TF10 Classification on MNIST  Restore.ipynb

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   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "tf.reset_default_graph() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, [None, 784], name='x')\n",
    "W = tf.Variable(tf.zeros([784,10]), name='W')\n",
    "y_true = tf.placeholder(tf.float32, [None, 10])\n",
    "y_pred = tf.matmul(x,W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "#softmax is good for multi class clasification\n",
    "loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_pred,labels=y_true))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "gd = tf.train.GradientDescentOptimizer(0.5).minimize(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "correct_mask = tf.equal(tf.argmax(y_pred,1) , tf.argmax(y_true,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_mask,tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Extracting D:\\tensorflow\\train-images-idx3-ubyte.gz\n",
      "Extracting D:\\tensorflow\\train-labels-idx1-ubyte.gz\n",
      "Extracting D:\\tensorflow\\t10k-images-idx3-ubyte.gz\n",
      "Extracting D:\\tensorflow\\t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "DATA_DIR = r'D:\\tensorflow'\n",
    "data = input_data.read_data_sets(DATA_DIR,one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from D:/tensorflow/mnist-model-990\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver.restore(sess,\"D:/tensorflow/mnist-model-990\")\n",
    "    ans = sess.run(accuracy, feed_dict={x:data.test.images, y_true:data.test.labels})"
   ]
  },
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   "execution_count": 10,
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   "outputs": [
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    "ans"
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": []
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