logistic_regression.ipynb

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   "source": [
    "# Logistic Regression Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MNIST Dataset Overview\n",
    "\n",
    "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n",
    "\n",
    "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n",
    "\n",
    "More info: http://yann.lecun.com/exdb/mnist/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-1-c8f2c6a0ea7f>:5: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "WARNING:tensorflow:From C:\\Users\\iD Student\\AppData\\Local\\conda\\conda\\envs\\tensorflow-env\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please write your own downloading logic.\n",
      "WARNING:tensorflow:From C:\\Users\\iD Student\\AppData\\Local\\conda\\conda\\envs\\tensorflow-env\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
      "WARNING:tensorflow:From C:\\Users\\iD Student\\AppData\\Local\\conda\\conda\\envs\\tensorflow-env\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From C:\\Users\\iD Student\\AppData\\Local\\conda\\conda\\envs\\tensorflow-env\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.one_hot on tensors.\n",
      "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
      "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From C:\\Users\\iD Student\\AppData\\Local\\conda\\conda\\envs\\tensorflow-env\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# Import MINST data\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55000, 784) (55000, 10) (10000, 784) (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "x_train = mnist.train.images\n",
    "y_train = mnist.train.labels\n",
    "\n",
    "x_test = mnist.test.images\n",
    "y_test = mnist.test.labels\n",
    "\n",
    "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-3-63c4c0faeb48>:19: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Parameters\n",
    "learning_rate = 0.01\n",
    "training_epochs = 800\n",
    "batch_size = 100\n",
    "display_step = 20\n",
    "\n",
    "# tf Graph Input\n",
    "x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784\n",
    "y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes\n",
    "\n",
    "# Set model weights\n",
    "W = tf.Variable(tf.zeros([784, 10]))\n",
    "b = tf.Variable(tf.zeros([10]))\n",
    "\n",
    "# Construct model\n",
    "pred = tf.matmul(x, W) + b \n",
    "\n",
    "# Minimize error using cross entropy\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n",
    "# Gradient Descent\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n",
    "\n",
    "# Initialize the variables (i.e. assign their default value)\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 20 Cost: 2.1131622791290283\n",
      "Epoch: 40 Cost: 1.942527174949646\n",
      "Epoch: 60 Cost: 1.7957216501235962\n",
      "Epoch: 80 Cost: 1.6693480014801025\n",
      "Epoch: 100 Cost: 1.560498833656311\n",
      "Epoch: 120 Cost: 1.4665437936782837\n",
      "Epoch: 140 Cost: 1.3851532936096191\n",
      "Epoch: 160 Cost: 1.3143187761306763\n",
      "Epoch: 180 Cost: 1.252343773841858\n",
      "Epoch: 200 Cost: 1.1978145837783813\n",
      "Epoch: 220 Cost: 1.149562954902649\n",
      "Epoch: 240 Cost: 1.1066257953643799\n",
      "Epoch: 260 Cost: 1.068210482597351\n",
      "Epoch: 280 Cost: 1.0336627960205078\n",
      "Epoch: 300 Cost: 1.002441167831421\n",
      "Epoch: 320 Cost: 0.9740954041481018\n",
      "Epoch: 340 Cost: 0.9482496976852417\n",
      "Epoch: 360 Cost: 0.9245885610580444\n",
      "Epoch: 380 Cost: 0.9028457403182983\n",
      "Epoch: 400 Cost: 0.882795512676239\n",
      "Epoch: 420 Cost: 0.8642454743385315\n",
      "Epoch: 440 Cost: 0.8470308184623718\n",
      "Epoch: 460 Cost: 0.8310095071792603\n",
      "Epoch: 480 Cost: 0.8160591125488281\n",
      "Epoch: 500 Cost: 0.8020728230476379\n",
      "Epoch: 520 Cost: 0.7889578342437744\n",
      "Epoch: 540 Cost: 0.7766326665878296\n",
      "Epoch: 560 Cost: 0.7650257349014282\n",
      "Epoch: 580 Cost: 0.7540738582611084\n",
      "Epoch: 600 Cost: 0.7437210083007812\n",
      "Epoch: 620 Cost: 0.7339175343513489\n",
      "Epoch: 640 Cost: 0.7246190309524536\n",
      "Epoch: 660 Cost: 0.7157857418060303\n",
      "Epoch: 680 Cost: 0.7073822021484375\n",
      "Epoch: 700 Cost: 0.6993762850761414\n",
      "Epoch: 720 Cost: 0.6917390823364258\n",
      "Epoch: 740 Cost: 0.6844446063041687\n",
      "Epoch: 760 Cost: 0.67746901512146\n",
      "Epoch: 780 Cost: 0.6707908511161804\n",
      "Epoch: 800 Cost: 0.6643905639648438\n",
      "Optimization Finished!\n",
      "Accuracy: 0.8339999914169312\n"
     ]
    }
   ],
   "source": [
    "# Start training\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "\n",
    "    # Training cycle\n",
    "    for epoch in range(training_epochs):\n",
    "        o, c = sess.run([optimizer, cost], feed_dict={x: x_train,\n",
    "                                                          y: y_train})\n",
    "\n",
    "        # Display logs per epoch step\n",
    "        if (epoch+1) % display_step == 0:\n",
    "            print(\"Epoch: {} Cost: {}\".format(epoch+1, c))\n",
    "\n",
    "    print(\"Optimization Finished!\")\n",
    "\n",
    "    # Test model\n",
    "    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n",
    "    # Calculate accuracy for 3000 examples\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "    print(\n",
    "        \"Accuracy: {}\".format(\n",
    "            sess.run(accuracy, feed_dict={x: mnist.test.images[:3000], y: mnist.test.labels[:3000]})\n",
    "        )\n",
    "    )"
   ]
  }
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