01_TF_MNIST_BASIC.ipynb

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     "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": [
    "# import  input_data and read the data set\n",
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n"
   ]
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    "# Define the tensor model\n",
    "\n",
    "# x - the input variable - 28X28=784 vector\n",
    "x = tf.placeholder(tf.float32, [None, 784])\n",
    "# w - the weights to learn - inputting 784 and outputing 10 weights, one per each of the identified digits\n",
    "W = tf.Variable(tf.zeros([784, 10]))\n",
    "# b - the bias to learn - per 10 output digits\n",
    "b = tf.Variable(tf.zeros([10]))\n",
    "# y is the model we try to learn\n",
    "y = tf.nn.softmax(tf.matmul(x, W) + b)\n",
    "# y_ is the \"real\" target\n",
    "y_ = tf.placeholder(tf.float32, [None, 10])\n",
    "\n",
    "# Defining the loss function and attempting to minimize it\n",
    "cross_entropy = tf.reduce_mean(\n",
    "  tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))\n",
    "# cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))\n",
    "\n",
    "# Workign with the gradient descent algorithm and setting the learning rate (=.5)\n",
    "train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)"
   ]
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   "source": [
    "# Train the model\n",
    "\n",
    "# open a TF interactive session and initialize the variables\n",
    "sess = tf.InteractiveSession()\n",
    "tf.initialize_all_variables().run() # for older version of TF\n",
    "# tf.global_variables_initializer().run() # for newer version of TF\n",
    "\n",
    "# running the loop of training steps with batches from the input\n",
    "for _ in range(10000):\n",
    "  batch_xs, batch_ys = mnist.train.next_batch(100)\n",
    "  sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\n"
   ]
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      "0.9241\n"
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   "source": [
    "# Check the accuracy of the model on the test set\n",
    "\n",
    "# Compare the prediction with the real values\n",
    "correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))\n",
    "\n",
    "# calculate the ratio of the correct predictions\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "\n",
    "# finally - run the trained model on the test set of images and labels and output the accuracy\n",
    "print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))"
   ]
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     "data": {
      "text/plain": [
       "Datasets(train=<tensorflow.contrib.learn.python.learn.datasets.mnist.DataSet object at 0x102dea3c8>, validation=<tensorflow.contrib.learn.python.learn.datasets.mnist.DataSet object at 0x114f1d668>, test=<tensorflow.contrib.learn.python.learn.datasets.mnist.DataSet object at 0x1123c78d0>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "mnist"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
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       "<tensorflow.contrib.learn.python.learn.datasets.mnist.DataSet at 0x102dea3c8>"
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     "execution_count": 9,
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   ],
   "source": [
    "mnist.train"
   ]
  }
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