Number Recognition MNIST.ipynb

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     "text": [
      "/Users/carlicema/Documents/My_Studio/Anaconda/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    },
    {
     "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": [
    "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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55000, 784)\n",
      "(55000, 10)\n",
      "(10000, 784)\n",
      "(10000, 10)\n"
     ]
    }
   ],
   "source": [
    "print (mnist.train.images.shape)\n",
    "print (mnist.train.labels.shape)\n",
    "print (mnist.test.images.shape)\n",
    "print (mnist.test.labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting memory: 692895744\n",
      "0.9196\n",
      "639 µs ± 2.05 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "Memory Used in MB: 3.776512\n"
     ]
    }
   ],
   "source": [
    "import resource\n",
    "start_mem = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss\n",
    "print (\"Starting memory:\", start_mem)\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "# model parameters\n",
    "W = tf.Variable(tf.zeros([784, 10]))\n",
    "b = tf.Variable(tf.zeros([10]))\n",
    "\n",
    "# input and output for softmax regression\n",
    "x = tf.placeholder(tf.float32, [None, 784])\n",
    "y_ = tf.placeholder(tf.float32, [None, 10])\n",
    "y = tf.nn.softmax(tf.matmul(x, W) + b)\n",
    "\n",
    "# cross-entropy (loss)\n",
    "cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))\n",
    "\n",
    "# optimizer\n",
    "train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "# training loop\n",
    "tf.global_variables_initializer().run()\n",
    "for _ 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",
    "# accuracy\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",
    "time = %timeit sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\n",
    "\n",
    "mem_used = (resource.getrusage(resource.RUSAGE_SELF).ru_maxrss - start_mem)/1000000\n",
    "print (\"Memory Used in MB:\", mem_used)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory for 60B images in TB: 4.3057152\n"
     ]
    }
   ],
   "source": [
    "#Memory for 60B Images:\n",
    "mem_60B = mem_used*60000000000/50000\n",
    "print (\"Memory for 60B images in TB:\", mem_60B/1000000)\n",
    "\n",
    "#Time for 60B Images:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to process 60B images: 6.390000000000001\n"
     ]
    }
   ],
   "source": [
    "#Time for 60B\n",
    "\n",
    "Time_seconds = 639/1000000*600000\n",
    "Time_minute = Time_seconds/60\n",
    "\n",
    "print(\"Time taken to process 60B images:\", Time_minute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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