2. TensorFlow MNIST(0,1,2,3,4,5,6,7,8,9) Image detection "Single Layer Perceptron".ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
      "0.9127\n"
     ]
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets('MNIST_data',one_hot=True)\n",
    "\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "# None defines first dimension corresponding to batch size \n",
    "# 28*28 defines dimensionality of 28x28 pixel MNIST image\n",
    "x = tf.placeholder(tf.float32, shape=[None,28*28])\n",
    "y = tf.placeholder(tf.float32, shape=[None,10])\n",
    "\n",
    "w = tf.Variable(tf.zeros([784,10]))\n",
    "b = tf.Variable(tf.zeros([10]))\n",
    "\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "y_ = tf.matmul(x,w) + b\n",
    "\n",
    "cross_entropy = tf.reduce_mean(\n",
    "    tf.nn.softmax_cross_entropy_with_logits(labels = y, logits = y_)\n",
    ")\n",
    "\n",
    "train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\n",
    "\n",
    "for _ in range(1000):\n",
    "    batch = mnist.train.next_batch(100)\n",
    "    train_step.run(feed_dict={x: batch[0],y:batch[1]})\n",
    "\n",
    "correct_prediction = tf.equal(tf.argmax(y_,1),tf.argmax(y,1))\n",
    "\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))\n",
    "\n",
    "# feed in test data to see the accuracy\n",
    "\n",
    "print(accuracy.eval(feed_dict={x:mnist.test.images,y:mnist.test.labels}))\n",
    "\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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