + "100/100 [==============================] - 11s 113ms/step - loss: 2.5607 - c1_loss: 0.3619 - c2_loss: 0.5187 - c3_loss: 0.7467 - c4_loss: 0.9335 - c1_acc: 0.8973 - c2_acc: 0.8633 - c3_acc: 0.8213 - c4_acc: 0.7772\n", + "1000/1000 [==============================] - 117s 117ms/step - loss: 0.0681 - c1_loss: 0.0153 - c2_loss: 0.0184 - c3_loss: 0.0190 - c4_loss: 0.0155 - c1_acc: 0.9943 - c2_acc: 0.9931 - c3_acc: 0.9928 - c4_acc: 0.9940 - val_loss: 2.5607 - val_c1_loss: 0.3619 - val_c2_loss: 0.5187 - val_c3_loss: 0.7467 - val_c4_loss: 0.9335 - val_c1_acc: 0.8973 - val_c2_acc: 0.8633 - val_c3_acc: 0.8213 - val_c4_acc: 0.7772\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from tensorflow.keras.callbacks import EarlyStopping, CSVLogger, ModelCheckpoint\n", + "from tensorflow.keras.optimizers import *\n", + "\n", + "train_data = CaptchaSequence(characters, batch_size=128, steps=1000)\n", + "valid_data = CaptchaSequence(characters, batch_size=128, steps=100)\n", + "callbacks = [EarlyStopping(patience=3), CSVLogger('cnn.csv'), ModelCheckpoint('cnn_best.h5', save_best_only=True)]\n", + "\n", + "model.compile(loss='categorical_crossentropy',\n", + " optimizer=Adam(1e-3, amsgrad=True), \n", + " metrics=['accuracy'])\n", + "model.fit_generator(train_data, epochs=100, validation_data=valid_data, workers=4, use_multiprocessing=True,\n", + " callbacks=callbacks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 载入最好的模型继续训练一会" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T07:50:24.448235Z", + "start_time": "2019-06-16T07:23:32.909470Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "100/100 [==============================] - 11s 112ms/step - loss: 0.0601 - c1_loss: 0.0147 - c2_loss: 0.0165 - c3_loss: 0.0169 - c4_loss: 0.0120 - c1_acc: 0.9951 - c2_acc: 0.9939 - c3_acc: 0.9935 - c4_acc: 0.9952\n", + "1000/1000 [==============================] - 128s 128ms/step - loss: 0.0586 - c1_loss: 0.0135 - c2_loss: 0.0160 - c3_loss: 0.0155 - c4_loss: 0.0136 - c1_acc: 0.9946 - c2_acc: 0.9937 - c3_acc: 0.9940 - c4_acc: 0.9948 - val_loss: 0.0601 - val_c1_loss: 0.0147 - val_c2_loss: 0.0165 - val_c3_loss: 0.0169 - val_c4_loss: 0.0120 - val_c1_acc: 0.9951 - val_c2_acc: 0.9939 - val_c3_acc: 0.9935 - val_c4_acc: 0.9952\n", + "Epoch 2/100\n", + "100/100 [==============================] - 11s 111ms/step - loss: 0.0578 - c1_loss: 0.0118 - c2_loss: 0.0172 - c3_loss: 0.0156 - c4_loss: 0.0132 - c1_acc: 0.9956 - c2_acc: 0.9928 - c3_acc: 0.9937 - c4_acc: 0.9948\n", + "1000/1000 [==============================] - 125s 125ms/step - loss: 0.0550 - c1_loss: 0.0118 - c2_loss: 0.0153 - c3_loss: 0.0152 - c4_loss: 0.0127 - c1_acc: 0.9954 - c2_acc: 0.9942 - c3_acc: 0.9940 - c4_acc: 0.9949 - val_loss: 0.0578 - val_c1_loss: 0.0118 - val_c2_loss: 0.0172 - val_c3_loss: 0.0156 - val_c4_loss: 0.0132 - val_c1_acc: 0.9956 - val_c2_acc: 0.9928 - val_c3_acc: 0.9937 - val_c4_acc: 0.9948\n", + "Epoch 3/100\n", + "100/100 [==============================] - 11s 113ms/step - loss: 0.0538 - c1_loss: 0.0107 - c2_loss: 0.0150 - c3_loss: 0.0146 - c4_loss: 0.0135 - c1_acc: 0.9958 - c2_acc: 0.9941 - c3_acc: 0.9949 - c4_acc: 0.9945\n", + "1000/1000 [==============================] - 118s 118ms/step - loss: 0.0527 - c1_loss: 0.0119 - c2_loss: 0.0144 - c3_loss: 0.0149 - c4_loss: 0.0115 - c1_acc: 0.9952 - c2_acc: 0.9943 - c3_acc: 0.9942 - c4_acc: 0.9954 - val_loss: 0.0538 - val_c1_loss: 0.0107 - val_c2_loss: 0.0150 - val_c3_loss: 0.0146 - val_c4_loss: 0.0135 - val_c1_acc: 0.9958 - val_c2_acc: 0.9941 - val_c3_acc: 0.9949 - val_c4_acc: 0.9945\n", + "Epoch 4/100\n", + "100/100 [==============================] - 11s 112ms/step - loss: 0.0496 - c1_loss: 0.0116 - c2_loss: 0.0147 - c3_loss: 0.0126 - c4_loss: 0.0106 - c1_acc: 0.9956 - c2_acc: 0.9937 - c3_acc: 0.9954 - c4_acc: 0.9960\n", + "1000/1000 [==============================] - 126s 126ms/step - loss: 0.0507 - c1_loss: 0.0115 - c2_loss: 0.0138 - c3_loss: 0.0132 - c4_loss: 0.0122 - c1_acc: 0.9954 - c2_acc: 0.9945 - c3_acc: 0.9948 - c4_acc: 0.9953 - val_loss: 0.0496 - val_c1_loss: 0.0116 - val_c2_loss: 0.0147 - val_c3_loss: 0.0126 - val_c4_loss: 0.0106 - val_c1_acc: 0.9956 - val_c2_acc: 0.9937 - val_c3_acc: 0.9954 - val_c4_acc: 0.9960\n", + "Epoch 5/100\n", + "100/100 [==============================] - 11s 112ms/step - loss: 0.0504 - c1_loss: 0.0109 - c2_loss: 0.0152 - c3_loss: 0.0139 - c4_loss: 0.0103 - c1_acc: 0.9948 - c2_acc: 0.9948 - c3_acc: 0.9947 - c4_acc: 0.9962\n", + "1000/1000 [==============================] - 124s 124ms/step - loss: 0.0501 - c1_loss: 0.0107 - c2_loss: 0.0141 - c3_loss: 0.0134 - c4_loss: 0.0119 - c1_acc: 0.9958 - c2_acc: 0.9944 - c3_acc: 0.9948 - c4_acc: 0.9954 - val_loss: 0.0504 - val_c1_loss: 0.0109 - val_c2_loss: 0.0152 - val_c3_loss: 0.0139 - val_c4_loss: 0.0103 - val_c1_acc: 0.9948 - val_c2_acc: 0.9948 - val_c3_acc: 0.9947 - val_c4_acc: 0.9962\n", + "Epoch 6/100\n", + "100/100 [==============================] - 11s 111ms/step - loss: 0.0573 - c1_loss: 0.0127 - c2_loss: 0.0159 - c3_loss: 0.0158 - c4_loss: 0.0129 - c1_acc: 0.9952 - c2_acc: 0.9937 - c3_acc: 0.9935 - c4_acc: 0.9945\n", + "1000/1000 [==============================] - 119s 119ms/step - loss: 0.0476 - c1_loss: 0.0105 - c2_loss: 0.0134 - c3_loss: 0.0130 - c4_loss: 0.0107 - c1_acc: 0.9958 - c2_acc: 0.9950 - c3_acc: 0.9949 - c4_acc: 0.9957 - val_loss: 0.0573 - val_c1_loss: 0.0127 - val_c2_loss: 0.0159 - val_c3_loss: 0.0158 - val_c4_loss: 0.0129 - val_c1_acc: 0.9952 - val_c2_acc: 0.9937 - val_c3_acc: 0.9935 - val_c4_acc: 0.9945\n", + "Epoch 7/100\n", + "100/100 [==============================] - 11s 110ms/step - loss: 0.0483 - c1_loss: 0.0110 - c2_loss: 0.0136 - c3_loss: 0.0132 - c4_loss: 0.0106 - c1_acc: 0.9954 - c2_acc: 0.9944 - c3_acc: 0.9952 - c4_acc: 0.9959\n", + "1000/1000 [==============================] - 125s 125ms/step - loss: 0.0472 - c1_loss: 0.0101 - c2_loss: 0.0133 - c3_loss: 0.0131 - c4_loss: 0.0107 - c1_acc: 0.9962 - c2_acc: 0.9949 - c3_acc: 0.9948 - c4_acc: 0.9960 - val_loss: 0.0483 - val_c1_loss: 0.0110 - val_c2_loss: 0.0136 - val_c3_loss: 0.0132 - val_c4_loss: 0.0106 - val_c1_acc: 0.9954 - val_c2_acc: 0.9944 - val_c3_acc: 0.9952 - val_c4_acc: 0.9959\n", + "Epoch 8/100\n", + "100/100 [==============================] - 11s 112ms/step - loss: 0.0511 - c1_loss: 0.0118 - c2_loss: 0.0150 - c3_loss: 0.0125 - c4_loss: 0.0119 - c1_acc: 0.9953 - c2_acc: 0.9947 - c3_acc: 0.9956 - c4_acc: 0.9947\n", + "1000/1000 [==============================] - 125s 125ms/step - loss: 0.0457 - c1_loss: 0.0101 - c2_loss: 0.0121 - c3_loss: 0.0128 - c4_loss: 0.0107 - c1_acc: 0.9960 - c2_acc: 0.9952 - c3_acc: 0.9952 - c4_acc: 0.9960 - val_loss: 0.0511 - val_c1_loss: 0.0118 - val_c2_loss: 0.0150 - val_c3_loss: 0.0125 - val_c4_loss: 0.0119 - val_c1_acc: 0.9953 - val_c2_acc: 0.9947 - val_c3_acc: 0.9956 - val_c4_acc: 0.9947\n", + "Epoch 9/100\n", + "100/100 [==============================] - 11s 112ms/step - loss: 0.0491 - c1_loss: 0.0123 - c2_loss: 0.0122 - c3_loss: 0.0121 - c4_loss: 0.0125 - c1_acc: 0.9956 - c2_acc: 0.9950 - c3_acc: 0.9958 - c4_acc: 0.9948\n", + "1000/1000 [==============================] - 117s 117ms/step - loss: 0.0434 - c1_loss: 0.0100 - c2_loss: 0.0120 - c3_loss: 0.0120 - c4_loss: 0.0094 - c1_acc: 0.9960 - c2_acc: 0.9953 - c3_acc: 0.9955 - c4_acc: 0.9964 - val_loss: 0.0491 - val_c1_loss: 0.0123 - val_c2_loss: 0.0122 - val_c3_loss: 0.0121 - val_c4_loss: 0.0125 - val_c1_acc: 0.9956 - val_c2_acc: 0.9950 - val_c3_acc: 0.9958 - val_c4_acc: 0.9948\n", + "Epoch 10/100\n", + "100/100 [==============================] - 11s 111ms/step - loss: 0.0417 - c1_loss: 0.0086 - c2_loss: 0.0124 - c3_loss: 0.0107 - c4_loss: 0.0100 - c1_acc: 0.9962 - c2_acc: 0.9954 - c3_acc: 0.9964 - c4_acc: 0.9963\n", + "1000/1000 [==============================] - 125s 125ms/step - loss: 0.0452 - c1_loss: 0.0103 - c2_loss: 0.0124 - c3_loss: 0.0126 - c4_loss: 0.0100 - c1_acc: 0.9959 - c2_acc: 0.9950 - c3_acc: 0.9953 - c4_acc: 0.9961 - val_loss: 0.0417 - val_c1_loss: 0.0086 - val_c2_loss: 0.0124 - val_c3_loss: 0.0107 - val_c4_loss: 0.0100 - val_c1_acc: 0.9962 - val_c2_acc: 0.9954 - val_c3_acc: 0.9964 - val_c4_acc: 0.9963\n", + "Epoch 11/100\n", + "100/100 [==============================] - 11s 113ms/step - loss: 0.0642 - c1_loss: 0.0130 - c2_loss: 0.0190 - c3_loss: 0.0174 - c4_loss: 0.0149 - c1_acc: 0.9946 - c2_acc: 0.9926 - c3_acc: 0.9930 - c4_acc: 0.9943\n", + "1000/1000 [==============================] - 126s 126ms/step - loss: 0.0450 - c1_loss: 0.0107 - c2_loss: 0.0120 - c3_loss: 0.0124 - c4_loss: 0.0099 - c1_acc: 0.9958 - c2_acc: 0.9955 - c3_acc: 0.9951 - c4_acc: 0.9962 - val_loss: 0.0642 - val_c1_loss: 0.0130 - val_c2_loss: 0.0190 - val_c3_loss: 0.0174 - val_c4_loss: 0.0149 - val_c1_acc: 0.9946 - val_c2_acc: 0.9926 - val_c3_acc: 0.9930 - val_c4_acc: 0.9943\n", + "Epoch 12/100\n", + "100/100 [==============================] - 11s 111ms/step - loss: 0.0448 - c1_loss: 0.0103 - c2_loss: 0.0130 - c3_loss: 0.0110 - c4_loss: 0.0106 - c1_acc: 0.9963 - c2_acc: 0.9946 - c3_acc: 0.9956 - c4_acc: 0.9955\n", + "1000/1000 [==============================] - 117s 117ms/step - loss: 0.0430 - c1_loss: 0.0103 - c2_loss: 0.0120 - c3_loss: 0.0120 - c4_loss: 0.0087 - c1_acc: 0.9960 - c2_acc: 0.9952 - c3_acc: 0.9953 - c4_acc: 0.9965 - val_loss: 0.0448 - val_c1_loss: 0.0103 - val_c2_loss: 0.0130 - val_c3_loss: 0.0110 - val_c4_loss: 0.0106 - val_c1_acc: 0.9963 - val_c2_acc: 0.9946 - val_c3_acc: 0.9956 - val_c4_acc: 0.9955\n", + "Epoch 13/100\n", + "100/100 [==============================] - 11s 111ms/step - loss: 0.0443 - c1_loss: 0.0096 - c2_loss: 0.0130 - c3_loss: 0.0129 - c4_loss: 0.0089 - c1_acc: 0.9957 - c2_acc: 0.9953 - c3_acc: 0.9952 - c4_acc: 0.9966\n", + "1000/1000 [==============================] - 126s 126ms/step - loss: 0.0433 - c1_loss: 0.0100 - c2_loss: 0.0121 - c3_loss: 0.0116 - c4_loss: 0.0096 - c1_acc: 0.9960 - c2_acc: 0.9952 - c3_acc: 0.9957 - c4_acc: 0.9963 - val_loss: 0.0443 - val_c1_loss: 0.0096 - val_c2_loss: 0.0130 - val_c3_loss: 0.0129 - val_c4_loss: 0.0089 - val_c1_acc: 0.9957 - val_c2_acc: 0.9953 - val_c3_acc: 0.9952 - val_c4_acc: 0.9966\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.load_weights('cnn_best.h5')\n", + "\n", + "callbacks = [EarlyStopping(patience=3), CSVLogger('cnn.csv', append=True), \n", + " ModelCheckpoint('cnn_best.h5', save_best_only=True)]\n", + "\n", + "model.compile(loss='categorical_crossentropy',\n", + " optimizer=Adam(1e-4, amsgrad=True), \n", + " metrics=['accuracy'])\n", + "model.fit_generator(train_data, epochs=100, validation_data=valid_data, workers=4, use_multiprocessing=True,\n", + " callbacks=callbacks)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T07:50:24.503998Z", + "start_time": "2019-06-16T07:50:24.450513Z" + } + }, + "outputs": [], + "source": [ + "model.load_weights('cnn_best.h5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 测试模型" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T07:50:25.239664Z", + "start_time": "2019-06-16T07:50:24.505655Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.5, 127.5, 63.5, -0.5)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 227, + "width": 370 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "X, y = data[0]\n", + "y_pred = model.predict(X)\n", + "plt.title('real: %s\\npred:%s'%(decode(y), decode(y_pred)))\n", + "plt.imshow(X[0], cmap='gray')\n", + "plt.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 计算模型总体准确率" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T07:51:08.652629Z", + "start_time": "2019-06-16T07:50:25.241494Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:43<00:00, 2.29it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "0.98265625" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from tqdm import tqdm\n", + "def evaluate(model, batch_num=100):\n", + " batch_acc = 0\n", + " with tqdm(CaptchaSequence(characters, batch_size=128, steps=100)) as pbar:\n", + " for X, y in pbar:\n", + " y_pred = model.predict(X)\n", + " y_pred = np.argmax(y_pred, axis=-1).T\n", + " y_true = np.argmax(y, axis=-1).T\n", + "\n", + " batch_acc += (y_true == y_pred).all(axis=-1).mean()\n", + " return batch_acc / batch_num\n", + "\n", + "evaluate(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 保存模型" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T07:51:08.720802Z", + "start_time": "2019-06-16T07:51:08.654545Z" + } + }, + "outputs": [], + "source": [ + "model.save('cnn.h5', include_optimizer=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 可视化训练曲线\n", + "\n", + "```sh\n", + "pip install pandas\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T07:51:08.886141Z", + "start_time": "2019-06-16T07:51:08.722437Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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p4LGrV6/W6tWrtXfvXm3YsEE33XSTfvzjH+ttb3ubnnnmmRG12ZXGDPY4vCUiXcxgAwCAMmlpadH73vc+bd26VXfffbd+9rOfKZ1O581eS9ILL7ygGTNmjAjXknTfffeVZayHH364JHcZ9UJy+5cuXVrw+QMPPFAf/vCHdd999ykajY76pWDatGmDnVHWrFmjjo4OPfDAA1Mev98I2OOIUyICAAAqJFcmct111+m6665TKBQacRHh4sWL1dHRoSeffDJv/9VXX60777yzLONcvny5DjroIK1fv16/+tWv8p771a9+pfvvv1+vfe1rtWLFCknSiy++qD//+c8j3mfPnj1KJBJ5Fz/ecccdSqdHZrAdO3ZIklpbW/38KL6gRGQc+V1EKBEBAADls3z5ch144IH65S9/qVQqpVNPPXVEOcT555+vO++8UytWrND73vc+tbe369FHH9X69ev13ve+d0TgLQVjjK699lq95S1v0ZlnnqnTTjtNBx98sJ577jn95je/USwW03XXXadAwJ3bfeKJJ3T66adr2bJlOuywwzR//nzt3LlTN998s1KplL7whS8MvvdZZ52l5uZmrVixQosXL5a1Vg888IAeeeQRLVu2TCeddFLJP99EMYM9jvwuIsxgAwCA8jrnnHOUSqUGt4c7+eSTdcstt+jQQw/VjTfeqKuvvlqRSETr1q3TKaecUrZxHn300XrkkUf0j//4j3rooYf0zW9+Uxs2bNDZZ5+tRx55REcfffTgsUcccYS++MUvKhKJ6I477tB//Md/6Pbbb9eyZct022236bOf/ezgsZdddpmOPfZYbdq0SVdccYWuueYapVIpff3rX9e6desKtu+rNGOtrfQYxmSM2bh06dKlw5fMLJe7n35V517nXqG68uA5+vGaIysyDgAAGsEzzzwjyV0KHPBLsX+uli1bpk2bNm2y1i6byvmYwR4HXUQAAAAwEQTscVAiAgAAgIngIsdx0EUEAADUs8cff1y/+c1vijp27dq1pR1MnSBgj4Ol0gEAQD17/PHHdckllxR1LAG7OJSIjCMa8bbpS6vaLwoFAACYiDVr1shaW9QNxSFgjyMUDKglHJQkOVbqTWYqPCIAAABUMwJ2ESgTAQAAQLEI2EWgkwgAAEBtqkRpCwG7CLG8TiLMYAMAUCrGGEmS4zgVHgnqRS5g5/5slQMBuwjMYAMAUB6RSESS1NvbW+GRoF7k/izl/myVAwG7CPTCBgCgPGKxmCRp+/bt6u7uluM4dK/AhFlr5TiOuru7tX37dklDf7bKgT7YRfC26iNgAwBQOjNmzFBvb6/6+vr08ssvV3o4qBOtra2aMWNG2c5HwC4CXUQAACiPQCCgRYsWqaOjQ93d3UokEsxgY1KMMYpEIorFYpoxY4YCgfIVbhCwixCjRAQAgLIJBAKaNWuWZs2aVemhAJNCDXYRvDPYPQkCNgAAAEZHwC6CN2B3USICAACAMRCwi0CJCAAAAIpFwC4CFzkCAACgWATsIrDQDAAAAIpFwC4CJSIAAAAoFgG7CHQRAQAAQLEI2EUYXoNNw3sAAACMhoBdhEgoqKag+6NKZawSaafCIwIAAEC1ImAXiV7YAAAAKAYBu0h0EgEAAEAxCNhFopMIAAAAikHALlJeJxECNgAAAEZBwC4SqzkCAACgGATsIkUjlIgAAABgfATsItFFBAAAAMUgYBcpThcRAAAAFIGAXSRvFxGWSwcAAMBoCNhF4iJHAAAAFIOAXST6YAMAAKAYBOwiRanBBgAAQBEI2EWiRAQAAADFIGAXiS4iAAAAKAYBu0h5Ndh0EQEAAMAoCNhFokQEAAAAxSBgF6klHFQwYCRJAylHqYxT4REBAACgGhGwi2SMUTRCHTYAAADGRsCeAMpEAAAAMJ6SBWxjzAeMMTZ7O7dU5yknFpsBAADAeEoSsI0xiyR9T1JPKd6/UmK06gMAAMA4fA/Yxhgj6RpJuyX9wO/3r6Q4JSIAAAAYRylmsD8taaWkD0rqLcH7VwwXOQIAAGA8vgZsY8whki6T9B1r7f1+vnc1yK/BZgYbAAAAI4XGP6Q4xpiQpJ9K+pukCyfx+o2jPHXwVMblJ2qwAQAAMB7fArakL0s6XNIKa22/j+9bNVguHQAAAOPxJWAbY46SO2v9H9bahybzHtbaZaO890ZJS6cwPN8wgw0AAIDxTLkG21Ma8hdJX5ryiKoYC80AAABgPH5c5BiV9FpJh0ga8CwuYyVdnD3mquy+b/twvophBhsAAADj8aNEJCHp6lGeWyq3Lnu9pOckTap8pFrQRQQAAADjmXLAzl7QWHApdGPMWrkB+1pr7Y+meq5KYwYbAAAA4ynJUun1yjuD3UMXEQAAABRAwJ4AZrABAAAwnpIGbGvtWmutqYfyEElqaxoK2D2JtDKOreBoAAAAUI2YwZ6AYMAoGskP2QAAAIAXAXuC6IUNAACAsRCwJ4g6bAAAAIyFgD1BdBIBAADAWAjYE0SJCAAAAMZCwJ4g70WOlIgAAABgOAL2BHlLRLoI2AAAABiGgD1BcUpEAAAAMAYC9gTRRQQAAABjIWBPUF4XEQI2AAAAhiFgT1D+RY6UiAAAACAfAXuCKBEBAADAWAjYE+QtESFgAwAAYDgC9gR5Z7C7KBEBAADAMATsCYqzVDoAAADGQMCeIGqwAQAAMBYC9gRFPQG7J5GWtbaCowEAAEC1IWBPUDgYUHPY/bFlHKu+ZKbCIwIAAEA1IWBPAp1EAAAAMBoC9iTk12HTSQQAAABDCNiTkDeDTScRAAAAeBCwJyFOJxEAAACMgoA9CdEIJSIAAAAojIA9CfTCBgAAwGgI2JOQ30WEGWwAAAAMIWBPAjPYAAAAGA0BexLogw0AAIDRELAnIRZhBhsAAACFEbAngYVmAAAAMBoC9iRQIgIAAIDRELAnIW8GO8EMNgAAAIYQsCeBLiIAAAAYDQF7ErwlIj0EbAAAAHgQsCeBGWwAAACMhoA9CZFQQOGgkSQlM44GUpkKjwgAAADVgoA9CcYYOokAAACgIAL2JNELGwAAAIUQsCfJG7B7EsxgAwAAwEXAnqQoy6UDAACgAAL2JOXXYFMiAgAAABcBe5K8JSJdzGADAAAgi4A9SXG6iAAAAKAAAvYk0UUEAAAAhRCwJymviwgz2AAAAMgiYE9SNEKJCAAAAEYiYE9SXolIghIRAAAAuAjYk5Rfg80MNgAAAFwE7Eny9sGmTR8AAAByCNiTFKeLCAAAAAogYE+SdwabLiIAAADIIWBPUpQabAAAABRAwJ6ktqagAsbd7k9llMo4lR0QAAAAqgIBe5KMMYpGWGwGAAAA+QjYU+Ctw6ZMBAAAABIBe0pYbAYAAADDEbCngMVmAAAAMBwBewooEQEAAMBwBOwpiLHYDAAAAIYhYE8BJSIAAAAYjoA9BfklIsxgAwAAgIA9JfldRJjBBgAAgE8B2xjzdWPMPcaYLcaYfmNMhzHmMWPMxcaYmX6coxrFIpSIAAAAIJ9fM9ifkdQm6XeSviPpZ5LSktZKetIYs8in81QVuogAAABguND4hxQlbq0dGL7TGPN/JF0o6YuSPu7TuaoGXUQAAAAwnC8z2IXCddZ/Ze//zo/zVBtmsAEAADBcqS9yPDV7/2SJz1MRzGADAABgOL9KRCRJxpjPSYpKapd0hKQVcsP1ZX6ep1p4A3YPM9gAAACQzwFb0uck7eN5fIekNdbaneO90BizcZSnDvZjYKUQi1AiAgAAgHy+lohYa+daa42kuZLOkLS/pMeMMUv9PE+1iHpnsJNpOY6t4GgAAABQDfyewZYkWWtflXSTMWaTpL9Iuk7SYeO8Zlmh/dmZ7aoM6MGAUVtTUL3JjKx1Q3bcc+EjAAAAGk9JL3K01r4k6WlJrzPGzCrluSqFTiIAAADwKsdS6fOz95kynKvs6CQCAAAArykHbGPMwcaYuQX2B7ILzcyRtMFau2eq56pGUTqJAAAAwMOPGuyTJX3TGHO/pL9K2i23k8ib5F7kuF3SP/lwnqpEiQgAAAC8/AjYd0v6oaTlkpZImiapV+7FjT+V9F1rbYcP56lK3hKRLkpEAAAAGt6UA7a19ilJn/BhLDUpnleDzQw2AABAoyvHRY51jRIRAAAAeBGwpygW8VzkmKBEBAAAoNERsKcoSokIAAAAPAjYU0SJCAAAALwI2FPEQjMAAADwImBPUX6bPmawAQAAGh0Be4rilIgAAADAg4A9Rd4ZbLqIAAAAgIA9RdEIXUQAAAAwhIA9RcO7iFhrKzgaAAAAVBoBe4qaQgFFQu6PMeNY9acyFR4RAAAAKomA7QN6YQMAACCHgO2DOL2wAQAAkEXA9gHLpQMAACCHgO2DGAEbAAAAWQRsH8Qi1GADAADARcD2QYwabAAAAGQRsH1AFxEAAADkELB9kDeDnSBgAwAANDICtg8oEQEAAEAOAdsHdBEBAABADgHbB/k12MxgAwAANDICtg+YwQYAAEAOAdsHdBEB0DCSvZLjVHoUAFDVCNg+iEaGZrB76CICoF795U7pGwdIVxwtJfsqPRoAqFoEbB/E6SICoBE88iMp3S/t+ov0/J2VHg0AVC0Ctg+8JSJdlIgAqFd7txTeBgDkIWD7oDkcUChgJEnJtKNEOlPhEQGAz6yVOj2huuuVyo0FAKocAdsHxhg6iQCobwOdUrJn6HHny5UbCwBUOQK2T+gkAqCuDZ+xZgYbAEZFwPZJXicRAjaAejN8xrqTgA0AoyFg+yRGJxEA9axz2EWNvTukdKIyYwGAKkfA9gmdRADUtUI1111byz8OAKgBBGyf0AsbQF0rVBJCHTYAFETA9gldRADUtUIz2NRhA0BBBGyf0EUEQF0rWCJCqz4AKISA7ZOoZwa7J0GJCIA64mSk7gL11sxgA0BBBGyfUCICoG71vCo5Bf5eowYbAAoiYPuEEhEAdctbHhIY+ruOgA0AhRGwfeKdwe6iiwiAeuIN2POWePYTsAGgEAK2T+KUiACoV96APf/woVns/g4p2VeZMQFAFSNg+yQaGfq1aU+CgA2gjngD9rR9pfi8occsNgMAIxCwfcJS6QDqljdgty+U4guHHtOqDwBGIGD7hC4iAOqWN0S3L5LaFww9pg4bAEYgYPukrSkkY9ztvmRG6YxT2QEBgF/yZrAXSHFPwKaTCACMQMD2SSBgFI14F5thFhtAHUj2SX273e1ASIru45aJ5BRa4REAGhwB20dxemEDqDfeixjj86VAkBlsABgHAdtH3hlsAjaAutC5ZWg7d3EjNdgAMCYCto/oJAKg7gzvICIN6yJCwAaA4QjYPqKTCIC64w3QuYDdOkMKNbvbiS5poKv84wKAKkbA9lHMW4OdYAYbQB3wlojkSkOMoQ4bAMZAwPYRM9gA6k7nsB7Yg9vUYQPAaAjYPorRRQRAveksUCIisZojAIyBgO0jZrAB1BVrC1/kKLkt+3KYwQaAPARsH9FFBEBd6euQ0v3udlNMam4feq6dGmwAGA0B20fMYAOoK3kXOC7Mfy7Oao4AMBoCto9iEW8NNjPYAGpcoRZ9g4+ZwQaA0RCwfcQMNoC6kld/vSD/ufiwLiLWlmdMAFADCNg+inoCdk+CgA2gxo1VItLcLjVF3e10v9S/p3zjAoAqR8D2UZw2fQDqSV6LvkX5zw1fbIY6bAAYRMD2kbdEpIsabAC1zhua4wtGPk8dNgAURMD2UTSSXyLiONQkAqhho/XAzmEGGwAKmnLANsbMNMaca4y5yRjzgjGm3xjTaYxZb4z5sDGmYUJ8KBhQa1NQknu9T2+SMhEANSqTknq2Zx+Y/IVlcryhmxlsABjkR/j9B0lXSTpa0sOSvi3p15IOk/QjSf9ljDE+nKcm0EkEQF3o3iZZx92OzpFCkZHHDO8kAgCQJIXGP2Rcf5H0Lkm/tTb3t7FkjLlQ0h8lvUfSGXJDd92LRkJ6VQlJdBIBUMPGKw+RqMEGgFFMeQbbWvt7a+0t3nCd3b9d0g+yD0+Y6nlqRayZxWYA1IFiAjarOQJAQaWuj84lzIaZys3vJNIwHxtAvckL2IsKH5M3g71VcpzCxwFAg/GjRKQgY0xI0urswzuKOH7jKE8d7NugyoBe2ADqwngt+iQ4XelhAAAgAElEQVSpqU1qniYN7JWclNS7U4rtU57xAUAVK+UM9mVyL3S8zVp7ZwnPU1XyL3KkRARAjSqmRGT4c12UiQCAVKIZbGPMpyVdIOlZSR8o5jXW2mWjvNdGSUv9G11p0UUEQF3wXrQ4VsCOL5Befcrd7nxFWlDwr3IAaCi+z2AbYz4h6TuSnpb0Zmtth9/nqGbRyFCJSA8BG0Ct6twytD3mDDadRABgOF8DtjHmfEmXS3pKbrjePs5L6g4lIgBqXqJbGuh0t4MRqXXW6MeymiMAjOBbwDbGfEHStyQ9Ljdc7/DrvWsJJSIAap530Zj2BVJgjH8qWM0RAEbwJWAbY74k96LGjZJOtNbu8uN9a5G3DzZt+gDUpGI6iBR6ntUcAUCSDxc5GmPOkfQVSRlJD0j6dIGV0Tdba38y1XPVgjglIgBqXV799Sg9sAefpwYbAIbzo4vIa7L3QUnnj3LMfZJ+4sO5ql6UEhEAta7YDiJS/gx29zYpk5aCJVtiAQBqgh9Lpa+11ppxbif4MNaa4C0R6UkQsAHUoGJ7YEtSKCK1zXa3rSP1NNy17QAwQqmXSm84dBEBUPPyAvY4NdgSddgAMAwB22fDu4hYays4GgCYhInUYEus5ggAwxCwfRYJBdUUcn+sacdqIOVUeEQAMAGOI3VtHXo8XheR4ccwgw0ABOxSoJMIgJrVu1PKJN3t5mlSJDr+a+gkAgB5CNglEI14AjYXOgKoJXn110WUh0is5ggAwxCwS8DbSYRWfQBqStcEOogUOo4ZbAAgYJcCnUQA1KyJdhCRqMEGgGEI2CUwvJMIANSMifTAzonNk0z2n5PeHVI64f+4AKCGELBLIL9EhBlsADVkMjXYwZAUnTv02NuFBAAaEAG7BPIucmQGG0AtmcwMtkQnEQDwIGCXQJwSEQC1yhuwi+mBXehY6rABNDgCdgnQRQRATUon3Bpqya2pjs0r/rWs5ggAgwjYJUAXEQA1yVvaEZvv1lYXixlsABhEwC4BZrAB1KTJtOgrdDw12AAaHAG7BPJmsBPMYAOoEZO9wFGS4p7jmcEG0OAI2CUQ5SJHALXIG4wnGrDzZrCpwQbQ2AjYJeDtItJDwAZQKzq3DG3HJxiw2+ZIgWx5XP8eKdnn37gAoMYQsEvAW4PdRcAGUCumUiISCEhxT9cR6rABNDACdgnQRQRATeqaQomINKwOmzIRAI2LgF0CLeGgggEjSUqkHSXTToVHBADjsHZqM9gSnUQAIIuAXQLGGGaxAdSWgb1SssfdDrdKLdMn/h70wgYASQTskolGPBc6JqjDBlDlhncQMWbi75G3miMBG0DjImCXCIvNAKgpUy0PkfJnsAnYABoYAbtEvCUiXZSIAKh2eS36JriKY047JSIAIBGwSybOYjMAakneDPaiyb1HnBIRAJAI2CVDiQiAmjLVFn2S1DpDCjW724kuaaBr6uMCgBpEwC4R70WOdBEBUPXyZrAnWSJiDHXYACACdsnEWC4dQC3xo0REog4bAETALpm8EhHa9AGoZk5G6to69Dg+f/LvlVeHzWqOABoTAbtEWGgGQM3o3i7ZjLvdOksKt0z+vbzhnBlsAA2KgF0i+W36mMEGUMX86IE9+HpqsAGAgF0icbqIAKgVXT4GbG+JSCclIgAaEwG7RKKUiACoFX5d4Cgxgw0AImCXDF1EANQMP1r05cSHdRGxdmrvBwA1iIBdIiw0A6Bm+FmD3dwuNUXd7XS/1L9nau8HADWIgF0idBEBUDP8LBEZvtgMddgAGhABu0SiTSEZ4273JjPKOPyaFECV8obg+BRLRCTqsAE0PAJ2iQQCRtEm6rABVLlkn9Tf4W4HwlJ0n6m/JzPYABocAbuEonm9sCkTAVCFvDPM8flSwId/Frx13MxgA2hABOwSyuskwnLpAKpR55ah7ale4JgzvJMIADQYAnYJ0UkEQNXzs4PI4PtQgw2gsRGwS4hOIgCqnneG2bcZbFZzBNDYCNglxAw2gKpX8hnsrZLj+PO+AFAjCNglFI0wgw2gynlrsOM+BeymNql5mrvtpKTenf68LwDUCAJ2CcW9JSJc5AigGnWVoERk+Ht1USYCoLEQsEsovwabgA2gylhbmhIRiU4iABoaAbuE8muwKREBUGX6dkvpAXc7Epea4/69N51EADQwAnYJMYMNoKqVogd2Dqs5AmhgBOwSoosIgKpWihZ9hd6PGWwADYaAXUJ0EQFQ1bwzy94ZZz9Qgw2ggRGwS4gSEQBVrZQlItRgA2hgBOwSilMiAqCa5bXoW+Tve3tnsLu3SRn+DgTQOAjYJcRS6QCqWqla9ElSKCK1zXa3rSP1bPf3/QGgihGwSyjqCdg9ibSstRUcDQAMkxewfa7BlqjDBtCwCNglFA4G1BIOSpIcK/UmMxUeEQBkZVJSd25W2Uix+f6fg9UcATQoAnaJRSkTAVCNurZKyv5WLTZXCjX5fw5msAE0KAJ2iXnrsHu40BFAtShli74cOokAaFAE7BLzLjbTRcAGUC1KeYFjDqs5AmhQBOwSi1MiAqAadZUhYLOaI4AGRcAuMRabAVCVyj6DTcAG0DgI2CWWv1w6ARtAlShHwI7Nk0z2n5neHVI6UZrzAECVIWCXmLcGuydBiQiAKuGdUS5VwA6GpOjcocddW0tzHgCoMr4EbGPMe40x3zPGPGCM6TLGWGPM9X68d62jRARAVcrrIlKigC3RSQRAQwqNf0hR/k3SEkk9kl6WdLBP71vzvDPYBGwAVWGgS0p0utvBiNQ2q3Tnii+Q9Ii7TR02gAbhV4nIZyS9VlJc0sd8es+64J3B7qKLCIBq0DWsPMSY0p2L1RwBNCBfZrCttety26aUf1HXoDglIgCqTTkucMyhkwiABsRFjiUWjXhLRJjBBlAFOrcMbZc6YFODDaAB+VWDPWXGmI2jPFXT9dx5S6UnmMEGUAXK0UEkx3sBJTPYABoEM9glRhcRAFWnnCUieTPY1GADaAxVM4NtrV1WaH92ZntpmYfjG7qIAKg6eS36Fox+nB/a5kiBsOSkpP49UrJPamot7TkBoMKYwS6x/BnslKy1FRwNAGhYDfai0p4rEJDi84YeU4cNoAEQsEssEgooHHQ7q6QyVom0U+ERAWhojpO/omJ7iWewpWF12JSJAKh/BOwSM8bklYnQCxtARfXucMs1JKllutTUVvpz0kkEQIMhYJdBXicR6rABVFI5L3DMoRc2gAbjy0WOxph3S3p39uHc7P2xxpifZLd3WWs/58e5ahGdRABUjbyAXeL668HzsJojgMbiVxeRN0g6Z9i+/bM3SXpJUuMG7AidRABUCWawAaDkfCkRsdautdaaMW6L/ThPrRreSQQAKqacLfpyqMEG0GCowS6DKCUiAKpFVyVmsFnNEUBjIWCXQZwuIgCqRSVqsFtnSKFmdzvZLQ10lue8AFAhBOwyyOsikmAGG0AF5QXsMpWIGEMdNoCGQsAuA7qIAKgKqQGpd6e7bYJSdO7Yx/uJOmwADYSAXQbehWa4yBFAxXiDbXy+FPSrkVQRvHXYBGwAdY6AXQbMYAOoCpXoIDJ4vvmecRCwAdQ3AnYZRCMEbABVoBI9sAfPR4kIgMZBwC6DvBIRLnIEUCneYFvugJ3Xqo/VHAHUNwJ2GcRZaAZANejcMrTNDDYAlAwBuwzyL3JkBhtAhVSyRGR4mz5ry3t+ACgjAnYZsFQ6gKrQWcESkeZ2qSnqbqf7pf495T0/AJQRAbsMWpuCChh3eyDlKJVxKjsgAI3H2srOYI9YbIY6bAD1i4BdBsYYOokAqKz+PVKq190Ot0nN08o/BuqwATQIAnaZeOuwewjYAMpteAcRY8o/BmawATQIAnaZeOuwu6jDBlBulSwPKXReZrAB1DECdpnE6SQCoJLyAnaZV3HMGd5JBADqFAG7TOgkAqCi8npgL6rMGKjBBtAgCNhlEm3mIkcAFVTJFn05rOYIoEEQsMuEGWwAFeUNtPEKlYjkzWBvlRxalgKoTwTsMsnrIpJgBhtAmVXDRY5NnvaATkrq3VmZcQBAiRGwyyRGiQiASsmkpe5tQ48rNYMtDeskQpkIgPpEwB5L727f3so7g91FwAZQTj3bJZtxt9vmSOHmyo2FTiIAGgABezTbnpC+8/fSg99xlxieojg12AAqpRpa9BU6P51EANQpAnYhfR3Sje+Xkj3S774s/fIcKdE9pbdkqXQAFVMN9dc5rOYIoAEQsAvJpKTY/KHHT98sXXWitOv5Sb8lFzkCqJi8gF2hHtiD52c1RwD1j4BdSGwf6ZxbpKM+MrRv13PSD98sPXPL5N6SEhEAlVINLfoKnZ8abAB1ioA9mlCT9I5vSqdfKYWyFwQlu93SkbsvkZzMhN6OLiIAKqaaSkSowQbQAAjY41lylvTh30nT9hvat/7/Sde/x63VLpK3RISADaCsuqqoRMQ7g929zW0hCAB1hoBdjHl/L33kXunAk4b2/e866co3SVsfL+otvBc59iTSyjhT70wCAEWppi4ioYjUNtvdto7bQhAA6gwBu1itM6R//C/p+M8P7ev8m3T1W6XHfjbuy4MBo7am4OBjLnQEUBbJXql/j7sdCLt9sCuNOmwAdY6APRGBoLTyIunsX0iRuLsvk5Bu/rh062ekdGLMl9NJBEDZeQNs+wIpUAV/7bOaI4A6VwV/09agg97ulozMPmRo36M/ln5yitS1ddSX0UkEQNl1bhnarnT9dQ4z2ADqHAF7smYeIJ17t/S6M4b2vfyIdOXx0ub1BV9CJxEAZVdNLfpy6CQCoM4RsKciEpXe+2PpbV+TTLa+unendO27pIe+P2KJ9fxOIsxgAygDb4CtdIu+HFZzBFDnCNhTZYx07Cek1Td7rozPSHdeKP3qQ+4FRllRZrABlFs19cDOYTVHAHWOgO2X1xwnfeQ+acERQ/v+/N/Sj06Sdv9VkhT3BOwuAjaAcsirwa6SgE0NNoA6R8D2U/sC6YO3SUd8aGjfjqfdJdafuz2/iwgBG0A5dFZhiUhsnmSy//z07hi3AxMA1BoCtt9CEemd35JO+74UjLj7Ep3SDWfppG0/UkCOJGqwAZSBtdV5kWMwJEXnDj0eo/sSANQiAnapHP5+6cN35rXFOmrLj3R1+JtqVw812ABKr3eX26tfkiLtUnO8suPxopMIgDpGwC6l+Ye7ddn7nzC4683BJ3RL00Vq3/tUxYYFoEFUY/11DnXYAOoYAbvU2mZK7/9vacVnB3ftG9ipz27+mHTnRVKip4KDA1DXqrFFXw6rOQKoYwTscggEpZMu1rNvukI9ttndJUd66HLp+0dLz95W4QECqEvV2KIvhxlsAHWMgF1G6de+U6ckv6YHM68b2tn1svSLs6VfrOIfGQD+ygvYVXKBYw412ADqGAG7jGLNIb1k52pV6kKtDX5atnXm0JPP3ip9/yjpDz+QnEzlBgmgfuQF7EWjH1cJcc+MOpMLAOoMAbuMhvpgG/2k9xit6P2G/tB+ytAByR7pji9IV62Utj5WkTECqCPVXCKSN4NNDTaA+kLALqPprWHtP6tt8PEriRad9eoq/UPiy/qrPP/YbHvcDdm3/6uU6K7ASAHUhWrsgZ3TNkcKZCcd+vdIyb7KjgcAfETALiNjjG4871h98s0HavHM1sH9j9iDdfLAv+ubqfcpYbP/4FhHevg/Zb9/lPTMrRUaMYCalU5KPa9mHxgpPr+iwxkhEJDi84YeU4cNoI4QsMtsdiyiz73tIK373Am69VMr9LETDtCiGS1KKaTvZ96ttya/rgcyhw0eb7q2Sjeu0u4fvUeZPVvGeGcA8OjeKsm627F5UjA85uEVkVeHTZkIgPoRqvQAGpUxRoctaNdhC9r1+bcdpCdf7tRv/7RNv32yRR/Y+0W9K7NBXwr/VLNNlyRp5st3q+87R+je+f+kmSs/pSP3n6NAwFT4U2DSrJV2PC1F4tK0Krv4DPWhmjuI5NBJBECdImBXAWOMliyapiWLpumLbz9Yj23Zq1uf2F+rnjxKa/p/on8MrZMktWpA79j6PT113S36YPhjes2S43Tqknk6fNF0wnatSPVLf/ql9McfStv/JMlIB58irfiMtPCISo8O9aSziheZyaEXNoA6RcCuMsYYLd13upbuO13OKYdo49+O19UP3q43P/817S93RuqwwGb9OP2vuu6Pb9U5G/5B8fYZesfr5+mtr5ur18xq06xok4whcFeVvX+THvmRtOk694KuQdZt0fjsrdJ+y6Xl50t/9xaJ/36YqmpeJj2H1RwB1CkCdhULBIyOXDxDRy5epUzqvXrpt9/Q/Ce+q7BNKmisPhi6U28P/lEXd5+jH60/Uj9a/6IkqSkU0IJpLVowrUXzpzVrwbRW9366u29ee4uaQuOU31srpfrcpdzTA1ImJWWS2dt424lR9nv2OWlp9sHSASulOYfWZ6C0Vnrxfne2+rnb3AtXvYIR92eV89KD7m3OodLyf5YOe0911s2iNuR1EKnSgM0MNoA6RcCuEcFwRPu9+0vS8avk3HqBAv/7e0nSXLNHVzZ9W3dnDtcl6dXqsm2KpvsV3j2gvt392mYG1KUBvWz61KYBtWlAUdOvWU0pzWpKanowqfbggKIaUKvtU5PTp1CqV0r1ygwPhKUS3ccN2geslPY/QYrOKc95SyXRIz35C+mPV0k7nx35/LR9pSP/STr8/VL3NmnD99yyESftPr/jaemm86R7viod+wlp6WopEi3vZ0Dtq+Ye2DnUYAOoU8ZaW+kxjMkYs3Hp0qVLN27cWOmhVA9rpad+Ld3xRal3R6VH47+5rx8K3IuOkcLNlR5RcXb/1S0DeexnUqJz5PP7nyAddZ702rdJgWD+c3u3SH/4T2njT6RUb/5zzdOko/7JfW10dokGj7pzxbHulzVJ+sh90vw3VHY8hfTulr65v7vdFJMupEwEQGUtW7ZMmzZt2mStXTaV92EGuxYZI73+vdKBJ0p3XyJtvKYkpxmwYfWoRf02oqRCSimklILZe/emYJMCoSaFwhGFmiJqaoooHGlWc6RZLS0tamluUWtLi4LhJimYu4WlTFr620PS/64bVpMs9+K/7X+SHvyOFGqRFi8fCtyzD66uchLHkf56j/TwldILvxv5fFNUWnK2G5BnHzT6+0xbJJ38Nen4z0mPXi394QdS3y73uYG90v3fdGe637BKeuMnpRn7l+bzoH5U8zLpOa0zpFCzW4aW7JYGOqXm9kqPCgCmjBnsevC3h6W7LnKXVw+3uqEuEnPLCnLbTVH3cSSmTLhNXU6zOlJN2pkMa/tAWFv7Q/pbb0AvdQf01y5pdyqijILjn7tIM9uaNDsW0T7xZs2JRTQnHlGsOazWkDSv7zkt3PMHzd25Qe27HlPApkd/o9i8/HKStlm+jXFCBjqlx3/uloF0/HXk8zMOkI76iPSGsycXGFL97vtv+J6058X850xAOvQ0t057/uGTGz/q20CndNm+7naoWbpoe3V9MfX67tKh/w997CFpn0MrOx4ADY0ZbAzZ92jp3Lvd0pEi/hENSpqevR1Q4HlrrTp6k9q6d0Cv7O3Tq10Jvdo1oB3dCfeW3e7oTRY9xN29Se3uTerZ7aMt/X60pKPVpn4dE3haxwX+pOMCf9IBgW35h3Vvkx7/mXuT9NfQgXqqeZmeaztSr0Rfr3Bzi2LNIcWaw4o3hxRvDiveknscVqw5pHiLex8OTmKdpZ3PuRctPn7DyFIOGbcDyFHnuV8AAlNYxyncIh35YWnZGumZ/5HWf1va9rj7nHWkP9/k3l5zvNt55ICV1RugUH7DW/RV85+N9gVDAbvrFQI2gLpAwK4nPv0jaozRzGhEM6MRvX7h6LOvybSjXT354Xtn14Be7UpoR/fQvl09CRX7i5JetegeZ5nucdwvjgvNTq0I/EnHBZ7UisBTajd9eccfkH5BB/S8IPXcqL7tET3qvFYdiilhm5RUSF0Ka5fCStiwEgorKfc+obBsMKJguFnBpojCkRaFIq2KRJrV1Nyq5pZWtbS0qjV7W9j5qPZ74XpNf3XDiDE7TXENvP5sZZZ9WOHZB6opGPCvL3kgKL3udOnQd7sdSR78jluSkvPi/e5t7uvdoH3ou6VgFf7fOp1wZ1X797r3A51u6cvAXveLYWxe9jbXvciV7imjSw24P7f+vW55VW7bu2/380PHV+sFjjms5gigDlXhv8SoFU2hgOZPa9H8aS1jHpfOONrdm9QOz0z4zu6E+pJp9acy6ktm1J/KaCB735fMaCDlbvcnF+q3yXn6ZepE2VRGf2/+V8cFntRxwT9pqXleITPU6aTVJHR88E8T+xAZSf3Z2wQ95yzUdZm36qaBFep7sFl68AVJL0iSQgGjplDAvQWH3Xu2w8GAQgGjUNAoFAgoFDQKBozCgYCCQaNwwCgYCCgcNAoG9lFo7mWaG/+Ljnjlpzpw5+8UsBl3MNv/JP36w+q9/cv664EfVM+sJQoFQwqGggoFgwoFAwqFQtntoMKhkMKhof3hkDujHwwG3RKU3E3G/eJmAu7MeV447hwlNBfYlx6YwE/WSG2z3bCdC92Dt3lD922zR14sWu287S+TPVKy170lurMBec/IsJwXoPdM8Gep6m3RlxOfP7RNJxEAdYKAjZILBQPaJ96sfeLNer0mfwFTKuNkQ7d7+2vPHoW2PKjWLferfesDau15ycdRF5axRr9zjtC1mbfqIedQSYVnqtOOVTrpflnwn5G0WgvNyTo3eJvODN6rFuOW67T1vaK/f/LSEpyznKzbHad3h7T9ydEPM0F3tnt48M7dN7WNc54xfssw2m+DcgHZG46TPdnAnN0eHpy9xyV73c9XNkY67PQynm8S2umFDaD+ELBRM8JBd8Y33pwtH5jVJi0+U9KZ7uOOF92Z3PRA9pbI3rLbmcTgPpseUDo5oExyQOlkv5zUgGzK3a90QiaTUCCTVMBJKuQk1BuI6sHWE3V78zu0VbOVTDs6MOMomc7eMo5SaUeJ7L5yeNnO0dr0Gn0nfYbOCd2l1cG7NMP0lOXcE5WyQXWqTV22VV2e+07bJiNHc8xe7WP2aB+zV7PUqYApIoTajFuT371N0mMl/wzVwgZCss3TpJbpMs3TZFqmSS3uYzVnt3P3+xwmTd+v0kMeW5zVHAHUHwI26seM17i3IhhJ4eytGBFJp2Zv47HWKpWxSmbDdip7n/CE8VwwTzmOMhmrtOO4s94Zm7138u8dq4xjlco42XurjONk761edV6n/5s6T8s6btPfd92rJqffLekYvFkZWck62QWEbHafI5O9l6wC2ZuRk70f2mcldatVXdYNxl1qHbzvsm2DAbpzWIDuUqv6FdGYM8YeQWU0S53ZwO3e5pg92kd78x7PNKNdMFvd+m2TetWsPhtRr1oGt/cqqs7sz7HTtmmvooM/107bpr02qk61qU8RqW/oZxkwQ18+w0GjUNAtQXLLjV5UwGxWwEhGRsZIAWMUCLiPA8a95qLgvdzjAsbIGCOTPVfAGIWDAUXCATWHgu59OKjmUECRcFCR7H1zgfvmcDD/daGgmlvnarDIbO8WtwzGW6ZU8FbFF20C1cBa9zdofbvzb5Lny/j0oS/noUhlx1uHfAvYxpiFkr4i6WRJMyVtk/QbSZdYa/eM9Vqgnhhj1BRya7BV9r+zjpnSqzOOHZqRz+Rm5t0vC4l0Ju+Lgkll1JJ2FEg7aklnNC3lPpdIZ9z7lGc77SiRyhR8PpnJfckY+hLR48zWXmeWns5+6SikSSnNzobuoRnwbADXHkVMatTPacYo0xgvuvXbJvWpWT1qVp9tVq+a1Wub1afmbFjOPpfdP/x5R1PoLlOAYzX4M65FcfXqydxaUntelL6+uKjXZQa/ArpfBx0z9PXQGs/XQ2PkKCjHBOSYkBwFZU1AjglmXxeSNe7z7n1Q1nMr9FgmKCcQlExo2OOg+xsGk9vOPQ65XYVMaHCfTFAKhKRAMPu8+34mIMlKJvtbHPfOut8pcl+UZeV+5bGDf5aH7rPHZ39ORlbGOgooraCTVtCmFHSSCtq0Ak5KQZtSwEkq4KQUsCkFnLSCmaSMk8ruS8pkho4xTkom+7xxMrKhiGyoWTY4dK/Q8O2IbLA5u90iBd19ytsfkULN7r0JymQnAwJy3IkAx5ExjoyTGfyvnjvGyJGc7LHWkWxm8DllH8s67k8n9wVt+Jc2aeSXuBHHmML7g01SyLPWQygiBSOefZGpdZWSpExqKCT37hoZnAvtyxTf6Uuhlmzgnj7yN2K5x4WCeaR96p+tTvkSsI0xB0jaIGmOpJslPSvpKEn/LOlkY8xya+1uP84FoHSCAaOWpqBafOyBPlXWusE7F8DT2Rn/jGOVcuzgbwCGZvat+5uBbFjPHZ/K2GHbjlK53xJk3Nekh+1P5X67kLGD2+6Yhiqpc2sJ2MH/kZpl1SxpuvuLguzztsBrh94j5Vil0s7g+FKecQ1uZzxjzz5X5UsZjKtLrdpl45pluib0uqCGfaGwo2wDVSCtoJIKK62QkgopqbBSCg8u4pbMbicVVtKGFJCj6erWNHVruroUH9ZBy/8B9kvd/VL31gm9zJFRn2lTOhsnrcl9xfPK/9pnx9nv/Xo4EGjRfv/2xITGVC38msG+Qm64/rS19nu5ncaY/yfpM5L+j6SP+nQuAA3EmGyXlerJ/FXD++UjL3wPhnV3ttOxkmOtHMcN+jb3OHtvrfteg8dlvwV4H7u7rDKOe8FxIp3RQMrRQPY3E6PdJ1Lucbnjh98PpDL6aur9+mzoV4qbvuyM5VBpUv5jR8Fi6vOBKhNSRiEVedG7TxVQfTaiDsXUYWPaY2PqUExWRtPUo3bTq2nqUdz0app6FTaTuyA/IKuo9Vz74/P/PbucVn/fsIymHLCNMftLequkzZK+P+zpiyV9RNIHjDEXWGuHr8wBAJgk75eP5oQbJLoAAAq4SURBVHDtfgOx9mQlM5cqk/0txeBvJ+zQY8eR0o4jx7q/tchkMso4GTlORplMRk7uZh1lMhlZJ6NMxpGTSbslAk5a1nH3Gycl62SkTEbWScsMPu9ITlrGScvajIyTlpzM4Ovd4zIyNi1lMtlShLRbtmA9N8+xAbmPA9kyhYB1nwt4jg94Hmd/IsoWd2QfmWGze/mPrSeRjfaatAkppbBSCimtkFKex+7NO5saUtJmt21IKQWVsGElFVTShpRUUAkbUsYxCiuliFJqUlJNNqWwTSqSnZ/N7Ysomd1OqkkpNSmlyOB2/r5mJSQpWxjifr3K5O6tW/rjfey4P+HsfUCOdcuH3GOyN2vkZH8uAVkFTO6nZrNHuF/gVOCLXaEve4P7jLuvSensLaWw0moy6exncvePVa5WrIw12qOhoNxhY+qwcXVk9+22Me1RTLttfPCYgaJrFK3aNKB29WqaccN3u3oHQ/g005N93JM9xj0url7FzSR63DYIP2awV2bv77LW5v3OzlrbbYx5UG4AP0bSPcNfDABobMYYRfgVBXxkB3/rMvTbmYK/vXGGfkvjFPhNzsjf9rjbGWfoeZO9ODhtJMcYJTXsomFZ90uWk1TQujXsQScpk70FMym3c5WTUiCTlJyUjJFsywzZ1pnuLdKusAlqjtxygfwPW+DzF9g54ufh/VySHMf7eW3esd6fhbVSh7XaZSWbSckkOrP17vnnzOsGZYcVgmSPHbxeOfd42DUFgUBA8WL+g1chPwL2Qdn7v4zy/PNyA/ZrNUbANub/t3f/oXbWdQDH3x+y3BrOH8uSyLpq2sYqKSXdBuqUzIhKY4sCzUQhSzDDoMBMV/3RX2ZqmBBmmmCgZESWgptupRYtakjT1LYs8kduOM1Na/bpj+c5eu7xnLt7tu89z3l23y+4fLnP85xzPtwPH87nPud7vt9YP+DUwt0PTZIkzTYR0bXYjKvOzKyDmw5gLJX46mdn55BtA853jh9Q4LUkSZKksTaKdbBf+QBgqosy85i+D67ubL+/dFCSJEnSTChxB7tzh3rQHtjze66TJEmS9lolGuyH6/GoAeePrMdBc7QlSZKkvUaJBntNPZ4aEZOeLyL2A5YBO4AHCryWJEmSNNb2uMHOzMeAu4AJ4IKe06uAecCNroEtSZKk2aDUlxy/QLVV+lURcQqwETgOWE41NeSSQq8jSZIkjbUSU0Q6d7GPBW6gaqwvBo4ArgKWZOaWEq8jSZIkjbtiy/Rl5t+Bc0o9nyRJktRGRe5gS5IkSarYYEuSJEkF2WBLkiRJBdlgS5IkSQXZYEuSJEkF2WBLkiRJBdlgS5IkSQVFZjYdw5QiYsvcuXMPWrRoUdOhSJIkaS+2ceNGduzYsTUzF+zJ87Shwd4EzAc2N/DyC+vxoQZeW8MzX+1jztrHnLWPOWsX89WsCeC5zDxsT55k7BvsJkXEeoDMPKbpWLRr5qt9zFn7mLP2MWftYr72Ds7BliRJkgqywZYkSZIKssGWJEmSCrLBliRJkgqywZYkSZIKchURSZIkqSDvYEuSJEkF2WBLkiRJBdlgS5IkSQXZYEuSJEkF2WBLkiRJBdlgS5IkSQXZYEuSJEkF2WD3ERFvi4jrI+KfEfFSRGyOiCsj4sCmY9Nr1fnJAT9PNh3fbBQRKyLi6ohYFxHP1bn48S4eszQi7oiIrRGxPSI2RMRFEfG6UcU9mw2Ts4iYmKLmMiJuGXX8s01ELIiI8yLipxHxaETsiIhtEfHriDg3Ivq+v1tnzRk2Z9ZZu+3TdADjJiKOAO4D3gz8DHgI+ADwReC0iFiWmVsaDFH9bQOu7HP836MORAB8DTia6u//D2DhVBdHxMeB24AXgZ8AW4GPAt8BlgErZzJYAUPmrPYn4PY+xx8sGJf6WwlcCzwBrAEeB94CfAL4AfDhiFiZXbvJWWeNGzpnNeushdzJsUdE3AmcClyYmVd3Hb8C+BJwXWae31R8eq2I2AyQmRPNRqKOiFhO1aQ9CpxI9WZyc2ae2efa+fV1+wPLMvP39fE5wGpgCfDpzPRuzQwaMmcTwCbgR5n52dFFqY6IOBmYB/wiM//XdfwQ4HfAocCKzLytPm6dNWw3cjaBddZaThHpEhGHUzXXm4Hv9Zy+DHgBOCsi5o04NKlVMnNNZj7S505MPyuAg4FbOm/69XO8SHVXFeDzMxCmugyZMzUsM1dn5s+7G7X6+JPA9+tfT+o6ZZ01bDdyphZzishkJ9fjXX0K4PmI+A1VA348cPeog9OU9o2IM4G3U/0jtAFYm5kvNxuWpqFTd7/qc24tsB1YGhH7ZuZLowtL0/DWiPgcsADYAtyfmRsajknw33rc2XXMOhtv/XLWYZ21kA32ZO+qx78MOP8IVYN9FDbY4+YQ4KaeY5si4pzMvLeJgDRtA+suM3dGxCZgMXA4sHGUgWmXPlj/vCIi7gHOzszHG4lolouIfYDP1L92N9PW2ZiaImcd1lkLOUVksv3rcduA853jB4wgFk3fD4FTqJrsecB7gOuACeCXEXF0c6FpGqy79tkOfBM4Bjiw/unM2z4JuNupdI35NvBu4I7MvLPruHU2vgblzDprMRvs4UQ9OkdxjGTmqnpu21OZuT0zH6y/iHoFMBe4vNkItYesuzGTmU9n5tcz8w+Z+Wz9s5bqE77fAu8Ezms2ytknIi4ELqZa/eqsYR9ej9bZCE2VM+us3WywJ+v8B7//gPPze67TeOt8aeSERqPQrlh3e4nM3Em13BhYdyMVERcA3wX+DCzPzK09l1hnY2YaOevLOmsHG+zJHq7HowacP7IeB83R1nh5uh79CG28Day7em7iYVRf/PnrKIPSbvtXPVp3IxIRFwHXUK2LvLxelaKXdTZGppmzqVhnY84Ge7I19Xhqnx2V9qNaiH8H8MCoA9NuWVKPvmGMt9X1eFqfcycAbwTuc2WD1ji+Hq27EYiIr1BtFPNHqkbt6QGXWmdjYoicTcU6G3M22F0y8zHgLqovx13Qc3oV1X+KN2bmCyMOTQNExOKIOKjP8XdQ3R0AmHKLbjXuVuAZ4FMRcWznYL0BxrfqX69tIjD1FxHHRcQb+hw/mWpDLrDuZlxEXEr1Bbn1wCmZ+cwUl1tnY2CYnFln7eZOjj36bJW+ETgOWE41NWSpW6WPj4i4HPgq1acPm4DngSOAjwBzgDuAMzLzP03FOBtFxOnA6fWvhwAforrTsq4+9kxmfrnn+luptnC+hWoL549RLS12K/BJN0CZWcPkrF4ibDFwD9XujwDv5dW1li/NzE7TphkQEWcDNwAvA1fTf+705sy8oesx1lmDhs2ZddZuNth9RMShwDeoPkpbADwB3A6smu6XEDQaEXEicD7wPl5dpu9Zqo/ebgJu8g1j9Op/fC6b4pK/9W5tHxHLgEuopvbModrW+XrgKjcMmnnD5CwizgXOoFpa7E3A64GngPuBazJz3aAnURnTyBfAvZl5Us/jrLOGDJsz66zdbLAlSZKkgpyDLUmSJBVkgy1JkiQVZIMtSZIkFWSDLUmSJBVkgy1JkiQVZIMtSZIkFWSDLUmSJBVkgy1JkiQVZIMtSZIkFWSDLUmSJBVkgy1JkiQVZIMtSZIkFWSDLUmSJBVkgy1JkiQVZIMtSZIkFWSDLUmSJBVkgy1JkiQV9H9O1cZM//c9AAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 250, + "width": 364 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.read_csv('cnn.csv')\n", + "df[['loss', 'val_loss']].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T07:51:09.242749Z", + "start_time": "2019-06-16T07:51:08.887386Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "image/png": { + "height": 250, + "width": 381 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "df[['loss', 'val_loss']].plot(logy=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/model.h5 b/ctc_2017.h5 similarity index 100% rename from model.h5 rename to ctc_2017.h5 diff --git a/ctc.ipynb b/ctc_2017.ipynb similarity index 100% rename from ctc.ipynb rename to ctc_2017.ipynb diff --git a/ctc_2019.ipynb b/ctc_2019.ipynb new file mode 100644 index 0000000..9666b18 --- /dev/null +++ b/ctc_2019.ipynb @@ -0,0 +1,1106 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 导入必要的库\n", + "\n", + "我们需要导入一个叫 [captcha](https://github.com/lepture/captcha/) 的库来生成验证码。\n", + "\n", + "我们生成验证码的字符由数字和大写字母组成。\n", + "\n", + "```sh\n", + "pip install captcha numpy matplotlib tensorflow-gpu\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T06:51:38.735536Z", + "start_time": "2019-06-16T06:51:38.469537Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ\n" + ] + } + ], + "source": [ + "from captcha.image import ImageCaptcha\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import random\n", + "\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "import string\n", + "characters = string.digits + string.ascii_uppercase\n", + "print(characters)\n", + "\n", + "width, height, n_len, n_class = 128, 64, 4, len(characters) + 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 防止 tensorflow 占用所有显存" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T06:51:40.012307Z", + "start_time": "2019-06-16T06:51:38.736824Z" + } + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import tensorflow.keras.backend as K\n", + "\n", + "config = tf.ConfigProto()\n", + "config.gpu_options.allow_growth=True\n", + "sess = tf.Session(config=config)\n", + "K.set_session(sess)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 定义 CTC Loss" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T06:51:40.019037Z", + "start_time": "2019-06-16T06:51:40.014648Z" + } + }, + "outputs": [], + "source": [ + "import tensorflow.keras.backend as K\n", + "\n", + "def ctc_lambda_func(args):\n", + " y_pred, labels, input_length, label_length = args\n", + " return K.ctc_batch_cost(labels, y_pred, input_length, label_length)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 定义网络结构" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T06:51:41.015916Z", + "start_time": "2019-06-16T06:51:40.021386Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/ypw/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Colocations handled automatically by placer.\n" + ] + } + ], + "source": [ + "from tensorflow.keras.models import *\n", + "from tensorflow.keras.layers import *\n", + "\n", + "input_tensor = Input((height, width, 3))\n", + "x = input_tensor\n", + "for i, n_cnn in enumerate([2, 2, 2, 2, 2]):\n", + " for j in range(n_cnn):\n", + " x = Conv2D(32*2**min(i, 3), kernel_size=3, padding='same', kernel_initializer='he_uniform')(x)\n", + " x = BatchNormalization()(x)\n", + " x = Activation('relu')(x)\n", + " x = MaxPooling2D(2 if i < 3 else (2, 1))(x)\n", + "\n", + "x = Permute((2, 1, 3))(x)\n", + "x = TimeDistributed(Flatten())(x)\n", + "\n", + "rnn_size = 128\n", + "x = Bidirectional(CuDNNGRU(rnn_size, return_sequences=True))(x)\n", + "x = Bidirectional(CuDNNGRU(rnn_size, return_sequences=True))(x)\n", + "x = Dense(n_class, activation='softmax')(x)\n", + "\n", + "base_model = Model(inputs=input_tensor, outputs=x)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T06:51:41.133269Z", + "start_time": "2019-06-16T06:51:41.017246Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/ypw/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/backend.py:5077: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.cast instead.\n", + "WARNING:tensorflow:From /home/ypw/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/backend.py:5056: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.cast instead.\n" + ] + } + ], + "source": [ + "labels = Input(name='the_labels', shape=[n_len], dtype='float32')\n", + "input_length = Input(name='input_length', shape=[1], dtype='int64')\n", + "label_length = Input(name='label_length', shape=[1], dtype='int64')\n", + "loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([x, labels, input_length, label_length])\n", + "\n", + "model = Model(inputs=[input_tensor, labels, input_length, label_length], outputs=loss_out)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 网络结构可视化" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T06:51:41.687532Z", + "start_time": "2019-06-16T06:51:41.135134Z" + }, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from tensorflow.keras.utils import plot_model\n", + "from IPython.display import Image\n", + "\n", + "plot_model(model, to_file='ctc.png', show_shapes=True)\n", + "Image('ctc.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T06:51:41.696732Z", + "start_time": "2019-06-16T06:51:41.688981Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_1 (InputLayer) (None, 64, 128, 3) 0 \n", + "_________________________________________________________________\n", + "conv2d (Conv2D) (None, 64, 128, 32) 896 \n", + "_________________________________________________________________\n", + "batch_normalization_v1 (Batc (None, 64, 128, 32) 128 \n", + "_________________________________________________________________\n", + "activation (Activation) (None, 64, 128, 32) 0 \n", + "_________________________________________________________________\n", + "conv2d_1 (Conv2D) (None, 64, 128, 32) 9248 \n", + "_________________________________________________________________\n", + "batch_normalization_v1_1 (Ba (None, 64, 128, 32) 128 \n", + "_________________________________________________________________\n", + "activation_1 (Activation) (None, 64, 128, 32) 0 \n", + "_________________________________________________________________\n", + "max_pooling2d (MaxPooling2D) (None, 32, 64, 32) 0 \n", + "_________________________________________________________________\n", + "conv2d_2 (Conv2D) (None, 32, 64, 64) 18496 \n", + "_________________________________________________________________\n", + "batch_normalization_v1_2 (Ba (None, 32, 64, 64) 256 \n", + "_________________________________________________________________\n", + "activation_2 (Activation) (None, 32, 64, 64) 0 \n", + "_________________________________________________________________\n", + "conv2d_3 (Conv2D) (None, 32, 64, 64) 36928 \n", + "_________________________________________________________________\n", + "batch_normalization_v1_3 (Ba (None, 32, 64, 64) 256 \n", + "_________________________________________________________________\n", + "activation_3 (Activation) (None, 32, 64, 64) 0 \n", + "_________________________________________________________________\n", + "max_pooling2d_1 (MaxPooling2 (None, 16, 32, 64) 0 \n", + "_________________________________________________________________\n", + "conv2d_4 (Conv2D) (None, 16, 32, 128) 73856 \n", + "_________________________________________________________________\n", + "batch_normalization_v1_4 (Ba (None, 16, 32, 128) 512 \n", + "_________________________________________________________________\n", + "activation_4 (Activation) (None, 16, 32, 128) 0 \n", + "_________________________________________________________________\n", + "conv2d_5 (Conv2D) (None, 16, 32, 128) 147584 \n", + "_________________________________________________________________\n", + "batch_normalization_v1_5 (Ba (None, 16, 32, 128) 512 \n", + "_________________________________________________________________\n", + "activation_5 (Activation) (None, 16, 32, 128) 0 \n", + "_________________________________________________________________\n", + "max_pooling2d_2 (MaxPooling2 (None, 8, 16, 128) 0 \n", + "_________________________________________________________________\n", + "conv2d_6 (Conv2D) (None, 8, 16, 256) 295168 \n", + "_________________________________________________________________\n", + "batch_normalization_v1_6 (Ba (None, 8, 16, 256) 1024 \n", + "_________________________________________________________________\n", + "activation_6 (Activation) (None, 8, 16, 256) 0 \n", + "_________________________________________________________________\n", + "conv2d_7 (Conv2D) (None, 8, 16, 256) 590080 \n", + "_________________________________________________________________\n", + "batch_normalization_v1_7 (Ba (None, 8, 16, 256) 1024 \n", + "_________________________________________________________________\n", + "activation_7 (Activation) (None, 8, 16, 256) 0 \n", + "_________________________________________________________________\n", + "max_pooling2d_3 (MaxPooling2 (None, 4, 16, 256) 0 \n", + "_________________________________________________________________\n", + "conv2d_8 (Conv2D) (None, 4, 16, 256) 590080 \n", + "_________________________________________________________________\n", + "batch_normalization_v1_8 (Ba (None, 4, 16, 256) 1024 \n", + "_________________________________________________________________\n", + "activation_8 (Activation) (None, 4, 16, 256) 0 \n", + "_________________________________________________________________\n", + "conv2d_9 (Conv2D) (None, 4, 16, 256) 590080 \n", + "_________________________________________________________________\n", + "batch_normalization_v1_9 (Ba (None, 4, 16, 256) 1024 \n", + "_________________________________________________________________\n", + "activation_9 (Activation) (None, 4, 16, 256) 0 \n", + "_________________________________________________________________\n", + "max_pooling2d_4 (MaxPooling2 (None, 2, 16, 256) 0 \n", + "_________________________________________________________________\n", + "permute (Permute) (None, 16, 2, 256) 0 \n", + "_________________________________________________________________\n", + "time_distributed (TimeDistri (None, 16, 512) 0 \n", + "_________________________________________________________________\n", + "bidirectional (Bidirectional (None, 16, 256) 493056 \n", + "_________________________________________________________________\n", + "bidirectional_1 (Bidirection (None, 16, 256) 296448 \n", + "_________________________________________________________________\n", + "dense (Dense) (None, 16, 37) 9509 \n", + "=================================================================\n", + "Total params: 3,157,317\n", + "Trainable params: 3,154,373\n", + "Non-trainable params: 2,944\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "base_model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 定义数据生成器" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T06:51:41.706826Z", + "start_time": "2019-06-16T06:51:41.698386Z" + } + }, + "outputs": [], + "source": [ + "from tensorflow.keras.utils import Sequence\n", + "\n", + "class CaptchaSequence(Sequence):\n", + " def __init__(self, characters, batch_size, steps, n_len=4, width=128, height=64, \n", + " input_length=16, label_length=4):\n", + " self.characters = characters\n", + " self.batch_size = batch_size\n", + " self.steps = steps\n", + " self.n_len = n_len\n", + " self.width = width\n", + " self.height = height\n", + " self.input_length = input_length\n", + " self.label_length = label_length\n", + " self.n_class = len(characters)\n", + " self.generator = ImageCaptcha(width=width, height=height)\n", + " \n", + " def __len__(self):\n", + " return self.steps\n", + "\n", + " def __getitem__(self, idx):\n", + " X = np.zeros((self.batch_size, self.height, self.width, 3), dtype=np.float32)\n", + " y = np.zeros((self.batch_size, self.n_len), dtype=np.uint8)\n", + " input_length = np.ones(self.batch_size)*self.input_length\n", + " label_length = np.ones(self.batch_size)*self.label_length\n", + " for i in range(self.batch_size):\n", + " random_str = ''.join([random.choice(self.characters) for j in range(self.n_len)])\n", + " X[i] = np.array(self.generator.generate_image(random_str)) / 255.0\n", + " y[i] = [self.characters.find(x) for x in random_str]\n", + " return [X, y, input_length, label_length], np.ones(self.batch_size)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# 测试生成器" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T06:51:41.909160Z", + "start_time": "2019-06-16T06:51:41.708359Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tensor(\"input_length:0\", shape=(?, 1), dtype=int64) Tensor(\"label_length:0\", shape=(?, 1), dtype=int64)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 213, + "width": 370 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "data = CaptchaSequence(characters, batch_size=1, steps=1)\n", + "[X_test, y_test, _, _], _ = data[0]\n", + "plt.imshow(X_test[0])\n", + "plt.title(''.join([characters[x] for x in y_test[0]]))\n", + "print(input_length, label_length)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 准确率回调函数" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T06:51:41.920047Z", + "start_time": "2019-06-16T06:51:41.911070Z" + } + }, + "outputs": [], + "source": [ + "from tqdm import tqdm\n", + "\n", + "def evaluate(model, batch_size=128, steps=20):\n", + " batch_acc = 0\n", + " valid_data = CaptchaSequence(characters, batch_size, steps)\n", + " for [X_test, y_test, _, _], _ in valid_data:\n", + " y_pred = base_model.predict(X_test)\n", + " shape = y_pred.shape\n", + " out = K.get_value(K.ctc_decode(y_pred, input_length=np.ones(shape[0])*shape[1])[0][0])[:, :4]\n", + " if out.shape[1] == 4:\n", + " batch_acc += (y_test == out).all(axis=1).mean()\n", + " return batch_acc / steps" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T06:51:41.927035Z", + "start_time": "2019-06-16T06:51:41.921727Z" + } + }, + "outputs": [], + "source": [ + "from tensorflow.keras.callbacks import Callback\n", + "\n", + "class Evaluate(Callback):\n", + " def __init__(self):\n", + " self.accs = []\n", + " \n", + " def on_epoch_end(self, epoch, logs=None):\n", + " logs = logs or {}\n", + " acc = evaluate(base_model)\n", + " logs['val_acc'] = acc\n", + " self.accs.append(acc)\n", + " print(f'\\nacc: {acc*100:.4f}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 训练模型" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T08:35:47.053701Z", + "start_time": "2019-06-16T06:51:41.929060Z" + }, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/ypw/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:102: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Deprecated in favor of operator or tf.math.divide.\n", + "Epoch 1/100\n", + "100/100 [==============================] - 29s 286ms/step - loss: 0.6825\n", + "WARNING:tensorflow:From /home/ypw/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/backend.py:5133: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n", + "\n", + "acc: 83.0078\n", + "1000/1000 [==============================] - 384s 384ms/step - loss: 1.5412 - val_loss: 0.6825\n", + "Epoch 2/100\n", + "100/100 [==============================] - 28s 276ms/step - loss: 0.2019\n", + "\n", + "acc: 92.5781\n", + "1000/1000 [==============================] - 378s 378ms/step - loss: 0.2000 - val_loss: 0.2019\n", + "Epoch 3/100\n", + "100/100 [==============================] - 28s 277ms/step - loss: 0.2165\n", + "\n", + "acc: 92.5000\n", + "1000/1000 [==============================] - 378s 378ms/step - loss: 0.1576 - val_loss: 0.2165\n", + "Epoch 4/100\n", + "100/100 [==============================] - 28s 280ms/step - loss: 1.4023\n", + "\n", + "acc: 68.4766\n", + "1000/1000 [==============================] - 377s 377ms/step - loss: 0.1290 - val_loss: 1.4023\n", + "Epoch 5/100\n", + "100/100 [==============================] - 28s 280ms/step - loss: 0.1416\n", + "\n", + "acc: 93.0469\n", + "1000/1000 [==============================] - 378s 378ms/step - loss: 0.1155 - val_loss: 0.1416\n", + "Epoch 6/100\n", + "100/100 [==============================] - 28s 277ms/step - loss: 0.1185\n", + "\n", + "acc: 95.4688\n", + "1000/1000 [==============================] - 351s 351ms/step - loss: 0.1083 - val_loss: 0.1185\n", + "Epoch 7/100\n", + "100/100 [==============================] - 27s 274ms/step - loss: 0.1034\n", + "\n", + "acc: 96.0938\n", + "1000/1000 [==============================] - 377s 377ms/step - loss: 0.0996 - val_loss: 0.1034\n", + "Epoch 8/100\n", + "100/100 [==============================] - 29s 287ms/step - loss: 1.2957\n", + "\n", + "acc: 73.9062\n", + "1000/1000 [==============================] - 379s 379ms/step - loss: 0.0958 - val_loss: 1.2957\n", + "Epoch 9/100\n", + "100/100 [==============================] - 28s 285ms/step - loss: 0.1613\n", + "\n", + "acc: 94.4531\n", + "1000/1000 [==============================] - 379s 379ms/step - loss: 0.0850 - val_loss: 0.1613\n", + "Epoch 10/100\n", + "100/100 [==============================] - 10s 101ms/step - loss: 0.0853\n", + "\n", + "acc: 96.1719\n", + "1000/1000 [==============================] - 235s 235ms/step - loss: 0.0837 - val_loss: 0.0853\n", + "Epoch 11/100\n", + "100/100 [==============================] - 10s 100ms/step - loss: 0.1014\n", + "\n", + "acc: 96.5234\n", + "1000/1000 [==============================] - 153s 153ms/step - loss: 0.0776 - val_loss: 0.1014\n", + "Epoch 12/100\n", + "100/100 [==============================] - 10s 98ms/step - loss: 0.0784\n", + "\n", + "acc: 97.3828\n", + "1000/1000 [==============================] - 153s 153ms/step - loss: 0.0727 - val_loss: 0.0784\n", + "Epoch 13/100\n", + "100/100 [==============================] - 10s 101ms/step - loss: 2.4791\n", + "\n", + "acc: 69.7266\n", + "1000/1000 [==============================] - 153s 153ms/step - loss: 0.0672 - val_loss: 2.4791\n", + "Epoch 14/100\n", + "100/100 [==============================] - 10s 99ms/step - loss: 0.0841\n", + "\n", + "acc: 96.9141\n", + "1000/1000 [==============================] - 154s 154ms/step - loss: 0.0671 - val_loss: 0.0841\n", + "Epoch 15/100\n", + "100/100 [==============================] - 10s 98ms/step - loss: 0.5806\n", + "\n", + "acc: 84.6484\n", + "1000/1000 [==============================] - 154s 154ms/step - loss: 0.0612 - val_loss: 0.5806\n", + "Epoch 16/100\n", + "100/100 [==============================] - 10s 100ms/step - loss: 0.0722\n", + "\n", + "acc: 97.4219\n", + "1000/1000 [==============================] - 154s 154ms/step - loss: 0.0609 - val_loss: 0.0722\n", + "Epoch 17/100\n", + "100/100 [==============================] - 10s 100ms/step - loss: 0.2373\n", + "\n", + "acc: 91.4453\n", + "1000/1000 [==============================] - 154s 154ms/step - loss: 0.0579 - val_loss: 0.2373\n", + "Epoch 18/100\n", + "100/100 [==============================] - 10s 100ms/step - loss: 0.0994\n", + "\n", + "acc: 96.6406\n", + "1000/1000 [==============================] - 154s 154ms/step - loss: 0.0554 - val_loss: 0.0994\n", + "Epoch 19/100\n", + "100/100 [==============================] - 10s 99ms/step - loss: 0.0810\n", + "\n", + "acc: 97.6172\n", + "1000/1000 [==============================] - 154s 154ms/step - loss: 0.0535 - val_loss: 0.0810\n", + "Epoch 20/100\n", + "100/100 [==============================] - 10s 101ms/step - loss: 0.0763\n", + "\n", + "acc: 97.3047\n", + "1000/1000 [==============================] - 155s 155ms/step - loss: 0.0484 - val_loss: 0.0763\n", + "Epoch 21/100\n", + "100/100 [==============================] - 10s 100ms/step - loss: 0.0662\n", + "\n", + "acc: 97.1875\n", + "1000/1000 [==============================] - 155s 155ms/step - loss: 0.0492 - val_loss: 0.0662\n", + "Epoch 22/100\n", + "100/100 [==============================] - 10s 100ms/step - loss: 0.0513\n", + "\n", + "acc: 98.1641\n", + "1000/1000 [==============================] - 155s 155ms/step - loss: 0.0436 - val_loss: 0.0513\n", + "Epoch 23/100\n", + "100/100 [==============================] - 10s 98ms/step - loss: 0.0556\n", + "\n", + "acc: 98.7891\n", + "1000/1000 [==============================] - 155s 155ms/step - loss: 0.0429 - val_loss: 0.0556\n", + "Epoch 24/100\n", + "100/100 [==============================] - 10s 101ms/step - loss: 0.0932\n", + "\n", + "acc: 96.5234\n", + "1000/1000 [==============================] - 156s 156ms/step - loss: 0.0397 - val_loss: 0.0932\n", + "Epoch 25/100\n", + "100/100 [==============================] - 10s 100ms/step - loss: 0.0922\n", + "\n", + "acc: 96.5625\n", + "1000/1000 [==============================] - 156s 156ms/step - loss: 0.0386 - val_loss: 0.0922\n", + "Epoch 26/100\n", + "100/100 [==============================] - 10s 98ms/step - loss: 0.4456\n", + "\n", + "acc: 89.2578\n", + "1000/1000 [==============================] - 156s 156ms/step - loss: 0.0380 - val_loss: 0.4456\n", + "Epoch 27/100\n", + "100/100 [==============================] - 10s 99ms/step - loss: 0.0754\n", + "\n", + "acc: 97.1094\n", + "1000/1000 [==============================] - 156s 156ms/step - loss: 0.0394 - val_loss: 0.0754\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from tensorflow.keras.callbacks import EarlyStopping, CSVLogger, ModelCheckpoint\n", + "from tensorflow.keras.optimizers import *\n", + "\n", + "train_data = CaptchaSequence(characters, batch_size=128, steps=1000)\n", + "valid_data = CaptchaSequence(characters, batch_size=128, steps=100)\n", + "callbacks = [EarlyStopping(patience=5), Evaluate(), \n", + " CSVLogger('ctc.csv'), ModelCheckpoint('ctc_best.h5', save_best_only=True)]\n", + "\n", + "model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=Adam(1e-3, amsgrad=True))\n", + "model.fit_generator(train_data, epochs=100, validation_data=valid_data, workers=4, use_multiprocessing=True,\n", + " callbacks=callbacks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 载入最好的模型继续训练一会" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T09:16:19.684510Z", + "start_time": "2019-06-16T08:35:47.057236Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "100/100 [==============================] - 10s 99ms/step - loss: 0.0346\n", + "\n", + "acc: 98.2812\n", + "1000/1000 [==============================] - 164s 164ms/step - loss: 0.0348 - val_loss: 0.0346\n", + "Epoch 2/100\n", + "100/100 [==============================] - 10s 99ms/step - loss: 0.0330\n", + "\n", + "acc: 98.7891\n", + "1000/1000 [==============================] - 160s 160ms/step - loss: 0.0316 - val_loss: 0.0330\n", + "Epoch 3/100\n", + "100/100 [==============================] - 10s 99ms/step - loss: 0.0284\n", + "\n", + "acc: 98.7891\n", + "1000/1000 [==============================] - 161s 161ms/step - loss: 0.0309 - val_loss: 0.0284\n", + "Epoch 4/100\n", + "100/100 [==============================] - 10s 100ms/step - loss: 0.0260\n", + "\n", + "acc: 98.6328\n", + "1000/1000 [==============================] - 161s 161ms/step - loss: 0.0288 - val_loss: 0.0260\n", + "Epoch 5/100\n", + "100/100 [==============================] - 10s 99ms/step - loss: 0.0346\n", + "\n", + "acc: 98.7500\n", + "1000/1000 [==============================] - 161s 161ms/step - loss: 0.0287 - val_loss: 0.0346\n", + "Epoch 6/100\n", + "100/100 [==============================] - 10s 98ms/step - loss: 0.0270\n", + "\n", + "acc: 99.4531\n", + "1000/1000 [==============================] - 161s 161ms/step - loss: 0.0280 - val_loss: 0.0270\n", + "Epoch 7/100\n", + "100/100 [==============================] - 10s 99ms/step - loss: 0.0277\n", + "\n", + "acc: 98.9062\n", + "1000/1000 [==============================] - 162s 162ms/step - loss: 0.0271 - val_loss: 0.0277\n", + "Epoch 8/100\n", + "100/100 [==============================] - 10s 101ms/step - loss: 0.0415\n", + "\n", + "acc: 98.6719\n", + "1000/1000 [==============================] - 162s 162ms/step - loss: 0.0254 - val_loss: 0.0415\n", + "Epoch 9/100\n", + "100/100 [==============================] - 10s 101ms/step - loss: 0.0226\n", + "\n", + "acc: 99.2188\n", + "1000/1000 [==============================] - 162s 162ms/step - loss: 0.0251 - val_loss: 0.0226\n", + "Epoch 10/100\n", + "100/100 [==============================] - 10s 100ms/step - loss: 0.0204\n", + "\n", + "acc: 98.9062\n", + "1000/1000 [==============================] - 162s 162ms/step - loss: 0.0255 - val_loss: 0.0204\n", + "Epoch 11/100\n", + "100/100 [==============================] - 10s 99ms/step - loss: 0.0236\n", + "\n", + "acc: 99.2969\n", + "1000/1000 [==============================] - 162s 162ms/step - loss: 0.0254 - val_loss: 0.0236\n", + "Epoch 12/100\n", + "100/100 [==============================] - 10s 98ms/step - loss: 0.0290\n", + "\n", + "acc: 98.7891\n", + "1000/1000 [==============================] - 162s 162ms/step - loss: 0.0235 - val_loss: 0.0290\n", + "Epoch 13/100\n", + "100/100 [==============================] - 10s 99ms/step - loss: 0.0282\n", + "\n", + "acc: 99.0625\n", + "1000/1000 [==============================] - 163s 163ms/step - loss: 0.0233 - val_loss: 0.0282\n", + "Epoch 14/100\n", + "100/100 [==============================] - 10s 100ms/step - loss: 0.0355\n", + "\n", + "acc: 98.7891\n", + "1000/1000 [==============================] - 163s 163ms/step - loss: 0.0227 - val_loss: 0.0355\n", + "Epoch 15/100\n", + "100/100 [==============================] - 10s 100ms/step - loss: 0.0255\n", + "\n", + "acc: 99.4922\n", + "1000/1000 [==============================] - 163s 163ms/step - loss: 0.0222 - val_loss: 0.0255\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.load_weights('ctc_best.h5')\n", + "\n", + "callbacks = [EarlyStopping(patience=5), Evaluate(), \n", + " CSVLogger('ctc.csv', append=True), ModelCheckpoint('ctc_best.h5', save_best_only=True)]\n", + "\n", + "model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=Adam(1e-4, amsgrad=True))\n", + "model.fit_generator(train_data, epochs=100, validation_data=valid_data, workers=4, use_multiprocessing=True,\n", + " callbacks=callbacks)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T09:16:19.729405Z", + "start_time": "2019-06-16T09:16:19.686054Z" + } + }, + "outputs": [], + "source": [ + "model.load_weights('ctc_best.h5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 测试模型" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T09:16:20.917277Z", + "start_time": "2019-06-16T09:16:19.731259Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(36, ' '),\n", + " (36, ' '),\n", + " (36, ' '),\n", + " (36, ' '),\n", + " (36, ' '),\n", + " (8, '8'),\n", + " (36, ' '),\n", + " (10, 'A'),\n", + " (36, ' '),\n", + " (36, ' '),\n", + " (35, 'Z'),\n", + " (36, ' '),\n", + " (36, ' '),\n", + " (36, ' '),\n", + " (36, ' '),\n", + " (6, '6')]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 227, + "width": 370 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "characters2 = characters + ' '\n", + "[X_test, y_test, _, _], _ = data[0]\n", + "y_pred = base_model.predict(X_test)\n", + "out = K.get_value(K.ctc_decode(y_pred, input_length=np.ones(y_pred.shape[0])*y_pred.shape[1], )[0][0])[:, :4]\n", + "out = ''.join([characters[x] for x in out[0]])\n", + "y_true = ''.join([characters[x] for x in y_test[0]])\n", + "\n", + "plt.imshow(X_test[0])\n", + "plt.title('pred:' + str(out) + '\\ntrue: ' + str(y_true))\n", + "\n", + "argmax = np.argmax(y_pred, axis=2)[0]\n", + "list(zip(argmax, ''.join([characters2[x] for x in argmax])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 计算模型总体准确率" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T09:16:44.017485Z", + "start_time": "2019-06-16T09:16:20.918496Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9921875" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluate(base_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 保存模型" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T09:16:44.073896Z", + "start_time": "2019-06-16T09:16:44.018652Z" + } + }, + "outputs": [], + "source": [ + "base_model.save('ctc.h5', include_optimizer=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# 可视化训练曲线\n", + "\n", + "```sh\n", + "pip install pandas\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T09:17:29.985054Z", + "start_time": "2019-06-16T09:17:29.852065Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 250, + "width": 373 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.read_csv('ctc.csv')\n", + "df[['loss', 'val_loss']].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T09:17:31.278107Z", + "start_time": "2019-06-16T09:17:31.050105Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "image/png": { + "height": 250, + "width": 381 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "df[['loss', 'val_loss']].plot(logy=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T09:17:33.107543Z", + "start_time": "2019-06-16T09:17:32.987147Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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