digit1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "ld_v = tf.logging.get_verbosity()\n",
    "tf.logging.set_verbosity(tf.logging.ERROR)\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": 21,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp=784\n",
    "hidden=500\n",
    "out=10\n",
    "x=tf.placeholder(tf.float32,[None,inp])\n",
    "w1=tf.Variable(tf.zeros([inp,hidden]))\n",
    "b1=tf.Variable(tf.zeros([hidden]))\n",
    "h=tf.nn.softmax(tf.matmul(x,w1)+b1)\n",
    "w2=tf.Variable(tf.zeros([hidden,out]))\n",
    "b2=tf.Variable(tf.zeros([out]))\n",
    "y=tf.nn.softmax(tf.matmul(h,w2)+b2)\n",
    "y_=tf.placeholder(tf.float32,[None,10])\n",
    "cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))\n",
    "train_step=tf.train.GradientDescentOptimizer(0.001).minimize(cross_entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1955\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    tf.global_variables_initializer().run()\n",
    "    for i in range(1000):\n",
    "        batch_xs,batch_ys=mnist.train.next_batch(100)\n",
    "        sess.run(train_step,feed_dict={x:batch_xs,y_:batch_ys})\n",
    "    \n",
    "    correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "    print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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