CNN_CIFAR10.ipynb

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     "text": [
      "/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.image as mpimg\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import os\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define required function \n",
    "\n",
    "#create and store weights\n",
    "def weight_variable(shape):\n",
    "    initial = tf.truncated_normal(shape, stddev=0.1)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "#create and store bias\n",
    "def bias_variable(shape):\n",
    "    initial = tf.constant(0.1, shape=shape)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "#build convolutional layer\n",
    "def conv2d(x, W):\n",
    "    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n",
    "\n",
    "#max pool\n",
    "def max_pool_2x2(x):\n",
    "    return tf.nn.max_pool(x, ksize=[1, 3, 3, 1],\n",
    "                        strides=[1, 2, 2, 1], padding='SAME')\n",
    "\n",
    "#randomly choose n samples in the dataset to train\n",
    "def random_batch(n, imgs, labels):\n",
    "    index = np.arange(0 , len(imgs))\n",
    "    np.random.shuffle(index)\n",
    "    idx = index[0:n]\n",
    "    imgs_random = [imgs[ i] for i in idx]\n",
    "    labels_random = [labels[ i] for i in idx]\n",
    "\n",
    "    return np.asarray(imgs_random), np.asarray(labels_random)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#change to the directory of data \n",
    "os.chdir('/Users/rosen/Documents/DS/Machine_Learning_1/project/train')\n",
    "\n",
    "#loading the images\n",
    "data=[]\n",
    "for i in range(1,50001):\n",
    "    x=str(i)+'.png'\n",
    "    y=mpimg.imread(x)\n",
    "    data.append(y)\n",
    "    \n",
    "#loading the labels and change the data type from string to array.\n",
    "label_o=pd.read_csv('trainLabels.csv')\n",
    "class_le = LabelEncoder()\n",
    "label_ = class_le.fit_transform(label_o['label'])   \n",
    "label_new=[]\n",
    "for i in range(0,len(label_)):\n",
    "    a=np.array([0,0,0,0,0,0,0,0,0,0])\n",
    "    a[label_[i]]=1\n",
    "    label_new.append(a)\n",
    "label = np.array(label_new)\n",
    "\n",
    "#split the data into training set and testing set.\n",
    "X_train=np.array(data[:40000]).reshape(-1,3072)\n",
    "X_test=np.array(data[40000:]).reshape(-1,3072)\n",
    "y_train=np.array(label[:40000])\n",
    "y_test=np.array(label[40000:])"
   ]
  },
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     "text": [
      "step 0, training accuracy 0.06\n",
      "step 100, training accuracy 0.26\n",
      "step 200, training accuracy 0.3\n",
      "step 300, training accuracy 0.38\n",
      "step 400, training accuracy 0.5\n",
      "step 500, training accuracy 0.42\n",
      "step 600, training accuracy 0.62\n",
      "step 700, training accuracy 0.48\n",
      "step 800, training accuracy 0.48\n",
      "step 900, training accuracy 0.42\n",
      "step 1000, training accuracy 0.54\n",
      "step 1100, training accuracy 0.4\n",
      "step 1200, training accuracy 0.5\n",
      "step 1300, training accuracy 0.6\n",
      "step 1400, training accuracy 0.54\n",
      "step 1500, training accuracy 0.54\n",
      "step 1600, training accuracy 0.5\n",
      "step 1700, training accuracy 0.48\n",
      "step 1800, training accuracy 0.52\n",
      "step 1900, training accuracy 0.6\n",
      "step 2000, training accuracy 0.62\n",
      "step 2100, training accuracy 0.58\n",
      "step 2200, training accuracy 0.52\n",
      "step 2300, training accuracy 0.56\n",
      "step 2400, training accuracy 0.54\n",
      "step 2500, training accuracy 0.6\n",
      "step 2600, training accuracy 0.68\n",
      "step 2700, training accuracy 0.6\n",
      "step 2800, training accuracy 0.5\n",
      "step 2900, training accuracy 0.66\n",
      "step 3000, training accuracy 0.76\n",
      "step 3100, training accuracy 0.68\n",
      "step 3200, training accuracy 0.68\n",
      "step 3300, training accuracy 0.44\n",
      "step 3400, training accuracy 0.68\n",
      "step 3500, training accuracy 0.62\n",
      "step 3600, training accuracy 0.68\n",
      "step 3700, training accuracy 0.66\n",
      "step 3800, training accuracy 0.74\n",
      "step 3900, training accuracy 0.54\n",
      "step 4000, training accuracy 0.76\n",
      "step 4100, training accuracy 0.64\n",
      "step 4200, training accuracy 0.68\n",
      "step 4300, training accuracy 0.76\n",
      "step 4400, training accuracy 0.66\n",
      "step 4500, training accuracy 0.72\n",
      "step 4600, training accuracy 0.64\n",
      "step 4700, training accuracy 0.74\n",
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      "step 4900, training accuracy 0.8\n",
      "step 5000, training accuracy 0.72\n",
      "step 5100, training accuracy 0.6\n",
      "step 5200, training accuracy 0.72\n",
      "step 5300, training accuracy 0.76\n",
      "step 5400, training accuracy 0.72\n",
      "step 5500, training accuracy 0.68\n",
      "step 5600, training accuracy 0.66\n",
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      "step 5800, training accuracy 0.74\n",
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      "step 6000, training accuracy 0.68\n",
      "step 6100, training accuracy 0.7\n",
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      "step 6300, training accuracy 0.66\n",
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      "step 6700, training accuracy 0.62\n",
      "step 6800, training accuracy 0.8\n",
      "step 6900, training accuracy 0.66\n",
      "step 7000, training accuracy 0.62\n",
      "step 7100, training accuracy 0.78\n",
      "step 7200, training accuracy 0.7\n",
      "step 7300, training accuracy 0.66\n",
      "step 7400, training accuracy 0.78\n",
      "step 7500, training accuracy 0.68\n",
      "step 7600, training accuracy 0.6\n",
      "step 7700, training accuracy 0.8\n",
      "step 7800, training accuracy 0.68\n",
      "step 7900, training accuracy 0.78\n",
      "step 8000, training accuracy 0.76\n",
      "step 8100, training accuracy 0.76\n",
      "step 8200, training accuracy 0.76\n",
      "step 8300, training accuracy 0.74\n",
      "step 8400, training accuracy 0.74\n",
      "step 8500, training accuracy 0.76\n",
      "step 8600, training accuracy 0.72\n",
      "step 8700, training accuracy 0.76\n",
      "step 8800, training accuracy 0.72\n",
      "step 8900, training accuracy 0.82\n",
      "step 9000, training accuracy 0.82\n",
      "step 9100, training accuracy 0.76\n",
      "step 9200, training accuracy 0.78\n",
      "step 9300, training accuracy 0.74\n",
      "step 9400, training accuracy 0.66\n",
      "step 9500, training accuracy 0.78\n",
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      "step 9700, training accuracy 0.74\n",
      "step 9800, training accuracy 0.72\n",
      "step 9900, training accuracy 0.68\n",
      "step 10000, training accuracy 0.72\n",
      "step 10100, training accuracy 0.7\n",
      "step 10200, training accuracy 0.78\n",
      "step 10300, training accuracy 0.78\n",
      "step 10400, training accuracy 0.88\n",
      "step 10500, training accuracy 0.9\n",
      "step 10600, training accuracy 0.76\n",
      "step 10700, training accuracy 0.82\n",
      "step 10800, training accuracy 0.74\n",
      "step 10900, training accuracy 0.82\n",
      "step 11000, training accuracy 0.82\n",
      "step 11100, training accuracy 0.86\n",
      "step 11200, training accuracy 0.76\n",
      "step 11300, training accuracy 0.84\n",
      "step 11400, training accuracy 0.8\n",
      "step 11500, training accuracy 0.84\n",
      "step 11600, training accuracy 0.82\n",
      "step 11700, training accuracy 0.76\n",
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      "step 12000, training accuracy 0.92\n",
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      "step 12200, training accuracy 0.9\n",
      "step 12300, training accuracy 0.92\n",
      "step 12400, training accuracy 0.84\n",
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      "step 14000, training accuracy 0.88\n",
      "step 14100, training accuracy 0.8\n",
      "step 14200, training accuracy 0.88\n",
      "step 14300, training accuracy 0.92\n",
      "step 14400, training accuracy 0.82\n",
      "step 14500, training accuracy 0.82\n",
      "step 14600, training accuracy 0.9\n",
      "step 14700, training accuracy 0.86\n",
      "step 14800, training accuracy 0.92\n",
      "step 14900, training accuracy 0.9\n",
      "step 15000, training accuracy 0.88\n",
      "step 15100, training accuracy 0.86\n",
      "step 15200, training accuracy 0.9\n",
      "step 15300, training accuracy 0.9\n",
      "step 15400, training accuracy 0.92\n",
      "step 15500, training accuracy 0.86\n",
      "step 15600, training accuracy 0.92\n",
      "step 15700, training accuracy 0.98\n",
      "step 15800, training accuracy 0.88\n",
      "step 15900, training accuracy 0.84\n",
      "step 16000, training accuracy 0.9\n",
      "step 16100, training accuracy 0.88\n",
      "step 16200, training accuracy 0.92\n",
      "step 16300, training accuracy 0.82\n",
      "step 16400, training accuracy 0.94\n",
      "step 16500, training accuracy 0.96\n",
      "step 16600, training accuracy 0.84\n",
      "step 16700, training accuracy 0.88\n",
      "step 16800, training accuracy 0.96\n",
      "step 16900, training accuracy 0.9\n",
      "step 17000, training accuracy 0.84\n",
      "step 17100, training accuracy 0.9\n",
      "step 17200, training accuracy 0.92\n",
      "step 17300, training accuracy 0.88\n",
      "step 17400, training accuracy 0.82\n",
      "step 17500, training accuracy 0.94\n",
      "step 17600, training accuracy 0.98\n",
      "step 17700, training accuracy 0.96\n",
      "step 17800, training accuracy 0.9\n",
      "step 17900, training accuracy 0.94\n",
      "step 18000, training accuracy 0.92\n",
      "step 18100, training accuracy 0.94\n",
      "step 18200, training accuracy 0.92\n",
      "step 18300, training accuracy 0.98\n",
      "step 18400, training accuracy 0.94\n",
      "step 18500, training accuracy 0.94\n",
      "step 18600, training accuracy 0.94\n",
      "step 18700, training accuracy 0.94\n",
      "step 18800, training accuracy 0.9\n",
      "step 18900, training accuracy 0.9\n",
      "step 19000, training accuracy 0.9\n",
      "step 19100, training accuracy 0.9\n",
      "step 19200, training accuracy 0.94\n",
      "step 19300, training accuracy 0.96\n",
      "step 19400, training accuracy 0.92\n",
      "step 19500, training accuracy 0.9\n",
      "step 19600, training accuracy 0.94\n",
      "step 19700, training accuracy 0.9\n",
      "step 19800, training accuracy 0.96\n",
      "step 19900, training accuracy 0.98\n",
      "test accuracy 0.7271\n",
      "Total time cost: 37.546091330051425 minutes\n"
     ]
    }
   ],
   "source": [
    "#building input holder\n",
    "x  = tf.placeholder(\"float\", shape=[None, 3072])\n",
    "y_ = tf.placeholder(\"float\", shape=[None, 10])\n",
    "x_image = tf.reshape(x, [-1,32,32,3])\n",
    "\n",
    "#building two convolutional layers with rectified linear activations and max pool function.\n",
    "W_conv1 = weight_variable([5, 5, 3, 32])\n",
    "b_conv1 = bias_variable([32])\n",
    "h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)\n",
    "h_pool1 = max_pool_2x2(h_conv1)\n",
    "W_conv2 = weight_variable([5, 5, 32, 64])\n",
    "b_conv2 = bias_variable([64])\n",
    "h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)\n",
    "h_pool2 = max_pool_2x2(h_conv2)\n",
    "\n",
    "\n",
    "#building two fully connected layers\n",
    "W_fc1 = weight_variable([8 * 8 * 64, 1024])\n",
    "b_fc1 = bias_variable([1024])\n",
    "\n",
    "h_pool_flat = tf.reshape(h_pool2, [-1, 8 * 8 * 64])\n",
    "h_fc1 = tf.nn.relu(tf.matmul(h_pool_flat, W_fc1) + b_fc1)\n",
    "\n",
    "keep_prob = tf.placeholder(\"float\")\n",
    "h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n",
    "\n",
    "W_fc2 = weight_variable([1024, 10])\n",
    "b_fc2 = bias_variable([10])\n",
    "\n",
    "y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)\n",
    "\n",
    "#building traning step to reduce cross entropy.\n",
    "cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))\n",
    "train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n",
    "\n",
    "#test the accuracy\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",
    "\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "   \n",
    "start=time()\n",
    "\n",
    "#train the model 20000 times, each time ramdonly pick 50 samples as the batch.\n",
    "for i in range(20000):\n",
    "    \n",
    "    batch = random_batch(50, X_train, y_train)\n",
    "    #show the traning accuracy every 100 steps.\n",
    "    if i%100 == 0:\n",
    "        train_accuracy = sess.run(accuracy, feed_dict={x:batch[0], y_: batch[1], keep_prob: 1.0})\n",
    "        print(\"step %d, training accuracy %g\"%(i, train_accuracy))\n",
    "    sess.run(train_step,feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})\n",
    "print (\"test accuracy %g\"% sess.run(accuracy, feed_dict={x: X_test, y_: y_test, keep_prob: 1.0}) )\n",
    "end = time()\n",
    "\n",
    "#print the time cost\n",
    "cost = str((end-start)/60.)+' minutes'\n",
    "print('Total time cost: '+cost)"
   ]
  },
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