a1q2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Images in train: 55000 validation: 5000 test: 10000\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "train_count = mnist.train.images.shape[0]\n",
    "test_count = mnist.test.images.shape[0]\n",
    "val_count = mnist.validation.images.shape[0]\n",
    "print (\"Images in train: {} validation: {} test: {}\".format(train_count,val_count,test_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xd2693b710>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#access first image\n",
    "first_image = mnist.train.images[10]\n",
    "first_image = np.array(first_image, dtype='float')\n",
    "pixels = first_image.reshape((28, 28))\n",
    "plt.imshow(pixels, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 100\n",
    "n_epochs = 50\n",
    "learning_rate = 0.0001\n",
    "X= tf.placeholder(tf.float32,[batch_size,784],name=\"image\")\n",
    "Y= tf.placeholder(tf.float32,[batch_size,10],name=\"label\")\n",
    "W = tf.Variable(tf.random_normal(shape=[784,10], stddev=0.01),name=\"weights\")\n",
    "b = tf.Variable(tf.zeros(shape=[1,10]), name=\"bias\")\n",
    "Z = tf.matmul(X,W) + b\n",
    "entropy = tf.nn.softmax_cross_entropy_with_logits(logits=Z,labels=Y)\n",
    "loss = tf.reduce_mean(entropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "init = tf.global_variables_initializer()\n",
    "total_correct_preds = 0\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    n_batches = int(mnist.train.num_examples/batch_size)\n",
    "    for i in range(n_epochs):\n",
    "        for _ in range(n_batches):\n",
    "            X_batch, Y_batch = mnist.train.next_batch(batch_size)\n",
    "            sess.run([optimizer,loss], feed_dict={X:X_batch, Y:Y_batch})\n",
    "    \n",
    "    n_batches = int(mnist.test.num_examples/batch_size)\n",
    "    total_correct_pred = 0\n",
    "    for i in range(n_batches):\n",
    "        X_batch, Y_batch = mnist.test.next_batch(batch_size)\n",
    "        _,batch_loss,logits=sess.run([optimizer,loss,Z],feed_dict={X:X_batch,Y:Y_batch})\n",
    "        preds = tf.nn.softmax(logits)\n",
    "        correct_preds = tf.equal(tf.argmax(preds,1), tf.argmax(Y_batch,1))\n",
    "        accuracy = sess.run(tf.reduce_mean(tf.cast(correct_prediction, tf.float32)))\n",
    "    for i in range(10):\n",
    "        plt.subplot(2, 5, i+1)\n",
    "        weight = sess.run(W)[:,i]\n",
    "        plt.title(i)\n",
    "        plt.imshow(weight.reshape([28,28]), cmap=plt.get_cmap('seismic'))\n",
    "        frame1 = plt.gca()\n",
    "        frame1.axes.get_xaxis().set_visible(False)\n",
    "        frame1.axes.get_yaxis().set_visible(False)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9399999976158142\n"
     ]
    }
   ],
   "source": [
    "#test model    \n",
    "print(\"Accuracy: {}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_weight(shape):\n",
    "    return tf.Variable(tf.random_normal(shape,stddev=0.01))\n",
    "\n",
    "def initialize_bias(shape):\n",
    "    return tf.Variable(tf.constant(0.1,shape=shape))\n",
    "\n",
    "def conv_2X2(x,w):\n",
    "    return tf.nn.conv2d(x,w,strides=[1,1,1,1],padding='SAME')\n",
    "\n",
    "def max_pool2X2(data):\n",
    "    return tf.nn.max_pool(data,ksize=[1,2,2,1],strides=[1,2,2,1],padding=\"SAME\")\n",
    "\n",
    "sess = tf.InteractiveSession()\n",
    "X= tf.placeholder(tf.float32,[None,784],name=\"image\")\n",
    "Y= tf.placeholder(tf.float32,[None,10],name=\"label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:0 train accuracy: 0.09000000357627869\n",
      "step:100 train accuracy: 0.25999999046325684\n",
      "step:200 train accuracy: 0.8199999928474426\n",
      "step:300 train accuracy: 0.9100000262260437\n",
      "step:400 train accuracy: 0.8799999952316284\n",
      "step:500 train accuracy: 0.9200000166893005\n",
      "step:600 train accuracy: 0.9399999976158142\n",
      "step:700 train accuracy: 0.9300000071525574\n",
      "step:800 train accuracy: 0.949999988079071\n",
      "step:900 train accuracy: 0.9599999785423279\n",
      "step:1000 train accuracy: 0.9100000262260437\n",
      "step:1100 train accuracy: 0.9300000071525574\n",
      "step:1200 train accuracy: 0.949999988079071\n",
      "step:1300 train accuracy: 0.9599999785423279\n",
      "step:1400 train accuracy: 0.9800000190734863\n",
      "step:1500 train accuracy: 0.949999988079071\n",
      "step:1600 train accuracy: 0.9800000190734863\n",
      "step:1700 train accuracy: 0.9700000286102295\n",
      "step:1800 train accuracy: 0.9700000286102295\n",
      "step:1900 train accuracy: 0.9700000286102295\n",
      "step:2000 train accuracy: 0.949999988079071\n",
      "step:2100 train accuracy: 0.9900000095367432\n",
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      "step:2600 train accuracy: 0.949999988079071\n",
      "step:2700 train accuracy: 0.9599999785423279\n",
      "step:2800 train accuracy: 0.9599999785423279\n",
      "step:2900 train accuracy: 0.949999988079071\n",
      "step:3000 train accuracy: 0.949999988079071\n",
      "step:3100 train accuracy: 0.9800000190734863\n",
      "step:3200 train accuracy: 0.9900000095367432\n",
      "step:3300 train accuracy: 0.9800000190734863\n",
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      "step:3500 train accuracy: 0.9800000190734863\n",
      "step:3600 train accuracy: 0.9599999785423279\n",
      "step:3700 train accuracy: 0.9800000190734863\n",
      "step:3800 train accuracy: 0.9900000095367432\n",
      "step:3900 train accuracy: 0.9700000286102295\n",
      "step:4000 train accuracy: 0.9800000190734863\n",
      "step:4100 train accuracy: 0.9599999785423279\n",
      "step:4200 train accuracy: 0.9900000095367432\n",
      "step:4300 train accuracy: 1.0\n",
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      "step:4500 train accuracy: 0.9800000190734863\n",
      "step:4600 train accuracy: 0.949999988079071\n",
      "step:4700 train accuracy: 0.9900000095367432\n",
      "step:4800 train accuracy: 1.0\n",
      "step:4900 train accuracy: 0.9700000286102295\n",
      "step:5000 train accuracy: 0.9900000095367432\n",
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      "step:5300 train accuracy: 0.9599999785423279\n",
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      "step:6000 train accuracy: 0.9900000095367432\n",
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      "step:7000 train accuracy: 0.9800000190734863\n",
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      "step:7200 train accuracy: 1.0\n",
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      "step:7900 train accuracy: 0.9900000095367432\n",
      "step:8000 train accuracy: 1.0\n",
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      "step:8300 train accuracy: 0.9900000095367432\n",
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      "step:8700 train accuracy: 0.9900000095367432\n",
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      "step:9000 train accuracy: 0.9800000190734863\n",
      "step:9100 train accuracy: 0.9900000095367432\n",
      "step:9200 train accuracy: 1.0\n",
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      "step:10000 train accuracy: 1.0\n",
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      "step:10200 train accuracy: 0.9800000190734863\n",
      "step:10300 train accuracy: 1.0\n",
      "step:10400 train accuracy: 1.0\n",
      "step:10500 train accuracy: 0.9800000190734863\n",
      "step:10600 train accuracy: 1.0\n",
      "step:10700 train accuracy: 0.9800000190734863\n",
      "step:10800 train accuracy: 0.9900000095367432\n",
      "step:10900 train accuracy: 1.0\n",
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      "step:11100 train accuracy: 1.0\n",
      "step:11200 train accuracy: 0.9900000095367432\n",
      "step:11300 train accuracy: 0.9800000190734863\n",
      "step:11400 train accuracy: 0.9900000095367432\n",
      "step:11500 train accuracy: 0.9900000095367432\n",
      "step:11600 train accuracy: 1.0\n",
      "step:11700 train accuracy: 1.0\n",
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      "step:12400 train accuracy: 0.9900000095367432\n",
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      "step:12600 train accuracy: 0.9800000190734863\n",
      "step:12700 train accuracy: 0.9900000095367432\n",
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      "step:13100 train accuracy: 1.0\n",
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      "step:14500 train accuracy: 1.0\n",
      "step:14600 train accuracy: 1.0\n",
      "step:14700 train accuracy: 1.0\n",
      "step:14800 train accuracy: 1.0\n",
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      "step:15000 train accuracy: 0.9900000095367432\n",
      "step:15100 train accuracy: 1.0\n",
      "step:15200 train accuracy: 1.0\n",
      "step:15300 train accuracy: 1.0\n",
      "step:15400 train accuracy: 1.0\n",
      "step:15500 train accuracy: 1.0\n",
      "step:15600 train accuracy: 1.0\n",
      "step:15700 train accuracy: 1.0\n",
      "step:15800 train accuracy: 1.0\n",
      "step:15900 train accuracy: 1.0\n",
      "step:16000 train accuracy: 1.0\n",
      "step:16100 train accuracy: 1.0\n",
      "step:16200 train accuracy: 1.0\n",
      "step:16300 train accuracy: 0.9900000095367432\n",
      "step:16400 train accuracy: 1.0\n",
      "step:16500 train accuracy: 1.0\n",
      "step:16600 train accuracy: 1.0\n",
      "step:16700 train accuracy: 1.0\n",
      "step:16800 train accuracy: 1.0\n",
      "step:16900 train accuracy: 1.0\n",
      "step:17000 train accuracy: 1.0\n",
      "step:17100 train accuracy: 1.0\n",
      "step:17200 train accuracy: 1.0\n",
      "step:17300 train accuracy: 1.0\n",
      "step:17400 train accuracy: 1.0\n",
      "step:17500 train accuracy: 1.0\n",
      "step:17600 train accuracy: 1.0\n",
      "step:17700 train accuracy: 1.0\n",
      "step:17800 train accuracy: 1.0\n",
      "step:17900 train accuracy: 1.0\n",
      "step:18000 train accuracy: 1.0\n",
      "step:18100 train accuracy: 1.0\n",
      "step:18200 train accuracy: 1.0\n",
      "step:18300 train accuracy: 1.0\n",
      "step:18400 train accuracy: 0.9800000190734863\n",
      "step:18500 train accuracy: 1.0\n",
      "step:18600 train accuracy: 1.0\n",
      "step:18700 train accuracy: 1.0\n",
      "step:18800 train accuracy: 1.0\n",
      "step:18900 train accuracy: 1.0\n",
      "step:19000 train accuracy: 1.0\n",
      "step:19100 train accuracy: 1.0\n",
      "step:19200 train accuracy: 0.9900000095367432\n",
      "step:19300 train accuracy: 0.9900000095367432\n",
      "step:19400 train accuracy: 1.0\n",
      "step:19500 train accuracy: 0.9900000095367432\n",
      "step:19600 train accuracy: 1.0\n",
      "step:19700 train accuracy: 1.0\n",
      "step:19800 train accuracy: 1.0\n",
      "step:19900 train accuracy: 1.0\n",
      "Test accuracy: 0.9912999868392944\n"
     ]
    }
   ],
   "source": [
    "#conv layers\n",
    "W_conv1 = initialize_weight([5,5,1,32])\n",
    "b_conv1 = initialize_bias([32])\n",
    "x_image = tf.reshape(X,[-1,28,28,1])\n",
    "h_conv1 = tf.nn.relu(conv_2X2(x_image,W_conv1) + b_conv1)\n",
    "h_pool1 = max_pool2X2(h_conv1)\n",
    "\n",
    "W_conv2 = initialize_weight([5,5,32,64])\n",
    "b_conv2 = initialize_bias([64])\n",
    "h_conv2 = tf.nn.relu(conv_2X2(h_pool1,W_conv2) + b_conv2)\n",
    "h_pool2 = max_pool2X2(h_conv2)\n",
    "\n",
    "#densely connected                     \n",
    "W_dense1 = initialize_weight([7*7*64,1024])\n",
    "b_dense1 = initialize_weight([1024])\n",
    "h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64]) \n",
    "h_dense1 = tf.nn.relu(tf.matmul(h_pool2_flat,W_dense1) + b_dense1)\n",
    "\n",
    "#dropout\n",
    "keep_prob = tf.placeholder(tf.float32)\n",
    "h_dense_drop = tf.nn.dropout(h_dense1,keep_prob=keep_prob)\n",
    "                     \n",
    "#softmax\n",
    "\n",
    "W_soft1 = initialize_weight([1024,10])\n",
    "b_soft1 = initialize_bias([10])\n",
    "y_conv1 = tf.matmul(h_dense_drop,W_soft1) + b_soft1\n",
    "\n",
    "entropy = tf.nn.softmax_cross_entropy_with_logits(labels=Y,logits=y_conv1)\n",
    "train_step = tf.train.AdamOptimizer(1e-4).minimize(entropy)\n",
    "correct_preds = tf.equal(tf.argmax(y_conv1,1), tf.argmax(Y,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_preds,tf.float32))\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "for i in range(20000):\n",
    "    X_batch, Y_batch = mnist.train.next_batch(100)\n",
    "    if i%100 == 0:\n",
    "        train_accuracy = accuracy.eval(feed_dict = {X:X_batch, Y: Y_batch,keep_prob:1.0})\n",
    "        print(\"step:{} train accuracy: {}\".format(i, train_accuracy))\n",
    "    train_step.run(feed_dict={X:X_batch, Y:Y_batch, keep_prob:0.5})\n",
    "\n",
    "print(\"Test accuracy: {}\".format(accuracy.eval(feed_dict={X:mnist.test.images,Y:mnist.test.labels,keep_prob:1.0})))\n",
    "                     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = y_conv1.eval(feed_dict={X:mnist.test.images,Y:mnist.test.labels,keep_prob:1.0})\n",
    "predict_labels = []\n",
    "for p in preds:\n",
    "    predict_labels.append(np.argmax(p))\n",
    "actual_matrix = []\n",
    "for la in mnist.test.labels:\n",
    "    actual_matrix.append(np.argmax(la))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total test samples:10000,predicted_labels:10000\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      1.00      0.99       980\n",
      "          1       0.99      1.00      1.00      1135\n",
      "          2       0.99      0.99      0.99      1032\n",
      "          3       0.99      0.99      0.99      1010\n",
      "          4       0.99      0.99      0.99       982\n",
      "          5       0.99      0.99      0.99       892\n",
      "          6       0.99      0.99      0.99       958\n",
      "          7       0.99      0.99      0.99      1028\n",
      "          8       0.99      0.99      0.99       974\n",
      "          9       0.99      0.99      0.99      1009\n",
      "\n",
      "avg / total       0.99      0.99      0.99     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Total test samples:{},predicted_labels:{}\".format(len(actual_matrix), len(predict_labels)))\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(actual_matrix,predict_labels))"
   ]
  }
 ],
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