shuffle_sample_CNN.ipynb

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   "source": [
    "##### 2层卷积+2层分类，50k数据集训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* train_data : shuffle 50k\n",
    "* vali_data : 5k\n",
    "* batch_size : 1000\n",
    "* Optimizer : SGD\n",
    "* learning_rate : 1e-5\n",
    "* accuracy_threshold : 0.98\n",
    "* step_limit : 64K"
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   "execution_count": 1,
   "metadata": {
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   ],
   "source": [
    "# start tensorflow interactiveSession           F1 = 0.711                 \n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import random\n",
    "import h5py\n",
    "\n",
    "filename = 'E:/Alibaba German AI Challenge/data_process/data_shuffle.npy'\n",
    "vali_filename = 'E:/Alibaba German AI Challenge/data_process/sample_of_training.npy'\n",
    "data = np.load(filename)\n",
    "vali = np.load(vali_filename)\n",
    "print('The shape of data is ',data.shape)\n",
    "print('The shape of vali is ',vali.shape)\n",
    "\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "#####################################################     Net Define     ##################################################### \n",
    "\n",
    "# weight initialization\n",
    "def weight_variable(shape):\n",
    "    initial = tf.truncated_normal(shape, stddev=0.1)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "def bias_variable(shape):\n",
    "    initial = tf.constant(0.1, shape = shape)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "# convolution\n",
    "def conv2d(x, W):\n",
    "    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n",
    "# pooling\n",
    "def max_pool_2x2(x):\n",
    "    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "\n",
    "# Create the model\n",
    "# placeholder\n",
    "x = tf.placeholder(\"float\", [None, 18432])\n",
    "y_ = tf.placeholder(\"float\", [None, 17])\n",
    "keep_prob = tf.placeholder(\"float\")\n",
    "x_image = tf.reshape(x, [-1, 32, 32, 18])\n",
    "\n",
    "# first convolutinal layer\n",
    "w_conv1 = weight_variable([5, 5, 18, 32])\n",
    "b_conv1 = bias_variable([32])\n",
    "\n",
    "h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1)\n",
    "h_pool1 = max_pool_2x2(h_conv1)\n",
    "\n",
    "# second convolutional layer\n",
    "w_conv2 = weight_variable([5, 5, 32, 64])\n",
    "b_conv2 = bias_variable([64])\n",
    "\n",
    "h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2)\n",
    "h_pool2 = max_pool_2x2(h_conv2)\n",
    "\n",
    "# flatten\n",
    "h_pool2_flat = tf.reshape(h_pool2, [-1, 8*8*64])\n",
    "\n",
    "# densely connected layer\n",
    "w_fc1 = weight_variable([8*8*64, 1024])\n",
    "b_fc1 = bias_variable([1024])\n",
    "\n",
    "h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)\n",
    "\n",
    "# dropout\n",
    "h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n",
    "\n",
    "# readout layer\n",
    "w_fc2 = weight_variable([1024, 17])\n",
    "b_fc2 = bias_variable([17])\n",
    "\n",
    "y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, w_fc2) + b_fc2)\n",
    "\n",
    "# train and evaluate the model\n",
    "#交叉熵作为损失函数\n",
    "delta = 1e-7\n",
    "cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv+delta))\n",
    "train_step = tf.train.GradientDescentOptimizer(1e-5).minimize(cross_entropy)\n",
    "correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "\n",
    "#####################################################       Train     ##################################################### \n",
    "\n",
    "\n",
    "##随机抽取一部分作为一个mini-batch\n",
    "def get_batch(data, batch_size):\n",
    "    sample = random.sample(list(data),batch_size)\n",
    "    sample = np.array(sample)\n",
    "    train_x = sample[:,:-17]\n",
    "    train_y = sample[:,-17:]\n",
    "    \n",
    "    return train_x, train_y\n",
    "\n",
    "Loss = []\n",
    "Eval = []\n",
    "\n",
    "batch_size = 1000\n",
    "for i in range(64000):\n",
    "    batch_x,batch_y = get_batch(data,batch_size)\n",
    "    train_step.run(feed_dict={x:batch_x, y_:batch_y, keep_prob:0.5})\n",
    "    temp_loss = cross_entropy.eval(feed_dict={x:batch_x, y_:batch_y, keep_prob:1.0})\n",
    "    vali_accuracy = accuracy.eval(feed_dict={x:vali[:,:-17], y_:vali[:,-17:], keep_prob:1.0})\n",
    "    Loss.append(temp_loss)\n",
    "    Eval.append(vali_accuracy)\n",
    "    if i%100 == 0:\n",
    "        train_accuracy = accuracy.eval(feed_dict={x:batch_x, y_:batch_y, keep_prob:1.0})\n",
    "        print (\"step %d, train accuracy %g, train loss %g\" %(i, train_accuracy, temp_loss))\n",
    "        if train_accuracy > 0.98:\n",
    "            break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pw8ltk0Sb5ATO696Wgq4NH4Rl5lv1KL2aeaWD/eubw1kQ9CyziEhLpp5zLfre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\nGYWziIhIM/P/AeNbZyt/pnDgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15e41c35d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "xx = list(range(len(Eval)))\n",
    "\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot(xx,Eval)\n",
    "plt.title('Eval')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A2TtY7yrgqu3UTwVGtUZbWsJolyTlgXeo480j96VrNpe2IZIktQLDHZiyYBUAP580p8Qt\nkSSp5Qx3YMYbNaVugiRJrcZwBzp5+1lJUo4Y7kBZJ38GSVJ+mGrAyP17l7oJkiS1GsMd6FzmeXlJ\nUn4Y7sCxQ/vtfCVJktoJwx2o7NUFgM+fcXCJWyJJUssZ7rx5tXznMn8OSVL7Z5pReJ57BDR5b3lJ\nUg4Y7pmUYHmNt5+VJLV/hnuRW6cuLnUTJElqMcNdkqScMdwlScoZw12SpJwx3CVJyhnDXZKknDHc\nJUnKGcNdkqScMdwlScoZw12SpJwx3CVJyhnDXZKknDHcJUnKGcN9G7OXrSt1EyRJahHDfRvfvXdG\nqZsgSVKLGO7beGJedambIElSixjukiTljOEuSVLOGO6SJOWM4S5JUs4Y7pmffOjoUjdBkqRWYbhn\nhlf2KHUTJElqFYZ7pl/3ilI3QZKkVmG4Zwb16VbqJkiS1CoM90yPLuXN5ZufXlTClkiS1DKG+3Z8\n866XS90ESZL22B6He0QMjYhHI2JmRMyIiC9l9f0j4uGImJu99yva5oqImBcRsyNibFH9CRExPVt2\ndUREy7olSVLH1ZIj9wbgX1NKI4GTgMsiYiQwAZicUhoBTM4+ky0bDxwJnAtcGxFl2b6uAz4LjMhe\n57agXZIkdWh7HO4ppaUppeey8jpgFjAYGAdMzFabCFyQlccBt6SUalNKC4B5wJiIGAT0Tik9nVJK\nwG+LtimZ1RvqSt0ESZL2SKuMuUfEMOA44BlgYEppabZoGTAwKw8GXi/abHFWNzgrb1tfUv9624ul\nboIkSXukxeEeET2BO4DLU0o1xcuyI/HU0u8o+q7PRcTUiJhaVVXVWrtt9ou/P7a5/MgrK1p9/5Ik\ntYUWhXtEVFAI9t+nlP6UVS/PTrWTvW9JySXA0KLNh2R1S7LytvVvkVK6PqU0OqU0urKysiVN364L\njiv5CQNJklqsJVfLB/AbYFZK6WdFi+4BLsrKFwF3F9WPj4guETGcwoVzU7JT+DURcVK2zwuLtimp\nS26eytpN9aVuhiRJu6UlR+6nAp8AzoqIF7LX+cCPgHdHxFzgnOwzKaUZwK3ATOAB4LKUUmO2r0uB\nX1O4yO5V4P4WtKvVPDhjOaf8cHKpmyFJ0m6JwrB4+zN69Og0derUVt/vHdMWv+ViuiP37819Xzyt\n1b9LkqRdFRHTUkqjd2Vd71C3jQ+dMOQtdTPeqOGlxWtK0BpJknaf4b6L3v/LJ0rdBEmSdonhvh1/\n+coZ260fNuE+LrxhCo86TU6StBcr3/kqHc+wAT12uOzxOVU8Pqcwx/6vXzuTof27t1WzJEnaJR65\nt8BpP3mUYRPu4/VVG0vdFEmSmhnuOzDn++ft8rqn/eRRPjPxWYZNuI+6hiZqNtezub6Rms3OkZck\ntT2nwv0NKSWGX/HnPdp2/z5deWPtZr527mFcdPIwjvz2g3zrfSP55CnDiACfaitJ2h27MxXOcN+J\n6vW1nPD9SS3ax1fecyj/8dCcreo+ecowvjL2MHp28bIHSdLOGe6t7On51Yy//um3Zd83fHI0xx/Q\njw9e9ySfPnU4ryyrYf++3bj0jEPelu+TJLVPhvvb4PVVG7n3paX8+IFX2uT7/u3cw/n8GQe3yXdJ\nkvZ+3qHubTC0f3c+f8bB3HXZqW3yfT9+4BXmLF8HwLRFq6lraKKuoekt6018ciHDJtzHa9VbX7Gf\nUuL47z3MLVNea5P2SpL2Hh6574GazfU8/9oa/v1/ZjC/akObfvfXzz+cJ1+t5srzj2DEwF4Mm3Af\nAMMH9ODRopvvNDQ2cciV99MpYP4P39umbZQktT5Py7ehuoYmmlLimkfn8fKStTw6u6rUTeI3F43m\nV4+9yrMLVwMw/wfn05QS5WWFEzVTFqyie+cyRg3us8v73FTXyH89Mpcvnj2CrhVlb0u7JUk7ZriX\n2JqNdXz9zuk8M38V1RvqSt2crfzkQ0fztTteAuB/vvBOltdspjElLrl5Gi9+6z307lbOsprN3DZ1\nMf981iHNU/b+c9Jcfj5pDocO7Ml9XzyNijJHdCSpLRnue5G6hiZ+9vAcjhnSh8///rlSN2e3nHlY\nJUcN6cvBlT345l0vU7O5AYB/PusQPnLCUIb27wbAE/OqqW9somtFGScfvA8AazfW89xrqznz8H35\n5l0v06NLORPOO7x53yklUoJOnZzvL0m7wnBvB078wSTOP2oQNz6xsNRNaVVXf+w43n/M/nzs+qd5\nan41Hx09hFunLgbg7MP35fsfGEXfbp257rFXuXryXGZ+dyyrNtTRv0dnund2zr+kt9/905cypF93\njhqy60OTewPDvZ1Zs7GOuSvW07msE+Ou6ZiPlh0+oAc3feodfO/eWXxszFDOOnxfIoIHXl7GP/1u\nGndeegqvrdrI9MVr+cb7RjZv19DYRFmnaB4+eHbhKj7yq6e44ZOjuenJRVz/iRPeco3Ai6+v4aDK\nHvTqWtGmfZS0d9hyIfLCH7Wvi413J9w9VNoL9O3emXcM6w9s/S/bLVNe41ePvcrCbJrb6YdWNj+R\nLm8WrNzAu376FwAmzVr+luUfuPbJ5vL62gZOPKg/444ZzCFX3k9lry789MNH88kbn+VDxw8B4NM3\nFf7we2xOFZfcPI1L3nUQV5x3BPWNTYy75gnGDO/PrZecvNV3vLxkLUfu33u7twZevHojG2obOWy/\nXq3VZUl623jk3g4tXr2RGW/UcNbh+3LHtMX8cerrrKipZcmaTaVuWrvzgw8cxWNzVvD0/FWs3VR4\n0M/NF4+hoqwTJw7vT0SwdO0mTv7hI8DWf3xtrm8kArqU792zB+atWMf465/hz198J/v27lrq5kgl\n55G79kpD+nVnSL/Cc+THjzmA8WMOAApHtBvrGiDBmB9MLmUT242v3zn9LXWf+M2U5vJt/3QyH/nV\nU82fb/jfBSxYuYGjhvTha7e/1Fz/u4tP5J0jBvDKshqq1tUysHdXPnjtkxw7tC9zV6zjma+fA8Dy\nms28tmpj85ma5TWbaWxK7N+3207bumpDHas31nFwZc9d6tvc5euo7NWFG59YyMr1tTw4czmfOOnA\nXdpWUvvmkXtO1Tc28ZfZVYzcvzfV62s5ekjf5mWNTYkFK9fTu2sFG+sa2a9PVw7/5gMA9OxSzvra\nhlI1u0O78ZPv4JRD9uG16o3069GZVRvqWLJ6E+8Y3p+eXco59rsPsWZj4ezC0UP68PvPnNh83cCG\n2gYSMHvZOvbt1YWh/bs3H51s8b0LRu12uM9Zvo5DB745FPHAy0s5akhfBu/CHyMd2Y1PLODUQwZs\n9dtp79ERjtwNdwGFu+6tqKnlkH17klJixhs1LFi5gYMre3LgPt25fdpiTj54H3771EKmL17L4tWb\n9ro5/B3RxE+PYc3GOr50yws7XffdIwfy1bGHsWTNJio6deL4A/vy0wdnc/k5h9KnWwUb6xroUl7G\nZyY+y6Ozq/jlPxzHF/77eX718ePp270zJxzYjxFX3k/Xik58+tThvL56E6cevA9nHb4vs5at412H\nVu60DY1NqXna5Ma6Br519wyuPP8I+vXo3Bo/x15j2IT7qCgL5l51fqmbou0w3PdihnvpNTWlHc5T\nX7Z2Mx+49gm+fM6hbKpv5Nv3zGhedvwBfXnutTVt1Uy1kUe/cgbXPz6f2vpGvv7eI+jZpZznXlvN\nzU8tYuyR+1GzuZ4n51XzwIxlzPzuWD5147M8s2AVF558IJefcyiTZy1n9rJ1HHtAX9571CCue+xV\n/mHMAfTt/mbw12yu56lXqxl75H4sr9lMXUMTQ/t3p7ahker1dQzo2YXGpkS3zq13HcTqDXXc9cIS\nPnnKsO1ebLk97TU8Oor2+s/HcFe7sah6Az95cDZXnn8E/Xt0bh4eOGZIH048aB9WrqvlfccMar76\nHWDM8P5MWbCqVE1WO3DtPx7P7dMWc9mZh9CnWwU/nzSHsUfux7K1mzj90EoG9e5Gn+67NhXyMxOf\nZdKsFdx92akcM/TN4a0NtQ10rSijbDt/4G4Jj/u/dBpHDOq9W21/en41Rw/ps8P7PixYuYH+PTpz\nzL8/xFfHHsZlZ27/8dC3TX2dr97+Ei995z30dtrnVgz3vZjhLijcCW/lhloqe3Vh5bpaKso6MbR/\nd1JKrNlYz6RZy/lq0YVv0q7qFBARNDZt/f/I4QN6cPSQPtz9whsAPHvlOazaUMem+kb+PH0pJx+8\nD5+68dnm9ad/5z38660vMrR/dyacdzivLF3Hmk11vLZqI9c/Pp9/efehnDdqEBVlwUMzl3PJzdM4\nbGAvZi9fxwH9u/PQl09vvlfD+toGRn37QU4c3p9nFqxi315dePjL7+LkH02msSkx+/vnNX/v2J8/\nzuzl67jnC6fSo0v5Ll+IWWzl+lpmvlHDaSMGNJ+1WF6zmafnVzPu2MG7tI9JM5fz+Nwqvjtu1G5/\nf1NTIrJ/Dq2ptcK9en0t62sbOHCfHq3RrJ0y3KVdsHZTPV0rOtGlvIyGxiaaUmE44cXFa/i7Y/an\nal0tn/ntVCp7dmbSrBVccvpBPDBjGYuqN3LcAX153qEFtZFeXcopLwtWZxdU7kzxMyS29X8+cgyL\nqjdwyiEDOGK/3vzs4dlMfGoRAC98693MXraOhqbE8Qf0433/9VdezZ58Oe+q81hf28Cx330YgC+f\ncyhfOmcEAHc+v5gf/PkV/u7o/Tl0YM/mGTw/ffAVrnn0VQAe/+qZ7NOzMxFQ3qkTncu3/3yKhsYm\nHpixjKH9ujPumid416GVTPz0GADuen4Jl//xBV781nu2e+ZlS55t+8dAQ2MTT89fxehh/ehaUdZq\n4X7YN+6ntqGpzc4AGO5SG9ny309TgtUb61i2djOH7deLuoYmVm2oY8W6WjbVNdKtcycO6N+DqnWF\nswwAE59cyIuL13D6iEqu/+t8/uMjx/Cde2awYGXbPkZY2lOnjRjAX+eu3O6ykw/ah6fmV+9w2387\n93D6da+gekMdNz25kHOOGMgxQ/ow4U9vnZ4K8PkzDubxOVXMeKOGiZ8ew0U3TGm+3fUWl9/yPHe9\n8Abzf3A+nToFK2o2M3fFeq68c3rzzcC+f8EovnHXywC88r1z6VpRxi8mzeGYoX0587B9ueF/F/Dd\ne2cy96rz3vKArLWb6pm9bB0nHNiPX0yaw389Mg9ou9P7hrvUjv1l9gpOPWTAdp+8t2TNJgb37UZd\nQxNrNtZR29BE3+4VdO9czuLVG+nWuYxJM1fw5+lL2VzfyHUfP4GV62s5qLIHh32jcD3DUYP78P5j\n9uenD86mrrGprbsn7VW+OvYwfvrg7B0u7xRw+TmH8rOH5+xwnQP6d+eyMw/m3+6YzqA+XVm6djO3\nXnIyY4b3b9W2Gu6SdltKiYamxPQla+nZpXy7c7Q31jXQtbyMhqbE8prNVPbqwqoNdXSK4On51Zw2\nYgCvLFtH14oyOpd14pePzuWZBau48vwjOHZoX558tZpv3zODkw7qz9Pz37wo8jPvHM6v/3dBW3ZX\nahOteVRvuEvKlcamxL0vvcHaTfWccei+7Nu7C00psbymll5dy9mnR2e+cttL/P07hrKoegOzlq7j\niXkrKesUDO7XjYdnFp5X8OlTh7Nucz23TVu83e/pWtGJzfWezVDrMdx3k+Eu6e1U29DItEWrOXH4\nPjQ0NdHYlJi7fD3HDO3LY3Oq6NutgmOG9iWlxOurNpFIDOzdlTUb69mvT+Ee/q9Wradf985071zG\ntX95lTXZ7YMnzVrePFZ9yekH8YWzDuGaR1+lal0tazfVMWnWiuZ2HNC/O6+t2sjh+/XilWXrSvJb\naM8Z7rvJcJfUEaWUuPP5JZxx2L70386d/Rav3kjvbhX06FzOQzOWcebh+9K1oozGpkTVulr269OV\nOcvX8ezCVXzv3pk8dPm7GNyvG+s21zPjjRq+fud0FlVv5BvvPYJOEdz05EJ+9fETWLGucLHoE/Oq\neXLeSsaO2o9Lbp4GwJB+3Vi8uvDgqoG9u7Bf767Mr9rAl84Zwffvm9Wmv8/exnDfTYa7JO0dUkrc\n//Iy3j1y4HYvBC1Wvb6W8rJO9OlWwdK1m3hyXjUfPH4wKdF8x8uazfXU1jfRu1t581MX73p+Cb98\ndB53XXaFj2A/AAAFyElEQVQq9Q1N9OvRmTumLaausYkPnzCEN9ZsYtKsFbzv6EH06VbBrKU1HFTZ\nk6p1tZzzs8foWtGJ2y45hdunvU59U+KgAT04qLIHA3p24f2/fAKADx4/mEXVG5m2aPUOp7v2z577\nsCt6dy3npe+M3Z2f8m8y3CVJakUbahso6xTNNxQqBR/5KklSK+rRpX3F5d8+fyJJktodw12SpJwx\n3CVJypm9Jtwj4tyImB0R8yJiQqnbI0lSe7VXhHtElAHXAOcBI4GPRcTI0rZKkqT2aa8Id2AMMC+l\nND+lVAfcAowrcZskSWqX9pZwHwy8XvR5cVYnSZJ2094S7rskIj4XEVMjYmpVVVWpmyNJ0l5pbwn3\nJcDQos9DsrqtpJSuTymNTimNrqysbLPGSZLUnuwt4f4sMCIihkdEZ2A8cE+J2yRJUru019xbPiLO\nB34BlAE3pJSu2sn6VcCiVmzCAGBlK+6vvenI/e/IfYeO3X/73nG1x/4fmFLapdPWe024l1pETN3V\nG/LnUUfuf0fuO3Ts/tv3jtl3yH//95bT8pIkqZUY7pIk5Yzh/qbrS92AEuvI/e/IfYeO3X/73nHl\nuv+OuUuSlDMeuUuSlDOGO/l5Il1E3BARKyLi5aK6/hHxcETMzd77FS27Iuvz7IgYW1R/QkRMz5Zd\nHRGR1XeJiD9m9c9ExLC27N/fEhFDI+LRiJgZETMi4ktZfe77HxFdI2JKRLyY9f3fs/rc971YRJRF\nxPMRcW/2uUP0PyIWZm1+ISKmZnUdou8AEdE3Im6PiFciYlZEnNyR+r9DKaUO/aIwr/5V4CCgM/Ai\nMLLU7drDvpwOHA+8XFT3E2BCVp4A/Dgrj8z62gUYnv0GZdmyKcBJQAD3A+dl9ZcCv8rK44E/lrrP\nRf0cBByflXsBc7I+5r7/WTt7ZuUK4Jms/bnv+za/w78A/w3c28H+3V8IDNimrkP0PWvTROAzWbkz\n0Lcj9X+Hv0upG1DqF3Ay8GDR5yuAK0rdrhb0Zxhbh/tsYFBWHgTM3l4/gQez32IQ8EpR/ceA/1u8\nTlYup3ADiCh1n3fwO9wNvLuj9R/oDjwHnNiR+k7hltWTgbN4M9w7RP/Zfrh3lL73ARZs256O0v+/\n9fK0fP6fSDcwpbQ0Ky8DBmblHfV7cFbetn6rbVJKDcBaYJ+3p9l7LjttdhyFI9gO0f/slPQLwArg\n4ZRSh+l75hfA14CmorqO0v8ETIqIaRHxuayuo/R9OFAF3JgNyfw6InrQcfq/Q4Z7B5IKf3rmenpE\nRPQE7gAuTynVFC/Lc/9TSo0ppWMpHMGOiYhR2yzPbd8j4n3AipTStB2tk+f+A+/M/tmfB1wWEacX\nL8x538spDEVel1I6DthA4TR8s5z3f4cM9118Il07tjwiBgFk7yuy+h31e0lW3rZ+q20iopzCKbHq\nt63luykiKigE++9TSn/KqjtM/wFSSmuAR4Fz6Th9PxV4f0QsBG4BzoqI39FB+p9SWpK9rwDuBMbQ\nQfpO4Qh7cXamCuB2CmHfUfq/Q4Z7/p9Idw9wUVa+iMJY9Jb68dmVoMOBEcCU7FRWTUSclF0teuE2\n22zZ14eBR7K/iksua+tvgFkppZ8VLcp9/yOiMiL6ZuVuFK41eIUO0HeAlNIVKaUhKaVhFP77fSSl\n9HE6QP8jokdE9NpSBt4DvEwH6DtASmkZ8HpEHJZVnQ3MpIP0/28q9aD/3vACzqdwdfWrwJWlbk8L\n+vEHYClQT+Ev2ospjA1NBuYCk4D+RetfmfV5NtmVoVn9aAr/g3gV+CVv3uyoK3AbMI/ClaUHlbrP\nRW1+J4VTby8BL2Sv8ztC/4Gjgeezvr8MfCurz33ft/NbnMGbF9Tlvv8UZvm8mL1mbPn/V0foe1G7\njwWmZv/+3wX060j939HLO9RJkpQznpaXJClnDHdJknLGcJckKWcMd0mScsZwlyQpZwx3SZJyxnCX\nJClnDHdJknLm/wOKjIvUKq2nggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15c31ac06d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "xx = list(range(len(Loss)))\n",
    "\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot(xx,Loss)\n",
    "plt.title('Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.28      0.75      0.40        71\n",
      "          1       0.67      0.56      0.61       342\n",
      "          2       0.82      0.48      0.61       474\n",
      "          3       0.40      0.75      0.53       119\n",
      "          4       0.39      0.64      0.48       217\n",
      "          5       0.67      0.54      0.60       487\n",
      "          6       0.46      0.93      0.62        40\n",
      "          7       0.88      0.51      0.65       586\n",
      "          8       0.49      0.64      0.55       188\n",
      "          9       0.34      0.49      0.40       158\n",
      "         10       0.95      0.85      0.90       622\n",
      "         11       0.27      0.82      0.41       125\n",
      "         12       0.61      0.75      0.67       134\n",
      "         13       0.85      0.50      0.63       585\n",
      "         14       0.38      1.00      0.55        26\n",
      "         15       0.60      0.96      0.73        91\n",
      "         16       0.98      0.97      0.98       735\n",
      "\n",
      "avg / total       0.75      0.67      0.68      5000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "pred = y_conv.eval(feed_dict={x:vali[:,:-17], y_:vali[:,-17:], keep_prob:1.0})\n",
    "train_val_y = np.argmax(vali[:,-17:],axis = 1)\n",
    "pred_y = np.argmax(pred, axis = 1)\n",
    "print (classification_report(train_val_y, pred_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0   1   2   3   4   5   6   7   8   9   10  11  12  13  14  15  16\n",
      "0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   1\n",
      "1   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "2   0   0   0   0   0   0   0   0   0   0   0   0   0   1   0   0   0\n",
      "3   0   0   0   0   0   0   0   0   0   0   1   0   0   0   0   0   0\n",
      "4   0   0   0   0   0   0   0   1   0   0   0   0   0   0   0   0   0\n"
     ]
    }
   ],
   "source": [
    "import h5py\n",
    "filename = 'E:/Alibaba German AI Challenge/origin_DATA/round1_test_a_20181109.h5'\n",
    "f = h5py.File(filename,'r')\n",
    "test_s1 = f['sen1']\n",
    "test_s2 = f['sen2']\n",
    "\n",
    "test = []\n",
    "for i in range(0,test_s1.shape[0]):\n",
    "    temp1 = test_s1[i].flatten()\n",
    "    temp2 = test_s2[i].flatten()\n",
    "    temp = np.hstack((temp1,temp2))\n",
    "    test.append(temp)\n",
    "test = np.array(test)\n",
    "\n",
    "test_y = np.zeros((test.shape[0],17))\n",
    "\n",
    "pred = tf.argmax(y_conv, 1)\n",
    "\n",
    "test_x_0 = test[0:1500]\n",
    "test_y_0 = test_y[0:1500]\n",
    "P_0 = pred.eval(feed_dict={x:test_x_0, y_:test_y_0, keep_prob:1.0})\n",
    "\n",
    "test_x_1 = test[1500:3000]\n",
    "test_y_1 = test_y[1500:3000]\n",
    "P_1 = pred.eval(feed_dict={x:test_x_1, y_:test_y_1, keep_prob:1.0})\n",
    "\n",
    "test_x_2 = test[3000:4500]\n",
    "test_y_2 = test_y[3000:4500]\n",
    "P_2 = pred.eval(feed_dict={x:test_x_2, y_:test_y_2, keep_prob:1.0})\n",
    "\n",
    "test_x_3 = test[4500:]\n",
    "test_y_3 = test_y[4500:]\n",
    "P_3 = pred.eval(feed_dict={x:test_x_3, y_:test_y_3, keep_prob:1.0})\n",
    "\n",
    "P = np.hstack([P_0,P_1,P_2,P_3])\n",
    "\n",
    "one_hot=tf.one_hot(P,17)\n",
    "Pred_one_hot = sess.run(one_hot)\n",
    "Pred_one_hot = Pred_one_hot.astype(np.int32)\n",
    "out = pd.DataFrame(Pred_one_hot, columns = list(range(17)))\n",
    "print(out.head())\n",
    "\n",
    "out.to_csv('sixth_64k_batch_shuffle_balance_50k_train_SGD.csv', index = False, header = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* train_data : shuffle 50k\n",
    "* vali_data : 5k\n",
    "* batch_size : 500\n",
    "* Optimizer : SGD\n",
    "* learning_rate : 1e-4\n",
    "* accuracy_threshold : 0.98\n",
    "* step_limit : 300K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     "output_type": "stream",
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      "The shape of vali is  (5000, 18449)\n",
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   ],
   "source": [
    "# start tensorflow interactiveSession                              \n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import random\n",
    "import h5py\n",
    "\n",
    "filename = 'E:/Alibaba German AI Challenge/data_process/data_shuffle.npy'\n",
    "vali_filename = 'E:/Alibaba German AI Challenge/data_process/sample_of_training.npy'\n",
    "data = np.load(filename)\n",
    "vali = np.load(vali_filename)\n",
    "print('The shape of data is ',data.shape)\n",
    "print('The shape of vali is ',vali.shape)\n",
    "\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "#####################################################     Net Define     ##################################################### \n",
    "\n",
    "# weight initialization\n",
    "def weight_variable(shape):\n",
    "    initial = tf.truncated_normal(shape, stddev=0.1)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "def bias_variable(shape):\n",
    "    initial = tf.constant(0.1, shape = shape)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "# convolution\n",
    "def conv2d(x, W):\n",
    "    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n",
    "# pooling\n",
    "def max_pool_2x2(x):\n",
    "    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "\n",
    "# Create the model\n",
    "# placeholder\n",
    "x = tf.placeholder(\"float\", [None, 18432])\n",
    "y_ = tf.placeholder(\"float\", [None, 17])\n",
    "keep_prob = tf.placeholder(\"float\")\n",
    "x_image = tf.reshape(x, [-1, 32, 32, 18])\n",
    "\n",
    "# first convolutinal layer\n",
    "w_conv1 = weight_variable([5, 5, 18, 32])\n",
    "b_conv1 = bias_variable([32])\n",
    "\n",
    "h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1)\n",
    "h_pool1 = max_pool_2x2(h_conv1)\n",
    "\n",
    "# second convolutional layer\n",
    "w_conv2 = weight_variable([5, 5, 32, 64])\n",
    "b_conv2 = bias_variable([64])\n",
    "\n",
    "h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2)\n",
    "h_pool2 = max_pool_2x2(h_conv2)\n",
    "\n",
    "# flatten\n",
    "h_pool2_flat = tf.reshape(h_pool2, [-1, 8*8*64])\n",
    "\n",
    "# densely connected layer\n",
    "w_fc1 = weight_variable([8*8*64, 1024])\n",
    "b_fc1 = bias_variable([1024])\n",
    "\n",
    "h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)\n",
    "\n",
    "# dropout\n",
    "h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n",
    "\n",
    "# readout layer\n",
    "w_fc2 = weight_variable([1024, 17])\n",
    "b_fc2 = bias_variable([17])\n",
    "\n",
    "y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, w_fc2) + b_fc2)\n",
    "\n",
    "# train and evaluate the model\n",
    "#交叉熵作为损失函数\n",
    "delta = 1e-7\n",
    "cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv+delta))\n",
    "train_step = tf.train.GradientDescentOptimizer(1e-4).minimize(cross_entropy)\n",
    "correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "\n",
    "#####################################################       Train     ##################################################### \n",
    "\n",
    "\n",
    "##随机抽取一部分作为一个mini-batch\n",
    "def get_batch(data, batch_size):\n",
    "    sample = random.sample(list(data),batch_size)\n",
    "    sample = np.array(sample)\n",
    "    train_x = sample[:,:-17]\n",
    "    train_y = sample[:,-17:]\n",
    "    \n",
    "    return train_x, train_y\n",
    "\n",
    "Loss = []\n",
    "#Eval = []\n",
    "\n",
    "batch_size = 500\n",
    "for i in range(300000):\n",
    "    batch_x,batch_y = get_batch(data,batch_size)\n",
    "    train_step.run(feed_dict={x:batch_x, y_:batch_y, keep_prob:0.5})\n",
    "    temp_loss = cross_entropy.eval(feed_dict={x:batch_x, y_:batch_y, keep_prob:1.0})\n",
    "#    vali_accuracy = accuracy.eval(feed_dict={x:vali[:,:-17], y_:vali[:,-17:], keep_prob:1.0})\n",
    "    Loss.append(temp_loss)\n",
    "#    Eval.append(vali_accuracy)\n",
    "    if i%100 == 0:\n",
    "        train_accuracy = accuracy.eval(feed_dict={x:batch_x, y_:batch_y, keep_prob:1.0})\n",
    "        print (\"step %d, train accuracy %g, train loss %g\" %(i, train_accuracy, temp_loss))\n",
    "        if train_accuracy > 0.98:\n",
    "            break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.36      0.85      0.51        71\n",
      "          1       0.65      0.58      0.61       342\n",
      "          2       0.81      0.56      0.66       474\n",
      "          3       0.42      0.72      0.53       119\n",
      "          4       0.41      0.63      0.50       217\n",
      "          5       0.70      0.60      0.65       487\n",
      "          6       0.50      0.93      0.65        40\n",
      "          7       0.85      0.46      0.60       586\n",
      "          8       0.55      0.65      0.60       188\n",
      "          9       0.33      0.59      0.42       158\n",
      "         10       0.96      0.79      0.86       622\n",
      "         11       0.33      0.86      0.48       125\n",
      "         12       0.58      0.83      0.69       134\n",
      "         13       0.87      0.55      0.67       585\n",
      "         14       0.41      1.00      0.58        26\n",
      "         15       0.51      0.96      0.67        91\n",
      "         16       0.98      0.97      0.98       735\n",
      "\n",
      "avg / total       0.76      0.68      0.70      5000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "pred = y_conv.eval(feed_dict={x:vali[:,:-17], y_:vali[:,-17:], keep_prob:1.0})\n",
    "train_val_y = np.argmax(vali[:,-17:],axis = 1)\n",
    "pred_y = np.argmax(pred, axis = 1)\n",
    "print (classification_report(train_val_y, pred_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0   1   2   3   4   5   6   7   8   9   10  11  12  13  14  15  16\n",
      "0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   1\n",
      "1   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "2   0   0   0   0   0   0   0   0   0   0   0   0   0   1   0   0   0\n",
      "3   0   0   0   0   0   0   0   0   1   0   0   0   0   0   0   0   0\n",
      "4   0   0   0   0   0   0   0   1   0   0   0   0   0   0   0   0   0\n"
     ]
    }
   ],
   "source": [
    "import h5py\n",
    "filename = 'E:/Alibaba German AI Challenge/origin_DATA/round1_test_a_20181109.h5'\n",
    "f = h5py.File(filename,'r')\n",
    "test_s1 = f['sen1']\n",
    "test_s2 = f['sen2']\n",
    "\n",
    "test = []\n",
    "for i in range(0,test_s1.shape[0]):\n",
    "    temp1 = test_s1[i].flatten()\n",
    "    temp2 = test_s2[i].flatten()\n",
    "    temp = np.hstack((temp1,temp2))\n",
    "    test.append(temp)\n",
    "test = np.array(test)\n",
    "\n",
    "test_y = np.zeros((test.shape[0],17))\n",
    "\n",
    "pred = tf.argmax(y_conv, 1)\n",
    "\n",
    "test_x_0 = test[0:1500]\n",
    "test_y_0 = test_y[0:1500]\n",
    "P_0 = pred.eval(feed_dict={x:test_x_0, y_:test_y_0, keep_prob:1.0})\n",
    "\n",
    "test_x_1 = test[1500:3000]\n",
    "test_y_1 = test_y[1500:3000]\n",
    "P_1 = pred.eval(feed_dict={x:test_x_1, y_:test_y_1, keep_prob:1.0})\n",
    "\n",
    "test_x_2 = test[3000:4500]\n",
    "test_y_2 = test_y[3000:4500]\n",
    "P_2 = pred.eval(feed_dict={x:test_x_2, y_:test_y_2, keep_prob:1.0})\n",
    "\n",
    "test_x_3 = test[4500:]\n",
    "test_y_3 = test_y[4500:]\n",
    "P_3 = pred.eval(feed_dict={x:test_x_3, y_:test_y_3, keep_prob:1.0})\n",
    "\n",
    "P = np.hstack([P_0,P_1,P_2,P_3])\n",
    "\n",
    "one_hot=tf.one_hot(P,17)\n",
    "Pred_one_hot = sess.run(one_hot)\n",
    "Pred_one_hot = Pred_one_hot.astype(np.int32)\n",
    "out = pd.DataFrame(Pred_one_hot, columns = list(range(17)))\n",
    "print(out.head())\n",
    "\n",
    "out.to_csv('seventh_batch_shuffle_balance_50k_train_SGD.csv', index = False, header = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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31WY2F1gDlAE3uXt5+KwbgYeBHsCL4SUiIiKtoN7Qd/ePgM/UUp4LnFfHNjOBmbWUpwDj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\nOjW0q6CErPwiDh/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I8C/gFnfPi1/msV1JXerRQGZ2KZDt7svqWkdt2mhJxLpR73X3zwAF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\n+iIiIhGh0BcREYkIhb6IiEhE/H8CDBd+dmR+kgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x144ab305b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "xx = list(range(len(Loss)))\n",
    "\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot(xx,Loss)\n",
    "plt.title('Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* train_data : shuffle 50k\n",
    "* vali_data : 5k\n",
    "* batch_size : 500\n",
    "* Optimizer : Adam\n",
    "* learning_rate : 1e-4\n",
    "* accuracy_threshold : no limit\n",
    "* step_limit : 45K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of data is  (50989, 18449)\n",
      "The shape of vali is  (5000, 18449)\n",
      "step 0, train accuracy 0.088, train loss 5047.02\n",
      "step 100, train accuracy 0.302, train loss 1865.6\n",
      "step 200, train accuracy 0.35, train loss 1489.53\n",
      "step 300, train accuracy 0.35, train loss 1229.81\n",
      "step 400, train accuracy 0.352, train loss 1348.95\n",
      "step 500, train accuracy 0.396, train loss 1147.52\n",
      "step 600, train accuracy 0.384, train loss 1028.41\n",
      "step 700, train accuracy 0.386, train loss 1016.24\n",
      "step 800, train accuracy 0.414, train loss 963.475\n",
      "step 900, train accuracy 0.412, train loss 1007.91\n",
      "step 1000, train accuracy 0.468, train loss 878.78\n",
      "step 1100, train accuracy 0.436, train loss 895.969\n",
      "step 1200, train accuracy 0.452, train loss 908.434\n",
      "step 1300, train accuracy 0.478, train loss 871.543\n",
      "step 1400, train accuracy 0.462, train loss 849.294\n",
      "step 1500, train accuracy 0.468, train loss 889.425\n",
      "step 1600, train accuracy 0.484, train loss 842.365\n",
      "step 1700, train accuracy 0.454, train loss 864.764\n",
      "step 1800, train accuracy 0.496, train loss 760.434\n",
      "step 1900, train accuracy 0.464, train loss 797.184\n",
      "step 2000, train accuracy 0.486, train loss 792.17\n",
      "step 2100, train accuracy 0.454, train loss 801.601\n",
      "step 2200, train accuracy 0.496, train loss 832.475\n",
      "step 2300, train accuracy 0.482, train loss 816.819\n",
      "step 2400, train accuracy 0.484, train loss 740.938\n",
      "step 2500, train accuracy 0.482, train loss 767.825\n",
      "step 2600, train accuracy 0.496, train loss 741.463\n",
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      "step 2800, train accuracy 0.518, train loss 711.896\n",
      "step 2900, train accuracy 0.476, train loss 747.694\n",
      "step 3000, train accuracy 0.506, train loss 729.181\n",
      "step 3100, train accuracy 0.518, train loss 746.762\n",
      "step 3200, train accuracy 0.55, train loss 694.162\n",
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      "step 3400, train accuracy 0.578, train loss 640.698\n",
      "step 3500, train accuracy 0.524, train loss 675.967\n",
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      "step 3900, train accuracy 0.524, train loss 701.977\n",
      "step 4000, train accuracy 0.566, train loss 659.563\n",
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      "step 4300, train accuracy 0.534, train loss 666.674\n",
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      "step 4500, train accuracy 0.56, train loss 677.061\n",
      "step 4600, train accuracy 0.56, train loss 634.815\n",
      "step 4700, train accuracy 0.568, train loss 631.826\n",
      "step 4800, train accuracy 0.528, train loss 650.496\n",
      "step 4900, train accuracy 0.588, train loss 602.395\n",
      "step 5000, train accuracy 0.576, train loss 641.285\n",
      "step 5100, train accuracy 0.586, train loss 669.308\n",
      "step 5200, train accuracy 0.582, train loss 610.72\n",
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      "step 5400, train accuracy 0.588, train loss 628.724\n",
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      "step 11900, train accuracy 0.718, train loss 414.209\n",
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      "step 15700, train accuracy 0.762, train loss 344.358\n",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 31300, train accuracy 0.948, train loss 112.939\n",
      "step 31400, train accuracy 0.946, train loss 101.689\n",
      "step 31500, train accuracy 0.944, train loss 112.261\n",
      "step 31600, train accuracy 0.94, train loss 120.889\n",
      "step 31700, train accuracy 0.938, train loss 142.736\n",
      "step 31800, train accuracy 0.942, train loss 112.938\n",
      "step 31900, train accuracy 0.942, train loss 108.699\n"
     ]
    }
   ],
   "source": [
    "# start tensorflow interactiveSession                              \n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import random\n",
    "import h5py\n",
    "\n",
    "filename = 'E:/Alibaba German AI Challenge/data_process/data_shuffle.npy'\n",
    "vali_filename = 'E:/Alibaba German AI Challenge/data_process/sample_of_training.npy'\n",
    "data = np.load(filename)\n",
    "vali = np.load(vali_filename)\n",
    "print('The shape of data is ',data.shape)\n",
    "print('The shape of vali is ',vali.shape)\n",
    "\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "#####################################################     Net Define     ##################################################### \n",
    "\n",
    "# weight initialization\n",
    "def weight_variable(shape):\n",
    "    initial = tf.truncated_normal(shape, stddev=0.1)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "def bias_variable(shape):\n",
    "    initial = tf.constant(0.1, shape = shape)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "# convolution\n",
    "def conv2d(x, W):\n",
    "    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n",
    "# pooling\n",
    "def max_pool_2x2(x):\n",
    "    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "\n",
    "# Create the model\n",
    "# placeholder\n",
    "x = tf.placeholder(\"float\", [None, 18432])\n",
    "y_ = tf.placeholder(\"float\", [None, 17])\n",
    "keep_prob = tf.placeholder(\"float\")\n",
    "x_image = tf.reshape(x, [-1, 32, 32, 18])\n",
    "\n",
    "# first convolutinal layer\n",
    "w_conv1 = weight_variable([5, 5, 18, 32])\n",
    "b_conv1 = bias_variable([32])\n",
    "\n",
    "h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1)\n",
    "h_pool1 = max_pool_2x2(h_conv1)\n",
    "\n",
    "# second convolutional layer\n",
    "w_conv2 = weight_variable([5, 5, 32, 64])\n",
    "b_conv2 = bias_variable([64])\n",
    "\n",
    "h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2)\n",
    "h_pool2 = max_pool_2x2(h_conv2)\n",
    "\n",
    "# flatten\n",
    "h_pool2_flat = tf.reshape(h_pool2, [-1, 8*8*64])\n",
    "\n",
    "# densely connected layer\n",
    "w_fc1 = weight_variable([8*8*64, 1024])\n",
    "b_fc1 = bias_variable([1024])\n",
    "\n",
    "h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)\n",
    "\n",
    "# dropout\n",
    "h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n",
    "\n",
    "# readout layer\n",
    "w_fc2 = weight_variable([1024, 17])\n",
    "b_fc2 = bias_variable([17])\n",
    "\n",
    "y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, w_fc2) + b_fc2)\n",
    "\n",
    "# train and evaluate the model\n",
    "#交叉熵作为损失函数\n",
    "delta = 1e-7\n",
    "cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv+delta))\n",
    "train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n",
    "correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "\n",
    "#####################################################       Train     ##################################################### \n",
    "\n",
    "\n",
    "##随机抽取一部分作为一个mini-batch\n",
    "def get_batch(data, batch_size):\n",
    "    sample = random.sample(list(data),batch_size)\n",
    "    sample = np.array(sample)\n",
    "    train_x = sample[:,:-17]\n",
    "    train_y = sample[:,-17:]\n",
    "    \n",
    "    return train_x, train_y\n",
    "\n",
    "Loss = []\n",
    "Eval = []\n",
    "\n",
    "batch_size = 500\n",
    "for i in range(45000):\n",
    "    batch_x,batch_y = get_batch(data,batch_size)\n",
    "    train_step.run(feed_dict={x:batch_x, y_:batch_y, keep_prob:0.5})\n",
    "    temp_loss = cross_entropy.eval(feed_dict={x:batch_x, y_:batch_y, keep_prob:1.0})\n",
    "    vali_accuracy = accuracy.eval(feed_dict={x:vali[:,:-17], y_:vali[:,-17:], keep_prob:1.0})\n",
    "    Loss.append(temp_loss)\n",
    "    Eval.append(vali_accuracy)\n",
    "    if i%100 == 0:\n",
    "        train_accuracy = accuracy.eval(feed_dict={x:batch_x, y_:batch_y, keep_prob:1.0})\n",
    "        print (\"step %d, train accuracy %g, train loss %g\" %(i, train_accuracy, temp_loss))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.27      0.72      0.39        71\n",
      "          1       0.53      0.46      0.49       342\n",
      "          2       0.63      0.33      0.44       474\n",
      "          3       0.39      0.63      0.48       119\n",
      "          4       0.33      0.45      0.38       217\n",
      "          5       0.55      0.43      0.48       487\n",
      "          6       0.32      0.90      0.47        40\n",
      "          7       0.82      0.46      0.59       586\n",
      "          8       0.44      0.65      0.53       188\n",
      "          9       0.28      0.51      0.36       158\n",
      "         10       0.94      0.80      0.87       622\n",
      "         11       0.33      0.79      0.46       125\n",
      "         12       0.51      0.71      0.59       134\n",
      "         13       0.79      0.55      0.65       585\n",
      "         14       0.38      1.00      0.55        26\n",
      "         15       0.50      0.87      0.64        91\n",
      "         16       0.98      0.97      0.97       735\n",
      "\n",
      "avg / total       0.69      0.62      0.63      5000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "pred = y_conv.eval(feed_dict={x:vali[:,:-17], y_:vali[:,-17:], keep_prob:1.0})\n",
    "train_val_y = np.argmax(vali[:,-17:],axis = 1)\n",
    "pred_y = np.argmax(pred, axis = 1)\n",
    "print (classification_report(train_val_y, pred_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8H3b3x4PmrcGwOMFzRdBeBgyN270waCsLlg9tfx93v8fdi9y9KC8vr7196ZBGBbiIiIRY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23091e22320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "xx = list(range(len(Loss)))\n",
    "\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot(xx,Loss)\n",
    "plt.title('Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OEBeywEOCL45RPZqWOnOXiIiEh8I5iu0+ePSkHKd+jUTuG9mJ807zd1uf06EB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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x230a69b2be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "xx = list(range(len(Eval)))\n",
    "\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot(xx,Eval)\n",
    "plt.title('Eval')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* train_data : shuffle 50k\n",
    "* vali_data : 5k\n",
    "* batch_size : 500\n",
    "* Optimizer : SGD\n",
    "* learning_rate : 1e-4\n",
    "* accuracy_threshold : no limit\n",
    "* step_limit : 150K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of data is  (50989, 18449)\n",
      "The shape of vali is  (5000, 18449)\n",
      "step 0, train accuracy 0.13, train loss 6146.86\n",
      "step 100, train accuracy 0.124, train loss 1410.16\n",
      "step 200, train accuracy 0.186, train loss 1260.41\n",
      "step 300, train accuracy 0.154, train loss 1256.2\n",
      "step 400, train accuracy 0.256, train loss 1096.48\n",
      "step 500, train accuracy 0.234, train loss 1103.16\n",
      "step 600, train accuracy 0.306, train loss 1005.87\n",
      "step 700, train accuracy 0.378, train loss 903.369\n",
      "step 800, train accuracy 0.368, train loss 911.074\n",
      "step 900, train accuracy 0.39, train loss 884.617\n",
      "step 1000, train accuracy 0.438, train loss 830.192\n",
      "step 1100, train accuracy 0.476, train loss 838.536\n",
      "step 1200, train accuracy 0.386, train loss 856.961\n",
      "step 1300, train accuracy 0.45, train loss 824.357\n",
      "step 1400, train accuracy 0.462, train loss 768.298\n",
      "step 1500, train accuracy 0.51, train loss 757.007\n",
      "step 1600, train accuracy 0.476, train loss 771.884\n",
      "step 1700, train accuracy 0.466, train loss 778.732\n",
      "step 1800, train accuracy 0.486, train loss 743.908\n",
      "step 1900, train accuracy 0.544, train loss 679.987\n",
      "step 2000, train accuracy 0.518, train loss 714.677\n",
      "step 2100, train accuracy 0.536, train loss 682.276\n",
      "step 2200, train accuracy 0.538, train loss 655.362\n",
      "step 2300, train accuracy 0.518, train loss 676.2\n",
      "step 2400, train accuracy 0.552, train loss 672.348\n",
      "step 2500, train accuracy 0.556, train loss 638.537\n",
      "step 2600, train accuracy 0.516, train loss 677.406\n",
      "step 2700, train accuracy 0.548, train loss 652.891\n",
      "step 2800, train accuracy 0.564, train loss 617.049\n",
      "step 2900, train accuracy 0.57, train loss 640.185\n",
      "step 3000, train accuracy 0.578, train loss 597.957\n",
      "step 3100, train accuracy 0.562, train loss 636.307\n",
      "step 3200, train accuracy 0.556, train loss 636.395\n",
      "step 3300, train accuracy 0.584, train loss 614.037\n",
      "step 3400, train accuracy 0.572, train loss 602.436\n",
      "step 3500, train accuracy 0.604, train loss 561.419\n",
      "step 3600, train accuracy 0.542, train loss 641.27\n",
      "step 3700, train accuracy 0.598, train loss 579.826\n",
      "step 3800, train accuracy 0.604, train loss 543.153\n",
      "step 3900, train accuracy 0.658, train loss 537.42\n",
      "step 4000, train accuracy 0.574, train loss 636.278\n",
      "step 4100, train accuracy 0.572, train loss 592.751\n",
      "step 4200, train accuracy 0.596, train loss 559.583\n",
      "step 4300, train accuracy 0.608, train loss 591.593\n",
      "step 4400, train accuracy 0.598, train loss 574.783\n",
      "step 4500, train accuracy 0.61, train loss 548.206\n",
      "step 4600, train accuracy 0.626, train loss 536.324\n",
      "step 4700, train accuracy 0.604, train loss 581.535\n",
      "step 4800, train accuracy 0.618, train loss 568.655\n",
      "step 4900, train accuracy 0.658, train loss 524.631\n",
      "step 5000, train accuracy 0.64, train loss 537.466\n",
      "step 5100, train accuracy 0.628, train loss 523.981\n",
      "step 5200, train accuracy 0.596, train loss 579.739\n",
      "step 5300, train accuracy 0.602, train loss 566.197\n",
      "step 5400, train accuracy 0.592, train loss 568.866\n",
      "step 5500, train accuracy 0.62, train loss 530.413\n",
      "step 5600, train accuracy 0.644, train loss 505.201\n",
      "step 5700, train accuracy 0.6, train loss 509.072\n",
      "step 5800, train accuracy 0.628, train loss 506.703\n",
      "step 5900, train accuracy 0.658, train loss 506.776\n",
      "step 6000, train accuracy 0.592, train loss 551.148\n",
      "step 6100, train accuracy 0.612, train loss 513.034\n",
      "step 6200, train accuracy 0.672, train loss 507.284\n",
      "step 6300, train accuracy 0.656, train loss 470.176\n",
      "step 6400, train accuracy 0.642, train loss 517.199\n",
      "step 6500, train accuracy 0.654, train loss 504.501\n",
      "step 6600, train accuracy 0.66, train loss 496.836\n",
      "step 6700, train accuracy 0.682, train loss 470.399\n",
      "step 6800, train accuracy 0.668, train loss 511.195\n",
      "step 6900, train accuracy 0.64, train loss 512.935\n",
      "step 7000, train accuracy 0.668, train loss 475.136\n",
      "step 7100, train accuracy 0.616, train loss 535.893\n",
      "step 7200, train accuracy 0.612, train loss 572.547\n",
      "step 7300, train accuracy 0.652, train loss 454.823\n",
      "step 7400, train accuracy 0.71, train loss 483.012\n",
      "step 7500, train accuracy 0.634, train loss 508.276\n",
      "step 7600, train accuracy 0.658, train loss 480.131\n",
      "step 7700, train accuracy 0.616, train loss 525.154\n",
      "step 7800, train accuracy 0.628, train loss 490.841\n",
      "step 7900, train accuracy 0.654, train loss 483.703\n",
      "step 8000, train accuracy 0.666, train loss 487.472\n",
      "step 8100, train accuracy 0.724, train loss 427.544\n",
      "step 8200, train accuracy 0.724, train loss 415.489\n",
      "step 8300, train accuracy 0.696, train loss 459.14\n",
      "step 8400, train accuracy 0.676, train loss 466.101\n",
      "step 8500, train accuracy 0.69, train loss 448.699\n",
      "step 8600, train accuracy 0.682, train loss 451.184\n",
      "step 8700, train accuracy 0.662, train loss 489.093\n",
      "step 8800, train accuracy 0.702, train loss 461.663\n",
      "step 8900, train accuracy 0.644, train loss 479.815\n",
      "step 9000, train accuracy 0.688, train loss 465.62\n",
      "step 9100, train accuracy 0.688, train loss 468.456\n",
      "step 9200, train accuracy 0.672, train loss 465.339\n",
      "step 9300, train accuracy 0.67, train loss 447.37\n",
      "step 9400, train accuracy 0.67, train loss 471.185\n",
      "step 9500, train accuracy 0.694, train loss 463.706\n",
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      "step 45700, train accuracy 0.89, train loss 166.799\n",
      "step 45800, train accuracy 0.9, train loss 157.039\n",
      "step 45900, train accuracy 0.922, train loss 147.967\n",
      "step 46000, train accuracy 0.894, train loss 170.091\n",
      "step 46100, train accuracy 0.888, train loss 186.301\n",
      "step 46200, train accuracy 0.882, train loss 169.83\n",
      "step 46300, train accuracy 0.908, train loss 155.002\n",
      "step 46400, train accuracy 0.902, train loss 176.697\n",
      "step 46500, train accuracy 0.876, train loss 204.036\n",
      "step 46600, train accuracy 0.86, train loss 193.816\n",
      "step 46700, train accuracy 0.842, train loss 282.909\n",
      "step 46800, train accuracy 0.852, train loss 194.385\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 46900, train accuracy 0.886, train loss 172.324\n",
      "step 47000, train accuracy 0.924, train loss 129.041\n",
      "step 47100, train accuracy 0.904, train loss 156.882\n",
      "step 47200, train accuracy 0.872, train loss 181.774\n",
      "step 47300, train accuracy 0.912, train loss 139.967\n",
      "step 47400, train accuracy 0.896, train loss 161.983\n",
      "step 47500, train accuracy 0.892, train loss 161.11\n",
      "step 47600, train accuracy 0.882, train loss 165.001\n",
      "step 47700, train accuracy 0.908, train loss 145.015\n",
      "step 47800, train accuracy 0.904, train loss 145.97\n",
      "step 47900, train accuracy 0.902, train loss 175.424\n",
      "step 48000, train accuracy 0.916, train loss 132.754\n",
      "step 48100, train accuracy 0.914, train loss 136.433\n",
      "step 48200, train accuracy 0.908, train loss 148.482\n",
      "step 48300, train accuracy 0.918, train loss 131.942\n",
      "step 48400, train accuracy 0.894, train loss 161.256\n",
      "step 48500, train accuracy 0.89, train loss 162.242\n",
      "step 48600, train accuracy 0.904, train loss 149.738\n",
      "step 48700, train accuracy 0.888, train loss 185.406\n",
      "step 48800, train accuracy 0.904, train loss 169.947\n",
      "step 48900, train accuracy 0.898, train loss 167.603\n",
      "step 49000, train accuracy 0.892, train loss 179.71\n",
      "step 49100, train accuracy 0.926, train loss 136.423\n",
      "step 49200, train accuracy 0.898, train loss 165.712\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-90c64aa97123>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m    102\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m150000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    103\u001b[0m     \u001b[0mbatch_x\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbatch_y\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 104\u001b[1;33m     \u001b[0mtrain_step\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mbatch_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mbatch_y\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeep_prob\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    105\u001b[0m     \u001b[0mtemp_loss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcross_entropy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mbatch_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mbatch_y\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeep_prob\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    106\u001b[0m     \u001b[0mvali_accuracy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mvali\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m17\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mvali\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m17\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeep_prob\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda3\\envs\\tensorflow-gpu\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, feed_dict, session)\u001b[0m\n\u001b[0;32m   2397\u001b[0m         \u001b[0mnone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0msession\u001b[0m \u001b[0mwill\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mused\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2398\u001b[0m     \"\"\"\n\u001b[1;32m-> 2399\u001b[1;33m     \u001b[0m_run_using_default_session\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2400\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2401\u001b[0m \u001b[0m_gradient_registry\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mregistry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mRegistry\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"gradient\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda3\\envs\\tensorflow-gpu\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36m_run_using_default_session\u001b[1;34m(operation, feed_dict, graph, session)\u001b[0m\n\u001b[0;32m   5246\u001b[0m                        \u001b[1;34m\"the operation's graph is different from the session's \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5247\u001b[0m                        \"graph.\")\n\u001b[1;32m-> 5248\u001b[1;33m   \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moperation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5249\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5250\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda3\\envs\\tensorflow-gpu\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    898\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    899\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 900\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    901\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    902\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda3\\envs\\tensorflow-gpu\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1133\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1134\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1135\u001b[1;33m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m   1136\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1137\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda3\\envs\\tensorflow-gpu\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1314\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1315\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[1;32m-> 1316\u001b[1;33m                            run_metadata)\n\u001b[0m\u001b[0;32m   1317\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1318\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda3\\envs\\tensorflow-gpu\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1320\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1321\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1322\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1323\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1324\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda3\\envs\\tensorflow-gpu\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1305\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1306\u001b[0m       return self._call_tf_sessionrun(\n\u001b[1;32m-> 1307\u001b[1;33m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[0;32m   1308\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1309\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda3\\envs\\tensorflow-gpu\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m   1407\u001b[0m       return tf_session.TF_SessionRun_wrapper(\n\u001b[0;32m   1408\u001b[0m           \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1409\u001b[1;33m           run_metadata)\n\u001b[0m\u001b[0;32m   1410\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1411\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_exception_on_not_ok_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# start tensorflow interactiveSession                              \n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import random\n",
    "import h5py\n",
    "\n",
    "filename = 'E:/Alibaba German AI Challenge/data_process/data_shuffle.npy'\n",
    "vali_filename = 'E:/Alibaba German AI Challenge/data_process/sample_of_training.npy'\n",
    "data = np.load(filename)\n",
    "vali = np.load(vali_filename)\n",
    "print('The shape of data is ',data.shape)\n",
    "print('The shape of vali is ',vali.shape)\n",
    "\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "#####################################################     Net Define     ##################################################### \n",
    "\n",
    "# weight initialization\n",
    "def weight_variable(shape):\n",
    "    initial = tf.truncated_normal(shape, stddev=0.1)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "def bias_variable(shape):\n",
    "    initial = tf.constant(0.1, shape = shape)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "# convolution\n",
    "def conv2d(x, W):\n",
    "    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n",
    "# pooling\n",
    "def max_pool_2x2(x):\n",
    "    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "\n",
    "# Create the model\n",
    "# placeholder\n",
    "x = tf.placeholder(\"float\", [None, 18432])\n",
    "y_ = tf.placeholder(\"float\", [None, 17])\n",
    "keep_prob = tf.placeholder(\"float\")\n",
    "x_image = tf.reshape(x, [-1, 32, 32, 18])\n",
    "\n",
    "# first convolutinal layer\n",
    "w_conv1 = weight_variable([5, 5, 18, 32])\n",
    "b_conv1 = bias_variable([32])\n",
    "\n",
    "h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1)\n",
    "h_pool1 = max_pool_2x2(h_conv1)\n",
    "\n",
    "# second convolutional layer\n",
    "w_conv2 = weight_variable([5, 5, 32, 64])\n",
    "b_conv2 = bias_variable([64])\n",
    "\n",
    "h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2)\n",
    "h_pool2 = max_pool_2x2(h_conv2)\n",
    "\n",
    "# flatten\n",
    "h_pool2_flat = tf.reshape(h_pool2, [-1, 8*8*64])\n",
    "\n",
    "# densely connected layer\n",
    "w_fc1 = weight_variable([8*8*64, 1024])\n",
    "b_fc1 = bias_variable([1024])\n",
    "\n",
    "h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)\n",
    "\n",
    "# dropout\n",
    "h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n",
    "\n",
    "# readout layer\n",
    "w_fc2 = weight_variable([1024, 17])\n",
    "b_fc2 = bias_variable([17])\n",
    "\n",
    "y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, w_fc2) + b_fc2)\n",
    "\n",
    "# train and evaluate the model\n",
    "#交叉熵作为损失函数\n",
    "delta = 1e-7\n",
    "cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv+delta))\n",
    "train_step = tf.train.GradientDescentOptimizer(1e-4).minimize(cross_entropy)\n",
    "correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "\n",
    "#####################################################       Train     ##################################################### \n",
    "\n",
    "\n",
    "##随机抽取一部分作为一个mini-batch\n",
    "def get_batch(data, batch_size):\n",
    "    sample = random.sample(list(data),batch_size)\n",
    "    sample = np.array(sample)\n",
    "    train_x = sample[:,:-17]\n",
    "    train_y = sample[:,-17:]\n",
    "    \n",
    "    return train_x, train_y\n",
    "\n",
    "Loss = []\n",
    "vali_Eval = []\n",
    "train_Eval = []\n",
    "\n",
    "batch_size = 500\n",
    "for i in range(150000):\n",
    "    batch_x,batch_y = get_batch(data,batch_size)\n",
    "    train_step.run(feed_dict={x:batch_x, y_:batch_y, keep_prob:0.5})\n",
    "    temp_loss = cross_entropy.eval(feed_dict={x:batch_x, y_:batch_y, keep_prob:1.0})\n",
    "    vali_accuracy = accuracy.eval(feed_dict={x:vali[:,:-17], y_:vali[:,-17:], keep_prob:1.0})\n",
    "    train_accuracy = accuracy.eval(feed_dict={x:batch_x, y_:batch_y, keep_prob:1.0})\n",
    "    Loss.append(temp_loss)\n",
    "    vali_Eval.append(vali_accuracy)\n",
    "    train_Eval.append(train_accuracy)\n",
    "    if i%100 == 0:\n",
    "        train_accuracy = accuracy.eval(feed_dict={x:batch_x, y_:batch_y, keep_prob:1.0})\n",
    "        print (\"step %d, train accuracy %g, train loss %g\" %(i, train_accuracy, temp_loss))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rm2LjtQZrYAbkvrJxD9noavPXX4HffnMn3ZFprGjB2ZDq6E9EXvLjhSJzzj7w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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x215582c8dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "xx = list(range(len(train_Eval)))\n",
    "\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot(xx,train_Eval, color = 'blue', label = 'train')\n",
    "plt.plot(xx,vali_Eval, color = 'red', label = 'vali')\n",
    "plt.title('Eval')\n",
    "plt.legend()\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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}