diff --git "a/\346\223\215\344\275\234\346\227\245\345\277\227/get_the_flag.data" "b/\346\223\215\344\275\234\346\227\245\345\277\227/get_the_flag.data"
new file mode 100644
index 0000000..631e74f
--- /dev/null
+++ "b/\346\223\215\344\275\234\346\227\245\345\277\227/get_the_flag.data"
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diff --git "a/\346\223\215\344\275\234\346\227\245\345\277\227/readme.md" "b/\346\223\215\344\275\234\346\227\245\345\277\227/readme.md"
index 05704dc..20b5d5c 100644
--- "a/\346\223\215\344\275\234\346\227\245\345\277\227/readme.md"
+++ "b/\346\223\215\344\275\234\346\227\245\345\277\227/readme.md"
@@ -1,5 +1 @@
-每行数据共58行，分别是：
-| |单词出现频率|字符出现频率|平均词长|最长词长|总词长|分类|
- |----|----|----|----|----|----|----|
- |个数|48|6|1|1|1|1|
- |类型|实数|实数|实数|整数|整数|布尔|
\ No newline at end of file
+Answer:10001001110101110100111011011100100101111110000011001001
diff --git "a/\346\223\215\344\275\234\346\227\245\345\277\227/tensorflow.ipynb" "b/\346\223\215\344\275\234\346\227\245\345\277\227/tensorflow.ipynb"
new file mode 100644
index 0000000..7003495
--- /dev/null
+++ "b/\346\223\215\344\275\234\346\227\245\345\277\227/tensorflow.ipynb"
@@ -0,0 +1,434 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.     0.64   0.64   ... 0.3756 0.61   0.278 ]\n",
+      " [0.06   0.     0.71   ... 0.9821 4.85   2.259 ]\n",
+      " [0.     0.     0.     ... 0.3537 0.4    0.191 ]\n",
+      " ...\n",
+      " [0.3    0.     0.3    ... 0.1404 0.06   0.118 ]\n",
+      " [0.96   0.     0.     ... 0.1147 0.05   0.078 ]\n",
+      " [0.     0.     0.65   ... 0.125  0.05   0.04  ]] ['1' '1' '1' ... '0' '0' '0']\n",
+      "[[0.21   0.28   0.5    ... 0.5114 1.01   1.028 ]\n",
+      " [0.     0.     0.     ... 0.3537 0.4    0.191 ]\n",
+      " [0.     0.69   0.34   ... 0.3728 0.61   0.261 ]\n",
+      " ...\n",
+      " [0.     0.     0.     ... 0.1    0.01   0.006 ]\n",
+      " [0.     0.     0.     ... 0.1727 0.05   0.019 ]\n",
+      " [0.     0.     1.19   ... 0.1    0.01   0.024 ]] ['1' '1' '1' ... '0' '0' '0']\n"
+     ]
+    }
+   ],
+   "source": [
+    "def load_data(file_path):\n",
+    "    data=[]\n",
+    "    label=[]\n",
+    "    with open(file_path,\"r\") as data_file:\n",
+    "        raw_data=data_file.readlines()\n",
+    "        for raw_line in raw_data:\n",
+    "            sample=raw_line[:-1]\n",
+    "            sample=sample.split(\",\")\n",
+    "            label.append(sample[-1])\n",
+    "            sample=np.array([float(feature) for feature in sample[:-1]])\n",
+    "            sample[-3]/=10\n",
+    "            sample[-2]/=100\n",
+    "            sample[-1]/=1000\n",
+    "            data.append(sample)\n",
+    "    return np.array(data),np.array(label)\n",
+    "train_data,train_label=load_data(\"train.data\")\n",
+    "test_data,test_label=load_data(\"test.data\")\n",
+    "print(train_data,train_label)\n",
+    "print(test_data,test_label)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "57"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(test_data[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Dropout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model=Sequential()\n",
+    "model.add(Dense(20, activation='sigmoid', input_shape=(57,)))\n",
+    "model.add(Dropout(0.2))\n",
+    "model.add(Dense(28, activation='softmax'))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "model.add(Dense(1, activation='sigmoid'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer='adam',\n",
+    "              loss='binary_crossentropy',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 3224 samples, validate on 1377 samples\n",
+      "Epoch 1/60\n",
+      "3224/3224 [==============================] - 1s 198us/step - loss: 0.7001 - acc: 0.4110 - val_loss: 0.6920 - val_acc: 0.5185\n",
+      "Epoch 2/60\n",
+      "3224/3224 [==============================] - 0s 22us/step - loss: 0.6859 - acc: 0.6014 - val_loss: 0.6791 - val_acc: 0.6057\n",
+      "Epoch 3/60\n",
+      "3224/3224 [==============================] - 0s 21us/step - loss: 0.6729 - acc: 0.6067 - val_loss: 0.6633 - val_acc: 0.6057\n",
+      "Epoch 4/60\n",
+      "3224/3224 [==============================] - 0s 21us/step - loss: 0.6548 - acc: 0.6079 - val_loss: 0.6398 - val_acc: 0.6057\n",
+      "Epoch 5/60\n",
+      "3224/3224 [==============================] - 0s 23us/step - loss: 0.6310 - acc: 0.6259 - val_loss: 0.6110 - val_acc: 0.6354\n",
+      "Epoch 6/60\n",
+      "3224/3224 [==============================] - 0s 18us/step - loss: 0.6051 - acc: 0.7019 - val_loss: 0.5817 - val_acc: 0.7712\n",
+      "Epoch 7/60\n",
+      "3224/3224 [==============================] - 0s 17us/step - loss: 0.5776 - acc: 0.7866 - val_loss: 0.5540 - val_acc: 0.8519\n",
+      "Epoch 8/60\n",
+      "3224/3224 [==============================] - 0s 21us/step - loss: 0.5535 - acc: 0.8356 - val_loss: 0.5293 - val_acc: 0.8925\n",
+      "Epoch 9/60\n",
+      "3224/3224 [==============================] - 0s 21us/step - loss: 0.5336 - acc: 0.8583 - val_loss: 0.5077 - val_acc: 0.9027\n",
+      "Epoch 10/60\n",
+      "3224/3224 [==============================] - 0s 23us/step - loss: 0.5096 - acc: 0.8738 - val_loss: 0.4870 - val_acc: 0.9092\n",
+      "Epoch 11/60\n",
+      "3224/3224 [==============================] - 0s 21us/step - loss: 0.4969 - acc: 0.8790 - val_loss: 0.4660 - val_acc: 0.9107\n",
+      "Epoch 12/60\n",
+      "3224/3224 [==============================] - 0s 17us/step - loss: 0.4766 - acc: 0.8868 - val_loss: 0.4404 - val_acc: 0.9121\n",
+      "Epoch 13/60\n",
+      "3224/3224 [==============================] - 0s 23us/step - loss: 0.4530 - acc: 0.8874 - val_loss: 0.4152 - val_acc: 0.9179\n",
+      "Epoch 14/60\n",
+      "3224/3224 [==============================] - 0s 24us/step - loss: 0.4346 - acc: 0.8896 - val_loss: 0.3943 - val_acc: 0.9216\n",
+      "Epoch 15/60\n",
+      "3224/3224 [==============================] - 0s 22us/step - loss: 0.4197 - acc: 0.8930 - val_loss: 0.3776 - val_acc: 0.9259\n",
+      "Epoch 16/60\n",
+      "3224/3224 [==============================] - 0s 22us/step - loss: 0.4099 - acc: 0.9001 - val_loss: 0.3641 - val_acc: 0.9281\n",
+      "Epoch 17/60\n",
+      "3224/3224 [==============================] - 0s 24us/step - loss: 0.3976 - acc: 0.8970 - val_loss: 0.3523 - val_acc: 0.9296\n",
+      "Epoch 18/60\n",
+      "3224/3224 [==============================] - 0s 18us/step - loss: 0.3911 - acc: 0.8998 - val_loss: 0.3415 - val_acc: 0.9288\n",
+      "Epoch 19/60\n",
+      "3224/3224 [==============================] - 0s 21us/step - loss: 0.3814 - acc: 0.9017 - val_loss: 0.3319 - val_acc: 0.9310\n",
+      "Epoch 20/60\n",
+      "3224/3224 [==============================] - 0s 25us/step - loss: 0.3727 - acc: 0.9014 - val_loss: 0.3231 - val_acc: 0.9339\n",
+      "Epoch 21/60\n",
+      "3224/3224 [==============================] - 0s 23us/step - loss: 0.3701 - acc: 0.9001 - val_loss: 0.3154 - val_acc: 0.9361\n",
+      "Epoch 22/60\n",
+      "3224/3224 [==============================] - 0s 19us/step - loss: 0.3658 - acc: 0.9038 - val_loss: 0.3085 - val_acc: 0.9346\n",
+      "Epoch 23/60\n",
+      "3224/3224 [==============================] - 0s 21us/step - loss: 0.3594 - acc: 0.9001 - val_loss: 0.3021 - val_acc: 0.9346\n",
+      "Epoch 24/60\n",
+      "3224/3224 [==============================] - 0s 23us/step - loss: 0.3615 - acc: 0.9029 - val_loss: 0.2972 - val_acc: 0.9339\n",
+      "Epoch 25/60\n",
+      "3224/3224 [==============================] - 0s 23us/step - loss: 0.3549 - acc: 0.9054 - val_loss: 0.2923 - val_acc: 0.9339\n",
+      "Epoch 26/60\n",
+      "3224/3224 [==============================] - 0s 20us/step - loss: 0.3479 - acc: 0.9113 - val_loss: 0.2876 - val_acc: 0.9332\n",
+      "Epoch 27/60\n",
+      "3224/3224 [==============================] - 0s 21us/step - loss: 0.3405 - acc: 0.9122 - val_loss: 0.2833 - val_acc: 0.9325\n",
+      "Epoch 28/60\n",
+      "3224/3224 [==============================] - 0s 17us/step - loss: 0.3401 - acc: 0.9128 - val_loss: 0.2791 - val_acc: 0.9332\n",
+      "Epoch 29/60\n",
+      "3224/3224 [==============================] - 0s 15us/step - loss: 0.3290 - acc: 0.9166 - val_loss: 0.2745 - val_acc: 0.9354\n",
+      "Epoch 30/60\n",
+      "3224/3224 [==============================] - 0s 24us/step - loss: 0.3295 - acc: 0.9181 - val_loss: 0.2704 - val_acc: 0.9346\n",
+      "Epoch 31/60\n",
+      "3224/3224 [==============================] - 0s 24us/step - loss: 0.3235 - acc: 0.9159 - val_loss: 0.2670 - val_acc: 0.9361\n",
+      "Epoch 32/60\n",
+      "3224/3224 [==============================] - 0s 19us/step - loss: 0.3213 - acc: 0.9181 - val_loss: 0.2635 - val_acc: 0.9346\n",
+      "Epoch 33/60\n",
+      "3224/3224 [==============================] - 0s 19us/step - loss: 0.3231 - acc: 0.9169 - val_loss: 0.2601 - val_acc: 0.9354\n",
+      "Epoch 34/60\n",
+      "3224/3224 [==============================] - 0s 20us/step - loss: 0.3250 - acc: 0.9147 - val_loss: 0.2574 - val_acc: 0.9354\n",
+      "Epoch 35/60\n",
+      "3224/3224 [==============================] - 0s 22us/step - loss: 0.3085 - acc: 0.9181 - val_loss: 0.2546 - val_acc: 0.9383\n",
+      "Epoch 36/60\n",
+      "3224/3224 [==============================] - 0s 21us/step - loss: 0.3071 - acc: 0.9234 - val_loss: 0.2512 - val_acc: 0.9397\n",
+      "Epoch 37/60\n",
+      "3224/3224 [==============================] - 0s 22us/step - loss: 0.3077 - acc: 0.9212 - val_loss: 0.2486 - val_acc: 0.9383\n",
+      "Epoch 38/60\n",
+      "3224/3224 [==============================] - 0s 20us/step - loss: 0.3018 - acc: 0.9194 - val_loss: 0.2460 - val_acc: 0.9412\n",
+      "Epoch 39/60\n",
+      "3224/3224 [==============================] - 0s 19us/step - loss: 0.3099 - acc: 0.9197 - val_loss: 0.2439 - val_acc: 0.9419\n",
+      "Epoch 40/60\n",
+      "3224/3224 [==============================] - 0s 18us/step - loss: 0.2948 - acc: 0.9225 - val_loss: 0.2416 - val_acc: 0.9426\n",
+      "Epoch 41/60\n",
+      "3224/3224 [==============================] - 0s 17us/step - loss: 0.3081 - acc: 0.9172 - val_loss: 0.2400 - val_acc: 0.9434\n",
+      "Epoch 42/60\n",
+      "3224/3224 [==============================] - 0s 19us/step - loss: 0.2982 - acc: 0.9166 - val_loss: 0.2382 - val_acc: 0.9412\n",
+      "Epoch 43/60\n",
+      "3224/3224 [==============================] - 0s 18us/step - loss: 0.2981 - acc: 0.9187 - val_loss: 0.2365 - val_acc: 0.9426\n",
+      "Epoch 44/60\n",
+      "3224/3224 [==============================] - 0s 21us/step - loss: 0.2856 - acc: 0.9256 - val_loss: 0.2343 - val_acc: 0.9426\n",
+      "Epoch 45/60\n",
+      "3224/3224 [==============================] - 0s 18us/step - loss: 0.2940 - acc: 0.9206 - val_loss: 0.2326 - val_acc: 0.9426\n",
+      "Epoch 46/60\n",
+      "3224/3224 [==============================] - 0s 19us/step - loss: 0.2941 - acc: 0.9200 - val_loss: 0.2314 - val_acc: 0.9419\n",
+      "Epoch 47/60\n",
+      "3224/3224 [==============================] - 0s 17us/step - loss: 0.2887 - acc: 0.9218 - val_loss: 0.2303 - val_acc: 0.9426\n",
+      "Epoch 48/60\n",
+      "3224/3224 [==============================] - 0s 19us/step - loss: 0.2866 - acc: 0.9212 - val_loss: 0.2292 - val_acc: 0.9405\n",
+      "Epoch 49/60\n",
+      "3224/3224 [==============================] - 0s 22us/step - loss: 0.2832 - acc: 0.9200 - val_loss: 0.2283 - val_acc: 0.9419\n",
+      "Epoch 50/60\n",
+      "3224/3224 [==============================] - 0s 22us/step - loss: 0.2748 - acc: 0.9187 - val_loss: 0.2271 - val_acc: 0.9426\n",
+      "Epoch 51/60\n",
+      "3224/3224 [==============================] - 0s 26us/step - loss: 0.2860 - acc: 0.9116 - val_loss: 0.2263 - val_acc: 0.9426\n",
+      "Epoch 52/60\n",
+      "3224/3224 [==============================] - 0s 22us/step - loss: 0.2767 - acc: 0.9197 - val_loss: 0.2248 - val_acc: 0.9426\n",
+      "Epoch 53/60\n",
+      "3224/3224 [==============================] - 0s 21us/step - loss: 0.2685 - acc: 0.9274 - val_loss: 0.2222 - val_acc: 0.9397\n",
+      "Epoch 54/60\n",
+      "3224/3224 [==============================] - 0s 20us/step - loss: 0.2775 - acc: 0.9209 - val_loss: 0.2223 - val_acc: 0.9434\n",
+      "Epoch 55/60\n",
+      "3224/3224 [==============================] - 0s 18us/step - loss: 0.2779 - acc: 0.9175 - val_loss: 0.2217 - val_acc: 0.9434\n",
+      "Epoch 56/60\n",
+      "3224/3224 [==============================] - 0s 22us/step - loss: 0.2658 - acc: 0.9265 - val_loss: 0.2201 - val_acc: 0.9434\n",
+      "Epoch 57/60\n",
+      "3224/3224 [==============================] - 0s 18us/step - loss: 0.2720 - acc: 0.9190 - val_loss: 0.2190 - val_acc: 0.9434\n",
+      "Epoch 58/60\n",
+      "3224/3224 [==============================] - 0s 23us/step - loss: 0.2704 - acc: 0.9212 - val_loss: 0.2200 - val_acc: 0.9434\n",
+      "Epoch 59/60\n",
+      "3224/3224 [==============================] - 0s 20us/step - loss: 0.2759 - acc: 0.9116 - val_loss: 0.2199 - val_acc: 0.9434\n",
+      "Epoch 60/60\n",
+      "3224/3224 [==============================] - 0s 18us/step - loss: 0.2678 - acc: 0.9228 - val_loss: 0.2182 - val_acc: 0.9434\n"
+     ]
+    }
+   ],
+   "source": [
+    "history=model.fit(train_data,\n",
+    "                    train_label,\n",
+    "                    epochs=60,\n",
+    "                    batch_size=100,\n",
+    "                    validation_data=(test_data, test_label),\n",
+    "                    verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "history_dict=history.history\n",
+    "history_dict.keys()\n",
+    "dict_keys=['loss', 'val_loss', 'val_acc', 'acc']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "acc = history.history['acc']\n",
+    "val_acc = history.history['val_acc']\n",
+    "loss = history.history['loss']\n",
+    "val_loss = history.history['val_loss']\n",
+    "epochs = range(1, len(acc) + 1)\n",
+    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
+    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
+    "plt.title('Training and validation loss')\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.ylabel('Loss')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "acc_values=history_dict['acc']\n",
+    "val_acc_values=history_dict['val_acc']\n",
+    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
+    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
+    "plt.title('Training and validation accuracy')\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_prediction_data(file_path):\n",
+    "    data=[]\n",
+    "    with open(file_path,\"r\") as data_file:\n",
+    "        raw_data=data_file.readlines()\n",
+    "        for raw_line in raw_data:\n",
+    "            sample=raw_line[:-1]\n",
+    "            sample=sample.split(\",\")\n",
+    "            sample=np.array([float(feature) for feature in sample])\n",
+    "            sample[-3]/=10\n",
+    "            sample[-2]/=100\n",
+    "            sample[-1]/=1000\n",
+    "            data.append(sample)\n",
+    "    return np.array(data)\n",
+    "prediction_data=load_prediction_data(\"get_the_flag.data\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = model.predict(prediction_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3600x72 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_show=[]\n",
+    "for i in predictions:\n",
+    "    if i[0]<0.5:\n",
+    "        data_show.append(0)\n",
+    "    else:\n",
+    "        data_show.append(1)\n",
+    "plt.rcParams['figure.figsize'] = (50.0, 1.0) \n",
+    "plt.scatter(range(len(data_show)),data_show,linewidth=0.1,s=10)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "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": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/\346\223\215\344\275\234\346\227\245\345\277\227/test.data" "b/\346\223\215\344\275\234\346\227\245\345\277\227/test.data"
new file mode 100644
index 0000000..1a78dd7
--- /dev/null
+++ "b/\346\223\215\344\275\234\346\227\245\345\277\227/test.data"
@@ -0,0 +1,1377 @@
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diff --git "a/\346\223\215\344\275\234\346\227\245\345\277\227/train.data" "b/\346\223\215\344\275\234\346\227\245\345\277\227/train.data"
new file mode 100644
index 0000000..260fb4c
--- /dev/null
+++ "b/\346\223\215\344\275\234\346\227\245\345\277\227/train.data"
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diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/Model/eval_data.plk" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/Model/eval_data.plk"
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diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/Model/eval_labels.plk" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/Model/eval_labels.plk"
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diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/Model/train_data.plk" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/Model/train_data.plk"
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diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/Model/train_labels.plk" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/Model/train_labels.plk"
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Binary files /dev/null and "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/Model/train_labels.plk" differ
diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/StduyTest/StudyOneHot.py" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/StduyTest/StudyOneHot.py"
new file mode 100644
index 0000000..b3cbf57
--- /dev/null
+++ "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/StduyTest/StudyOneHot.py"	
@@ -0,0 +1,23 @@
+import tensorflow as tf
+# import os
+# os.system('cls')
+
+NUM_CLASSES = 30 # 10分类
+
+labels = list(range(0,30,1)) # sample label
+print("labels",labels)
+
+batch_size = tf.size(labels) # get size of labels : 4
+
+labels = tf.expand_dims(labels, 1) # 增加一个维度
+
+indices = tf.expand_dims(tf.range(0, batch_size,1), 1) #生成索引
+concated = tf.concat([indices, labels] , 1) #作为拼接
+onehot_labels = tf.sparse_to_dense(concated, tf.stack([batch_size, NUM_CLASSES]), 1.0, 0.0) # 生成one-hot编码的标签
+
+with tf.Session() as ssess:
+    print("labels",labels.eval())
+    print("indices",indices.eval())
+    print("concated",concated.eval())
+    print(onehot_labels[0])
+
diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/StduyTest/study_cifar10_cnn.py" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/StduyTest/study_cifar10_cnn.py"
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--- /dev/null
+++ "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/StduyTest/study_cifar10_cnn.py"	
@@ -0,0 +1,140 @@
+'''Train a simple deep CNN on the CIFAR10 small images dataset.
+
+It gets to 75% validation accuracy in 25 epochs, and 79% after 50 epochs.
+(it's still underfitting at that point, though).
+'''
+
+from __future__ import print_function
+import keras
+from keras.datasets import cifar10
+from keras.preprocessing.image import ImageDataGenerator
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation, Flatten
+from keras.layers import Conv2D, MaxPooling2D
+import os
+
+batch_size = 32     #批尺度——梯度下降法
+                    # batch_size=1 
+                    # batch_size=N ( N为总数量 ) 
+                    # batch_size=n ( n < N )
+
+num_classes = 10    #
+epochs = 100        #
+data_augmentation = True
+num_predictions = 20
+save_dir = os.path.join(os.getcwd(), 'saved_models')
+model_name = 'keras_cifar10_trained_model.h5'
+
+# The data, split between train and test sets: 
+# 数据分为训练集和测试集：
+(x_train, y_train), (x_test, y_test) = cifar10.load_data()
+print('x_train shape:', x_train.shape)
+print(x_train.shape[0], 'train samples')
+print(x_test.shape[0], 'test samples')
+
+# Convert class vectors to binary class matrices.
+# 将类向量转换为二进制类矩阵。
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
+
+model = Sequential()
+model.add(Conv2D(32, (3, 3), padding='same',
+                 input_shape=x_train.shape[1:]))
+model.add(Activation('relu'))
+model.add(Conv2D(32, (3, 3)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+
+model.add(Conv2D(64, (3, 3), padding='same'))
+model.add(Activation('relu'))
+model.add(Conv2D(64, (3, 3)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+
+model.add(Flatten())
+model.add(Dense(512))
+model.add(Activation('relu'))
+model.add(Dropout(0.5))
+model.add(Dense(num_classes))
+model.add(Activation('softmax'))
+
+# initiate RMSprop optimizer
+# 启动RMSprop优化器
+opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
+
+# Let's train the model using RMSprop
+# 我们使用RMSprop来训练模型
+model.compile(loss='categorical_crossentropy',
+              optimizer=opt,
+              metrics=['accuracy'])
+
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
+
+if not data_augmentation:
+    print('Not using data augmentation.')
+    model.fit(x_train, y_train,
+              batch_size=batch_size,
+              epochs=epochs,
+              validation_data=(x_test, y_test),
+              shuffle=True)
+else:
+    print('Using real-time data augmentation.')
+    # This will do preprocessing and realtime data augmentation:
+    # 这将做预处理和实时数据增强：
+        # 在深度学习中，我们经常需要用到一些技巧(比如将图片进行旋转，翻转等)来进行data augmentation, 来减少过拟合。
+    datagen = ImageDataGenerator(
+        featurewise_center=False,  # set input mean to 0 over the dataset
+                                   # 在数据集上设置输入均值为0
+        samplewise_center=False,  # set each sample mean to 0
+                                  # 将每个样本均值设置为0
+        featurewise_std_normalization=False,  # divide inputs by std of the dataset
+                                              # 按照数据集的std(标准)分割输入
+        samplewise_std_normalization=False,  # divide each input by its std
+                                             #
+        zca_whitening=False,  # apply ZCA whitening
+                              #
+                              
+        rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
+                           # 整数，数据提升时图片随机转动的角度
+        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
+                                # 浮点数，图片宽度的某个比例，数据提升时图片水平偏移的幅度`
+        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
+                                 # 浮点数，图片高度的某个比例，数据提升时图片竖直偏移的幅度
+        horizontal_flip=True,  # randomly flip images
+                               # 布尔值，进行随机水平翻转
+        vertical_flip=False  # randomly flip images
+                             # 布尔值，进行随机竖直翻转
+    )
+
+    # Compute quantities required for feature-wise normalization
+    # 计算功能所需的标准化所需的数量
+    # (std, mean, and principal components if ZCA whitening is applied).
+    # (如果应用ZCA白化，则为标准，平均和主要成分).
+    datagen.fit(x_train)
+
+    # Fit the model on the batches generated by datagen.flow().
+    # 将模型放在由 datagen.flow() 生成的批处理上。
+    model.fit_generator(datagen.flow(x_train, y_train,
+                                     batch_size=batch_size),
+                        epochs=epochs,
+                        validation_data=(x_test, y_test),
+                        workers=4)
+
+# Save model and weights
+# 保存模型和权重
+if not os.path.isdir(save_dir):
+    os.makedirs(save_dir)
+model_path = os.path.join(save_dir, model_name)
+model.save(model_path)
+print('Saved trained model at %s ' % model_path)
+
+# Score trained model.
+# 评分训练模型
+scores = model.evaluate(x_test, y_test, verbose=1)
+print('Test loss:', scores[0])
+print('Test accuracy:', scores[1])
diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/StduyTest/study_keras_cnn.py" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/StduyTest/study_keras_cnn.py"
new file mode 100644
index 0000000..dd5fc31
--- /dev/null
+++ "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/StduyTest/study_keras_cnn.py"	
@@ -0,0 +1,12 @@
+# -*- coding: utf-8 -*-
+
+
+
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation, Flatten
+from keras.layers import Convolution2D, MaxPooling2D
+from keras.utils import np_utils
+from keras import backend as K
+
+
diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/StduyTest/study_load_file.py" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/StduyTest/study_load_file.py"
new file mode 100644
index 0000000..64e9dfd
--- /dev/null
+++ "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/StduyTest/study_load_file.py"	
@@ -0,0 +1,41 @@
+# encoding: utf-8
+'''
+1、读取指定目录下的所有文件
+2、读取文件，正则匹配出需要的内容，获取文件名
+3、打开此文件(可以选择打开可以选择复制到别的地方去)
+'''
+import os.path
+import sys
+import re
+
+# 遍历指定目录，显示目录下的所有文件名
+def eachFile(filepath):
+    pathDir =  os.listdir(filepath)
+    for allDir in pathDir:
+            print(allDir)
+        # child = os.path.join('%s\%s' % (filepath, allDir))
+        # if os.path.isfile(child):
+        #     readFile(child)
+        #     # print child.decode('gbk') # .decode('gbk')是解决中文显示乱码问题
+        #     continue
+        # eachFile(child)
+
+# # 遍历出结果 返回文件的名字
+# def readFile(filenames):
+#         fopen = open(filenames, 'r', encoding='UTF-8') # r 代表read
+#         fileread = fopen.read()
+#         fopen.close()
+#         t=re.search(r'clearSpitValve',fileread)
+#         if t:
+#             # print "匹配到的文件是:"+filenames
+#             arr.append(filenames)    
+
+if __name__ == "__main__":
+    print('目前系统的编码为：',sys.getdefaultencoding()) 
+
+    filenames = "Data/logos/" # refer root dir
+    arr=[]
+    eachFile(filenames)
+    for i in arr:
+        print(i)
+
diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/StduyTest/test.py" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/StduyTest/test.py"
new file mode 100644
index 0000000..7c43175
--- /dev/null
+++ "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/StduyTest/test.py"	
@@ -0,0 +1,79 @@
+# import json
+
+# def myJsonLoad(filePath):
+#     '''把文件打开从字符串转换成数据类型'''
+#     with open(filePath,'rb') as load_file:
+#         load_dict = json.load(load_file)
+#         return load_dict
+
+# logo_id =myJsonLoad('Data/logo_30/id_label.json') 
+# print(logo_id)
+
+
+
+
+# import re
+# allDir =  'aodi.500_0aic_QXOX3HGW_0DHP431L.jpg'
+# theTpye = re.split('\.',allDir )[0]
+# print(theTpye)
+
+
+# import tensorflow as tf
+# def myOneHot(num_classes):
+#     NUM_CLASSES = num_classes  # 分类个数
+
+#     labels = list(range(0,num_classes,1)) # sample label
+
+#     batch_size = tf.size(labels) # get size of labels : 4
+
+#     labels = tf.expand_dims(labels, 1) # 增加一个维度
+#     indices = tf.expand_dims(tf.range(0, batch_size,1), 1) #生成索引
+#     concated = tf.concat([indices, labels] , 1) #作为拼接
+#     onehot_labels = tf.sparse_to_dense(concated, tf.stack([batch_size, NUM_CLASSES]), 1.0, 0.0) # 生成one-hot编码的标签    
+#     with tf.Session() as ssess:
+#         # print(onehot_labels.eval())
+#         return onehot_labels.eval()
+
+# ontHot = myOneHot(30)
+# print(ontHot[1])
+
+
+
+# import tensorflow as tf
+# x = tf.placeholder(tf.float32,[None, 784*3])
+# data = tf.reshape(x, [-1, 28, 28, 3]) #最后一维代表通道数目，如果是rgb则为3 
+
+# print(x)
+# print(data)
+
+
+
+
+
+
+# import tensorflow as tf
+# data = tf.placeholder(tf.float32,[1, 5])
+# # with tf.Session() as sess:
+# print(data)
+
+
+
+
+import tensorflow as tf
+# python pkl 文件读写
+import pickle as pickle
+
+train_data_np = pickle.load(open('Model/train_data.plk', 'rb'))
+train_labels = pickle.load(open('Model/train_labels.plk', 'rb'))
+
+eval_data_np = pickle.load(open('Model/eval_data.plk', 'rb'))
+eval_labels = pickle.load(open('Model/eval_labels.plk', 'rb'))
+
+with tf.Session() as sess:
+    train_data = tf.convert_to_tensor(train_data_np)
+    eval_data = tf.convert_to_tensor(eval_data_np)
+
+    print(train_data.eval())
+# print(train_labels)
+    print(eval_data.eval())
+# print(eval_labels)
\ No newline at end of file
diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/Utiliy/MyJson.py" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/Utiliy/MyJson.py"
new file mode 100644
index 0000000..985cd8e
--- /dev/null
+++ "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/Utiliy/MyJson.py"	
@@ -0,0 +1,40 @@
+# -*- coding: utf-8 -*-
+
+import json
+'''
+    Json模块提供了四个功能：dumps、dump、loads、load
+        - dumps把python数据类型转换成字符串         json_str = json.dumps(test_dict)
+        - dump把数据类型转换成字符串并存储在文件中   json.dump(new_dict,file)
+        - loads把字符串转换成数据类型               new_dict = json.loads(json_str)
+        - load把文件打开从字符串转换成数据类型       load_dict = json.load(load_file)
+'''
+
+def myJsonDump(filePath,pyDict):
+    '''dump把数据类型转换成字符串并存储在文件中'''
+    with open(filePath,"w") as fileJson:
+        json.dump(pyDict,fileJson)
+        print("加载入文件完成...")
+
+def myJsonLoad(filePath):
+    '''把文件打开从字符串转换成数据类型'''
+    with open(filePath,'r') as load_file:
+        load_dict = json.load(load_file)
+        return load_dict
+'''
+写入json文件示例：
+    test_dict = {'bigberg': [7600, {1: [['iPhone', 6300], ['Bike', 800], ['shirt', 300]]}]}
+
+    with open("../config/record.json",'r') as load_f:
+        load_dict = json.load(load_f)
+        print(load_dict)
+
+
+    load_dict['smallberg'] = [8200,{1:[['Python',81],['shirt',300]]}]
+    print(load_dict)
+
+    with open("../config/record.json","w") as dump_f:
+        json.dump(load_dict,dump_f)
+
+'''
+
+
diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/Utiliy/__init__.py" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/Utiliy/__init__.py"
new file mode 100644
index 0000000..633f866
--- /dev/null
+++ "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/Utiliy/__init__.py"	
@@ -0,0 +1,2 @@
+# -*- coding: utf-8 -*-
+
diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/Utiliy/studyJson.py" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/Utiliy/studyJson.py"
new file mode 100644
index 0000000..35236a8
--- /dev/null
+++ "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/Utiliy/studyJson.py"	
@@ -0,0 +1,45 @@
+# -*- coding: utf-8 -*-
+
+import json
+'''
+    Json模块提供了四个功能：dumps、dump、loads、load
+        - dumps把python数据类型转换成字符串         json_str = json.dumps(test_dict)
+        - dump把数据类型转换成字符串并存储在文件中  
+        - loads把字符串转换成数据类型
+        - load把文件打开从字符串转换成数据类型
+'''
+
+'''     dumps：将python中的 字典 转换为 字符串  '''
+test_dict = {'bigberg': [7600, {1: [['iPhone', 6300], ['Bike', 800], ['shirt', 300]]}]}
+print(test_dict)
+print(type(test_dict))
+#dumps 将数据转换成字符串
+json_str = json.dumps(test_dict)
+print(json_str)
+print(type(json_str))
+
+'''    loads: 将 字符串 转换为 字典    '''
+new_dict = json.loads(json_str)
+print(new_dict)
+print(type(new_dict))
+
+'''    dump: 将数据写入json文件中   '''
+with open("../config/record.json","w") as f:
+    json.dump(new_dict,f)
+    print("加载入文件完成...")
+
+'''     load:把文件打开，并把字符串变换为数据类型   '''
+# with open("../config/record.json",'r') as load_f:
+with open("record.json",'r') as load_f:
+    load_dict = json.load(load_f)
+    print(load_dict)
+
+
+load_dict['smallberg'] = [8200,{1:[['Python',81],['shirt',300]]}]
+print(load_dict)
+
+# with open("../config/record.json","w") as dump_f:
+with open("record.json","w") as dump_f:
+    json.dump(load_dict,dump_f)
+
+
diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/input_data.py" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/input_data.py"
new file mode 100644
index 0000000..67776d5
--- /dev/null
+++ "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/input_data.py"	
@@ -0,0 +1,302 @@
+# coding:utf-8
+
+import os.path
+import sys
+import re
+import os
+import json
+
+#python pkl 文件读写
+import pickle as pickle
+
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import numpy as np
+
+def myJsonLoad(filePath):
+    '''把文件打开从字符串转换成数据类型'''
+    with open(filePath,'rb') as load_file:
+        load_dict = json.load(load_file)
+        return load_dict
+
+def load_id_zh():
+    return myJsonLoad('Data/logo_30/id_label_zh.json') 
+def load_id_us():
+    return myJsonLoad('Data/logo_30/id_label_us.json') 
+
+def myOneHot(num_classes):
+    NUM_CLASSES = num_classes  # 分类个数
+
+    labels = list(range(0,num_classes,1)) # sample label
+
+    batch_size = tf.size(labels) # get size of labels : 4
+
+    labels = tf.expand_dims(labels, 1) # 增加一个维度
+    indices = tf.expand_dims(tf.range(0, batch_size,1), 1) #生成索引
+    concated = tf.concat([indices, labels] , 1) #作为拼接
+    onehot_labels = tf.sparse_to_dense(concated, tf.stack([batch_size, NUM_CLASSES]), 1.0, 0.0) # 生成one-hot编码的标签    
+    with tf.Session() as ssess:
+        # print(onehot_labels.eval())
+        return onehot_labels.eval()
+
+class MyData():
+    def __init__(self):
+        self.data_filePath = []
+        self.data_fileName = []
+        self.data_tpye = []
+
+        self.data = []
+        # self.data = tf.placeholder(tf.float32,[1, 784*3])
+        # self.data = tf.reshape(x, [-1, 28, 28, 3]) #最后一维代表通道数目，如果是rgb则为3 
+        # 此函数可以理解为形参，用于定义过程，在执行的时候再赋具体的值
+        
+        self.labels = []
+        # self.data_oneHot_labels = []
+
+# 遍历指定目录，显示目录下的所有文件名
+def eachFile(filepath):
+    pathDir =  os.listdir(filepath)
+    ontHot = myOneHot(30)
+    data = MyData()
+    id_dict = load_id_us()
+    for allDir in pathDir:
+        child = os.path.join('%s/%s' % (filepath, allDir))
+        if os.path.isfile(child):
+            data.data_filePath.append(child)
+            data.data_fileName.append(allDir)
+            theTpye = re.split('\.',allDir)[0]
+            # print(theTpye)
+            data.data_tpye.append( theTpye )
+            data.labels.append( int(id_dict[theTpye]) -1 )
+            # data.data_oneHot_labels.append( ontHot[ int(id_dict[theTpye]) -1 ] )
+
+    # # 显示
+    # for i in array:
+    #     print(i)      
+    return data
+
+
+def myFastGFile(py_data):
+    # 新建一个Session
+    with tf.Session() as sess:
+        '''
+        image_raw_data = tf.gfile.FastGFile(py_data.data_filePath[0], 'rb').read()
+        img_data = tf.image.decode_jpeg(image_raw_data)
+        plt.imshow(img_data.eval())
+        plt.show()
+
+        resized = tf.image.resize_images(img_data, [28, 28], method=0)
+        print(resized)
+        resized = tf.reshape(resized, [28, 28, 3]) #最后一维代表通道数目，如果是rgb则为3 
+        print(resized)
+        # TensorFlow的函数处理图片后存储的数据是float32格式的，需要转换成uint8才能正确打印图片。
+        print("Digital type: ", resized.dtype)
+        resized = np.asarray(resized.eval(), dtype='uint8')
+        
+        # tf.image.convert_image_dtype(rgb_image, tf.float32)
+        plt.imshow(resized)
+        plt.show()
+        '''
+
+        '''
+        image_raw_data = tf.gfile.FastGFile(py_data.data_filePath[0], 'rb').read()
+        img_data = tf.image.decode_jpeg(image_raw_data)
+        plt.imshow(img_data.eval())
+        plt.show()
+
+        resized = tf.image.resize_images(img_data, [28, 28], method=0)
+        
+        # TensorFlow的函数处理图片后存储的数据是float32格式的，需要转换成uint8才能正确打印图片。
+        print("Digital type: ", resized.dtype)
+        resized = np.asarray(resized.eval(), dtype='uint8')
+        
+        # tf.image.convert_image_dtype(rgb_image, tf.float32)
+        plt.imshow(resized)
+        plt.show()
+        '''
+        # path = py_data.data_filePath[0]
+        for path in py_data.data_filePath:
+            # 读取文件
+            image_raw_data = tf.gfile.FastGFile(path, 'rb').read()
+            # 解码
+            img_data = tf.image.decode_jpeg(image_raw_data)
+            # print(img_data)
+            # 转灰度图
+            img_data = sess.run(tf.image.rgb_to_grayscale(img_data))  
+            # 改变图片尺寸
+            resized = tf.image.resize_images(img_data, [28, 28], method=0)
+            # 设定 shape
+            resized = tf.reshape(resized, [28, 28, 1]) #最后一维代表通道数目，如果是rgb则为3 
+            # 标准化
+            standardization_image = tf.image.per_image_standardization(resized)#标准化
+            # print(standardization_image)
+            # print(standardization_image.eval())
+            resized = tf.reshape(standardization_image, [-1]) #最后一维代表通道数目，如果是rgb则为3 
+
+            ## 链接     
+            ## resized = tf.expand_dims(resized, 0) # 增加一个维度
+            ## print(resized)
+            ## print(py_data.data)
+            ## test_data = tf.concat(0, [test_data, resized])
+            
+            py_data.data.append(resized.eval())
+
+        '''
+        # #验证数据转换正确
+        resized = tf.reshape(py_data.data[0], [28, 28, 3])
+        resized = np.asarray(resized.eval(), dtype='uint8')        
+        plt.imshow(resized)
+        plt.show()
+        '''
+        
+
+        # print(py_data.data.shape)
+
+        # # string_input_producer会产生一个文件名队列
+        # filename_queue = tf.train.string_input_producer(fileArray, shuffle=False, num_epochs=5)
+        
+        # # reader从文件名队列中读数据。对应的方法是reader.read
+        # reader = tf.WholeFileReader()
+        # key, value = reader.read(filename_queue)
+        
+        # # tf.train.string_input_producer定义了一个epoch变量，要对它进行初始化
+        # tf.local_variables_initializer().run()
+
+        # # 使用start_queue_runners之后，才会开始填充队列
+        # threads = tf.train.start_queue_runners(sess=sess)
+        # i = 0
+        # while True:
+        #     i += 1
+        #     # 获取图片数据并保存
+        #     image_data = sess.run(value)
+        #     with open('read/test_%d.jpg' % i, 'wb') as f:
+        #         f.write(image_data)
+
+
+
+
+        # 读取图像数据
+        # img = tf.gfile.FastGFile(fileArray[0], 'rb').read()
+        # print(img)
+        
+
+        # # 用ipeg格式将图像解码得到三维矩阵(png格式用decode_png)
+        # # 解码后得到结果为张量
+        # img_data = tf.image.decode_jpeg(img)
+        # # print(img_data.shape)
+        # plt.imshow(img_data.eval())
+        # plt.show()
+
+        # # 打印出得到的三维矩阵
+        # # print( img_data.eval() )
+
+if __name__ == "__main__":
+    print('目前系统的编码为：',sys.getdefaultencoding()) 
+
+    trainData = eachFile("Data/logos/train") #注意：末尾不加/
+    # for i in range(0,len(data.data_fileName)):
+    #     print(data.data_tpye[i])
+    #     print(data.data_oneHot_labels[i])
+
+    myFastGFile(trainData)  
+    # print(trainData.data[0].shape)
+    # print(trainData.data[0])
+    '''
+    with tf.Session() as sess:
+        train_data =tf.convert_to_tensor(np.array( trainData.data ) )
+    '''
+    train_data = np.array( trainData.data )
+    train_labels = trainData.labels
+    
+    # *******************************
+
+    evalData = eachFile("Data/logos/eval") #注意：末尾不加/
+    # for i in range(0,len(data.data_fileName)):
+    #     print(data.data_tpye[i])
+    #     print(data.data_oneHot_labels[i])
+
+    myFastGFile(evalData)  
+    # print(evalData.data[0].shape)
+    # print(evalData.data[0])
+    '''
+    with tf.Session() as sess:
+        train_data =tf.convert_to_tensor(np.array( evalData.data ) )
+    '''
+    eval_data = np.array( evalData.data )
+    eval_labels = evalData.labels
+
+
+
+    # import os
+    if os.path.exists('Model/train_data.plk'): #删除文件，可使用以下两种方法。
+        os.remove('Model/train_data.plk')      #os.unlink(my_file)
+    if os.path.exists('Model/eval_data.plk'): #删除文件，可使用以下两种方法。
+        os.remove('Model/eval_data.plk')      #os.unlink(my_file)
+    if os.path.exists('Model/train_labels.plk'): #删除文件，可使用以下两种方法。
+        os.remove('Model/train_labels.plk')      #os.unlink(my_file)
+    if os.path.exists('Model/eval_labels.plk'): #删除文件，可使用以下两种方法。
+        os.remove('Model/eval_labels.plk')      #os.unlink(my_file)
+
+    
+
+    with open('Model/train_data.plk','wb') as f:
+        pickle.dump(train_data, f)
+    with open('Model/eval_data.plk','wb') as f:
+        pickle.dump(eval_data, f)
+    with open('Model/train_labels.plk','wb') as f:
+        pickle.dump(train_labels, f)
+    with open('Model/eval_labels.plk','wb') as f:
+        pickle.dump(eval_labels, f)
+
+
+    print('done!')
+
+# # 读取图像数据
+# img = tf.gfile.FastGFile('daibola.jpg').read()
+
+# with tf.Session() as sess:
+#     # 用ipeg格式将图像解码得到三维矩阵(png格式用decode_png)
+#     # 解码后得到结果为张量
+#     img_data = tf.image.decode_jpeg(img)
+#     # 打印出得到的三维矩阵
+#     print( img_data.eval() )
+    
+#     # 使用pyplot可视化得到的图像
+#     plt.imshow(img_data.eval())
+#     plt.show()
+
+#     #转换格式   
+#     # 转换图像的数据类型
+#     img_data = tf.image.convert_image_dtype(img_data, dtype=tf.uint8)
+#     # 将图像的三维矩阵重新按照png格式存入文件
+#     encoded_image = tf.image.encode_png(img_data)
+#     # 得到图像的png格式
+#     with tf.gfile.GFile('model/model.png', 'wb') as f:
+#         f.write(encoded_image.eval())
+
+
+'''
+
+import TensorFlow as tf 
+
+# 新建一个Session
+with tf.Session() as sess:
+    # 我们要读三幅图片A.jpg, B.jpg, C.jpg
+    filename = ['A.jpg', 'B.jpg', 'C.jpg']
+    # string_input_producer会产生一个文件名队列
+    filename_queue = tf.train.string_input_producer(filename, shuffle=False, num_epochs=5)
+    # reader从文件名队列中读数据。对应的方法是reader.read
+    reader = tf.WholeFileReader()
+    key, value = reader.read(filename_queue)
+    # tf.train.string_input_producer定义了一个epoch变量，要对它进行初始化
+    tf.local_variables_initializer().run()
+    # 使用start_queue_runners之后，才会开始填充队列
+    threads = tf.train.start_queue_runners(sess=sess)
+    i = 0
+    while True:
+        i += 1
+        # 获取图片数据并保存
+        image_data = sess.run(value)
+        with open('read/test_%d.jpg' % i, 'wb') as f:
+            f.write(image_data)
+'''
diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/mnist_convnet_model/checkpoint" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/mnist_convnet_model/checkpoint"
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--- /dev/null
+++ "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/mnist_convnet_model/checkpoint"	
@@ -0,0 +1,6 @@
+model_checkpoint_path: "model.ckpt-2000"
+all_model_checkpoint_paths: "model.ckpt-1000"
+all_model_checkpoint_paths: "model.ckpt-1001"
+all_model_checkpoint_paths: "model.ckpt-1500"
+all_model_checkpoint_paths: "model.ckpt-1501"
+all_model_checkpoint_paths: "model.ckpt-2000"
diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/mnist_convnet_model/eval/events.out.tfevents.1523480157.MACHINE" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/mnist_convnet_model/eval/events.out.tfevents.1523480157.MACHINE"
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diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/mnist_convnet_model/graph.pbtxt" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/mnist_convnet_model/graph.pbtxt"
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index 0000000..22f1533
--- /dev/null
+++ "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/mnist_convnet_model/graph.pbtxt"	
@@ -0,0 +1,13856 @@
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diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/mnist_convnet_model/model.ckpt-1000.data-00000-of-00001" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/Here is the code/mnist_convnet_model/model.ckpt-1000.data-00000-of-00001"
new file mode 100644
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@@ -0,0 +1,240 @@
+#  Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+"""Convolutional Neural Network Estimator for MNIST, built with tf.layers."""
+
+import numpy as np
+import tensorflow as tf
+
+# python pkl 文件读写
+import pickle as pickle
+
+
+tf.logging.set_verbosity(tf.logging.INFO)
+'''
+    作用：将 TensorFlow 日志信息输出到屏幕
+    
+    TensorFlow有五个不同级别的日志信息。其严重性为调试DEBUG<信息INFO<警告WARN<错误ERROR<致命FATAL。当你配置日志记录在任何级别，TensorFlow将输出与该级别相对应的所有日志消息以及更高程度严重性的所有级别的日志信息。例如，如果设置错误的日志记录级别，将得到包含错误和致命消息的日志输出，并且如果设置了调试级别，则将从所有五个级别获取日志消息。
+    
+    默认情况下，TENSFlow在WARN的日志记录级别进行配置，但是在跟踪模型训练时，需要将级别调整为INFO
+'''
+
+def cnn_model_fn(features, labels, mode):
+    '''CNN函数模型'''
+    # Input Layer
+    # Reshape X to 4-D tensor: [batch_size, width, height, channels]
+    # MNIST images are 28x28 pixels, and have one color channel
+    input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
+    '''图片规范化为28*28'''
+    # Convolutional Layer #1
+    # Computes 32 features using a 5x5 filter with ReLU activation.
+    # Padding is added to preserve width and height.
+    # Input Tensor Shape: [batch_size, 28, 28, 1]
+    # Output Tensor Shape: [batch_size, 28, 28, 32]
+    '''第一层'''
+    '''
+        作用
+        
+        2D 卷积层的函数接口 这个层创建了一个卷积核，将输入进行卷积来输出一个 tensor。如果 use_bias 是 True（且提供了 bias_initializer），则一个偏差向量会被加到输出中。最后，如果 activation 不是 None，激活函数也会被应用到输出中。
+        
+        参数
+        
+        inputs：Tensor 输入
+        
+        filters：整数，表示输出空间的维数（即卷积过滤器的数量）
+        
+        kernel_size：一个整数，或者包含了两个整数的元组/队列，表示卷积窗的高和宽。如果是一个整数，则宽高相等。
+        
+        strides：一个整数，或者包含了两个整数的元组/队列，表示卷积的纵向和横向的步长。如果是一个整数，则横纵步长相等。另外， strides 不等于1 和 dilation_rate 不等于1 这两种情况不能同时存在。
+        
+        padding："valid" 或者 "same"（不区分大小写）。"valid" 表示不够卷积核大小的块就丢弃，"same"表示不够卷积核大小的块就补0。 "valid" 的输出形状为
+        
+        "valid" 的输出形状为
+        其中， 为输入的 size（高或宽）， 为 filter 的 size， 为 strides 的大小， 为向上取整。
+        data_format：channels_last 或者 channels_first，表示输入维度的排序。
+    '''
+    conv1 = tf.layers.conv2d(
+        inputs=input_layer,
+        filters=32,
+        kernel_size=[5, 5],
+        padding="same",
+        activation=tf.nn.relu)
+
+    # Pooling Layer #1
+    # First max pooling layer with a 2x2 filter and stride of 2
+    # Input Tensor Shape: [batch_size, 28, 28, 32]
+    # Output Tensor Shape: [batch_size, 14, 14, 32]
+    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
+    '''
+        池化
+        目的是使得图片表示更小更可管理
+        对每个activation map进行独立操作
+    '''
+    # Convolutional Layer #2
+    # Computes 64 features using a 5x5 filter.
+    # Padding is added to preserve width and height.
+    # Input Tensor Shape: [batch_size, 14, 14, 32]
+    # Output Tensor Shape: [batch_size, 14, 14, 64]
+    '''第二层同理'''
+    conv2 = tf.layers.conv2d(
+        inputs=pool1,
+        filters=64,
+        kernel_size=[5, 5],
+        padding="same",
+        activation=tf.nn.relu)
+    # Pooling Layer #2
+    # Second max pooling layer with a 2x2 filter and stride of 2
+    # Input Tensor Shape: [batch_size, 14, 14, 64]
+    # Output Tensor Shape: [batch_size, 7, 7, 64]
+    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
+    # Flatten tensor into a batch of vectors
+    # Input Tensor Shape: [batch_size, 7, 7, 64]
+    # Output Tensor Shape: [batch_size, 7 * 7 * 64]
+    pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
+    # Dense Layer
+    # Densely connected layer with 1024 neurons
+    # Input Tensor Shape: [batch_size, 7 * 7 * 64]
+    # Output Tensor Shape: [batch_size, 1024]
+    '''
+        dense ：全连接层  相当于添加一个层
+    '''
+    dense = tf.layers.dense(
+        inputs=pool2_flat, units=1024, activation=tf.nn.relu)
+    # Add dropout operation; 0.6 probability that element will be kept
+    '''
+        训练时丢弃一些节点，inputs是输入层,rate是丢弃比例，training是个bool，我额外加了括号方便理解
+    '''
+    dropout = tf.layers.dropout(
+        inputs=dense, rate=0.4, training=(mode == tf.estimator.ModeKeys.TRAIN))
+    # Logits layer
+    # Input Tensor Shape: [batch_size, 1024]
+    # Output Tensor Shape: [batch_size, 30]
+    logits = tf.layers.dense(inputs=dropout, units=30)
+    '''
+       定义一个字典，键值对为 类型：可能性
+    '''
+    predictions = {
+        # Generate predictions (for PREDICT and EVAL mode)
+        "classes": tf.argmax(input=logits, axis=1),
+        # Add `softmax_tensor` to the graph. It is used for PREDICT and by the
+        # `logging_hook`.
+        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
+    }
+    
+    '''
+        训练/测试模式下不同动作
+    '''
+    '''
+        这个类被用来训练和评估tensorflow模型
+        这个对象封装了EstimatorSpe类，根据给定的输入和一些其他的参数，去训练或者评估模型。
+        这个类所有的输出都会被写到”model_dir”参数所对应的目录，如果这个参数为null，则会被写入到一个临时文件夹。
+        Estimator类中config参数需传递一个RunConfig对象实例，这个对象用来控制程序的运行环境。他会被传入到Model实例中。
+    '''
+    if mode == tf.estimator.ModeKeys.PREDICT:
+        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
+
+    # Calculate Loss (for both TRAIN and EVAL modes)
+    '''
+        交叉损失熵？总之这是一个评估指标嗯。
+    '''
+    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
+
+    # Configure the Training Op (for TRAIN mode)
+    '''
+        随机梯度下降法优化网络，学习率0.001（我试图改这个参数证明“我能掌握这个”，但是调过后模型崩了......）
+    '''
+    if mode == tf.estimator.ModeKeys.TRAIN:
+        optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
+        train_op = optimizer.minimize(
+            loss=loss,
+            global_step=tf.train.get_global_step())
+        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
+    '''
+        定义评估准确度的字典
+    '''
+    # Add evaluation metrics (for EVAL mode)
+    eval_metric_ops = {
+        "accuracy": tf.metrics.accuracy(
+            labels=labels, predictions=predictions["classes"])}
+    return tf.estimator.EstimatorSpec(
+        mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
+
+
+def main(unused_argv):
+        # Load training and eval data
+        # mnist = tf.contrib.learn.datasets.load_dataset("mnist")
+        #   mnist = tf.contrib.learn.datasets.load_dataset("mnist")
+        #   from tensorflow.examples.tutorials.mnist import input_data
+        #   mnist = input_data.read_data_sets("Controller/MNIST_data/", one_hot=True) #MNIST数据输入
+
+        #   train_data = mnist.train.images  # Returns np.array
+        #   train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
+        #   eval_data = mnist.test.images  # Returns np.array
+        #   eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)
+    '''加载训练集以及测试集'''
+    train_data = np.array(pickle.load(open('Model/train_data.plk', 'rb')) )
+    train_labels = np.array(pickle.load(open('Model/train_labels.plk', 'rb')) )
+    eval_data = np.array(pickle.load(open('Model/eval_data.plk', 'rb')) )
+    eval_labels = np.array(pickle.load(open('Model/eval_labels.plk', 'rb')) )
+
+    # with tf.Session() as sess:
+    #     train_data = tf.convert_to_tensor(train_data_np)
+    #     eval_data = tf.convert_to_tensor(eval_data_np)
+
+    # print(train_data)
+    # print(train_labels)
+    # print(eval_data)
+    # print(eval_labels)cls
+    # Create the Estimator
+    '''
+        定义归类器，训练模型，这个mnist和tf官网那个没什么关系啊......
+    '''
+    mnist_classifier = tf.estimator.Estimator(
+        model_fn=cnn_model_fn, model_dir="mnist_convnet_model")
+    
+    # Set up logging for predictions
+    # Log the values in the "Softmax" tensor with label "probabilities"
+    tensors_to_log = {"probabilities": "softmax_tensor"}
+    '''每训练50次的时候输出预测的概率值'''
+    logging_hook = tf.train.LoggingTensorHook(
+        tensors=tensors_to_log, every_n_iter=50)
+
+    # Train the model
+    '''
+        定义训练输入
+    '''
+    train_input_fn = tf.estimator.inputs.numpy_input_fn(
+        x={"x": train_data},
+        y=train_labels,
+        batch_size=20,
+        num_epochs=None,
+        shuffle=True)
+    '''训练500步后停止，利用hook参数触发日志函数'''
+    mnist_classifier.train(
+        input_fn=train_input_fn,
+        steps=500,
+        hooks=[logging_hook])
+
+    # Evaluate the model and print results
+    '''评估模型，shuffle=False说明循环遍历数据'''
+    eval_input_fn = tf.estimator.inputs.numpy_input_fn(
+        x={"x": eval_data},
+        y=eval_labels,
+        num_epochs=1,
+        shuffle=False)
+    eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
+    print(eval_results)
+
+
+if __name__ == "__main__":
+    tf.app.run()
diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/README.md" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/README.md"
new file mode 100644
index 0000000..c48ed95
--- /dev/null
+++ "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/README.md"	
@@ -0,0 +1,4 @@
+# 使用配置说明
+我们的运行环境：python3.5+tensorflow+numpy+pickle
+终端运行 python3 ./Here is the code/myCNN.py
+迭代次数2000、准确率0.37、损失率2.58（似乎还有优化之处）
diff --git "a/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/\346\227\240\344\272\272\350\275\246\347\254\254\344\270\200\346\254\241\345\237\271\350\256\255\346\212\200\346\234\257\346\212\245\345\221\212.doc" "b/\347\254\254\344\270\200\346\254\241\345\256\236\351\252\214 \345\260\271\345\277\227\345\255\230 \346\256\267\345\272\267\345\256\201 \346\235\216\345\230\211\347\202\234/\346\227\240\344\272\272\350\275\246\347\254\254\344\270\200\346\254\241\345\237\271\350\256\255\346\212\200\346\234\257\346\212\245\345\221\212.doc"
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diff --git "a/\347\254\254\344\272\214\346\254\241\345\256\236\351\252\214/readme.md" "b/\347\254\254\344\272\214\346\254\241\345\256\236\351\252\214/readme.md"
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+++ "b/\347\254\254\344\272\214\346\254\241\345\256\236\351\252\214/readme.md"
@@ -0,0 +1 @@
+采用python编程，通过小车自带的topic向小车发布命令，拥有行走，转向，加速，减速等功能的键盘控制小车的代码
diff --git "a/\347\254\254\344\272\214\346\254\241\345\256\236\351\252\214/robot_keyboard_teleop.py" "b/\347\254\254\344\272\214\346\254\241\345\256\236\351\252\214/robot_keyboard_teleop.py"
new file mode 100644
index 0000000..1652f81
--- /dev/null
+++ "b/\347\254\254\344\272\214\346\254\241\345\256\236\351\252\214/robot_keyboard_teleop.py"
@@ -0,0 +1,147 @@
+#!/usr/bin/env python
+import rospy
+
+from geometry_msgs.msg import Twist
+
+import sys, select, termios, tty
+
+msg = """
+Control The Robot!
+---------------------------
+Moving around:
+   u    i    o
+   j    k    l
+   m    ,    .
+
+q/z : increase/decrease max speeds by 10%
+w/x : increase/decrease only linear speed by 10%
+e/c : increase/decrease only angular speed by 10%
+space key, k : force stop
+anything else : stop smoothly
+
+CTRL-C to quit
+"""
+#在终端中形成文字提示，提升用户体验
+
+moveBindings = {
+        'i':(1,0),
+        'o':(1,-1),
+        'j':(0,1),
+        'l':(0,-1),
+        'u':(1,1),
+        ',':(-1,0),
+        '.':(-1,1),
+        'm':(-1,-1),
+           }
+#定义一个按键对应小车移动方向的字典（用元组表示不可修改），元组的第一个元素表示线速度，第二个表示角速度
+speedBindings={
+        'q':(1.1,1.1),
+        'z':(.9,.9),
+        'w':(1.1,1),
+        'x':(.9,1),
+        'e':(1,1.1),
+        'c':(1,.9),
+          }
+#同上，定义一个小车加速减速的字典
+def getKey():
+    tty.setraw(sys.stdin.fileno())
+    rlist, _, _ = select.select([sys.stdin], [], [], 0.1)
+    if rlist:
+        key = sys.stdin.read(1)
+    else:
+        key = ''
+
+    termios.tcsetattr(sys.stdin, termios.TCSADRAIN, settings)
+    return key
+#定义一个与键盘相关联的函数
+speed = .2
+turn = 1
+#初始速度
+def vels(speed,turn):
+    return "currently:\tspeed %s\tturn %s " % (speed,turn)
+#定义函数打印出现在的线速度与转速
+
+if __name__=="__main__":
+    settings = termios.tcgetattr(sys.stdin)
+    
+    rospy.init_node('robot_teleop')
+    pub = rospy.Publisher('/husky_velocity_controller/cmd_vel', Twist, queue_size=5)
+#将消息采用twist的方式传到'/husky_velocity_controller/cmd_vel'此topic上
+
+    x = 0
+    th = 0
+    status = 0
+    count = 0
+    acc = 0.1
+    target_speed = 0
+    target_turn = 0
+    control_speed = 0
+    control_turn = 0
+    try:
+        print msg
+        print vels(speed,turn)
+        while(1):
+            key = getKey()
+            if key in moveBindings.keys():
+                x = moveBindings[key][0]
+                th = moveBindings[key][1]
+                count = 0
+            elif key in speedBindings.keys():
+                speed = speed * speedBindings[key][0]
+                turn = turn * speedBindings[key][1]
+                count = 0
+
+                print vels(speed,turn)
+                if (status == 14):
+                    print msg
+                status = (status + 1) % 15
+            elif key == ' ' or key == 'k' :
+                x = 0
+                th = 0
+                control_speed = 0
+                control_turn = 0
+            else:
+                count = count + 1
+                if count > 4:
+                    x = 0
+                    th = 0
+                if (key == '\x03'):
+                    break
+#定义初始值，将按键与键盘与加速，转向等联系起来
+            target_speed = speed * x
+            target_turn = turn * th
+
+            if target_speed > control_speed:
+                control_speed = min( target_speed, control_speed + 0.02 )
+            elif target_speed < control_speed:
+                control_speed = max( target_speed, control_speed - 0.02 )
+            else:
+                control_speed = target_speed
+
+            if target_turn > control_turn:
+                control_turn = min( target_turn, control_turn + 0.1 )
+            elif target_turn < control_turn:
+                control_turn = max( target_turn, control_turn - 0.1 )
+            else:
+                control_turn = target_turn
+
+            twist = Twist()
+            twist.linear.x = control_speed; twist.linear.y = 0; twist.linear.z = 0
+            twist.angular.x = 0; twist.angular.y = 0; twist.angular.z = control_turn
+            pub.publish(twist)
+
+            #print("loop: {0}".format(count))
+            #print("target: vx: {0}, wz: {1}".format(target_speed, target_turn))
+            #print("publihsed: vx: {0}, wz: {1}".format(twist.linear.x, twist.angular.z))
+#在键盘上不进行操作后速度逐渐下降至停止
+
+    except:
+        print e
+
+    finally:
+        twist = Twist()
+        twist.linear.x = 0; twist.linear.y = 0; twist.linear.z = 0
+        twist.angular.x = 0; twist.angular.y = 0; twist.angular.z = 0
+        pub.publish(twist)
+
+    termios.tcsetattr(sys.stdin, termios.TCSADRAIN, settings)