Untitled1.ipynb

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   "source": [
    "import tensorflow as tf"
   ]
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   "cell_type": "code",
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   "source": [
    "a = tf.constant(1)\n",
    "b = tf.constant(2)"
   ]
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   "outputs": [],
   "source": [
    "c = a + b\n",
    "d = a * b"
   ]
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   "execution_count": 8,
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   "source": [
    "V1 = tf.constant([1.,2.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensorflow.python.framework.ops.Tensor"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(V1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "V2 = tf.constant([3.,4.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "V3 = V1 + V2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "M = tf.constant([[1.,2.],[3.,4.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "NN = tf.matmul(M,M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  7.,  10.],\n",
       "       [ 15.,  22.]], dtype=float32)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(NN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = tf.Variable(0, name=\"weight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<contextlib._GeneratorContextManager at 0x10e91518f28>"
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     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "init_op = tf.global_variables_initializer()\n",
    "sess.run(init_op)"
   ]
  },
  {
   "cell_type": "code",
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       "2"
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     "execution_count": 57,
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   "source": [
    "w.eval(session=sess)"
   ]
  },
  {
   "cell_type": "code",
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    "collapsed": true
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   "outputs": [],
   "source": [
    "w += a"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
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    {
     "data": {
      "text/plain": [
       "3"
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     "execution_count": 59,
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   "source": [
    "w.eval(session=sess)"
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   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
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   "outputs": [],
   "source": [
    "E = d + b "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "E.eval(session=sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 2]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run([E,d])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression \n",
    "* Classifying images of characters by font\n",
    "* Using logistic regression to \"score\" each class\n",
    "* Building a logistic regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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