Ch2 Intro to Tensorflow/006 - Tensorflow Softmax Regression.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 numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)"
   ]
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   "source": [
    "x = tf.placeholder(tf.float32)\n",
    "\n",
    "W = tf.Variable(tf.zeros([784, 10]))\n",
    "b = tf.Variable(tf.zeros([10]))\n",
    "\n",
    "y = tf.nn.softmax(tf.matmul(x, W) + b)\n",
    "y_ = tf.placeholder(tf.float32, [None, 10])\n",
    "\n",
    "cross_entropy = tf.reduce_mean(\n",
    "    - tf.reduce_sum(\n",
    "        y_ * tf.log(y),\n",
    "        reduction_indices = [1]\n",
    "    )\n",
    ")\n",
    "\n",
    "train_step = tf.train\\\n",
    "    .GradientDescentOptimizer(0.5)\\\n",
    "    .minimize(cross_entropy)\n",
    "    \n",
    "init = tf.initialize_all_variables()\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(init)\n",
    "\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})"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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   "outputs": [],
   "source": [
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "0.9139\n"
     ]
    }
   ],
   "source": [
    "print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "What are the digits that this Softmax Regression model can't recorgnize?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    ""
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