ctc_2019.ipynb

{
 "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 pydot tqdm\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": [
    {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "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": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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": [
       "<tensorflow.python.keras.callbacks.History at 0x7f64d81e3198>"
      ]
     },
     "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": [
       "<tensorflow.python.keras.callbacks.History at 0x7f64878ede80>"
      ]
     },
     "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": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f647434f1d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f647432cc88>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f647421d8d0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 380
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['val_acc'].plot()"
   ]
  },
  {
   "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
}