data-science-ipython-notebooks/deep-learning/tensor-flow-examples/notebooks/1_intro/basic_operations.ipynb

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    "# Basic Operations in TensorFlow\n",
    "\n",
    "Credits: Forked from [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien\n",
    "\n",
    "## Setup\n",
    "\n",
    "Refer to the [setup instructions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/Setup_TensorFlow.md)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
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    "# Basic constant operations\n",
    "# The value returned by the constructor represents the output\n",
    "# of the Constant op.\n",
    "a = tf.constant(2)\n",
    "b = tf.constant(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "text": [
      "a=2, b=3\n",
      "Addition with constants: 5\n",
      "Multiplication with constants: 6\n"
     ]
    }
   ],
   "source": [
    "# Launch the default graph.\n",
    "with tf.Session() as sess:\n",
    "    print \"a=2, b=3\"\n",
    "    print \"Addition with constants: %i\" % sess.run(a+b)\n",
    "    print \"Multiplication with constants: %i\" % sess.run(a*b)"
   ]
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    "# Basic Operations with variable as graph input\n",
    "# The value returned by the constructor represents the output\n",
    "# of the Variable op. (define as input when running session)\n",
    "# tf Graph input\n",
    "a = tf.placeholder(tf.int16)\n",
    "b = tf.placeholder(tf.int16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Define some operations\n",
    "add = tf.add(a, b)\n",
    "mul = tf.mul(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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   "outputs": [
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     "text": [
      "Addition with variables: 5\n",
      "Multiplication with variables: 6\n"
     ]
    }
   ],
   "source": [
    "# Launch the default graph.\n",
    "with tf.Session() as sess:\n",
    "    # Run every operation with variable input\n",
    "    print \"Addition with variables: %i\" % sess.run(add, feed_dict={a: 2, b: 3})\n",
    "    print \"Multiplication with variables: %i\" % sess.run(mul, feed_dict={a: 2, b: 3})"
   ]
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  {
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   "execution_count": 8,
   "metadata": {
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   "source": [
    "# ----------------\n",
    "# More in details:\n",
    "# Matrix Multiplication from TensorFlow official tutorial\n",
    "\n",
    "# Create a Constant op that produces a 1x2 matrix.  The op is\n",
    "# added as a node to the default graph.\n",
    "#\n",
    "# The value returned by the constructor represents the output\n",
    "# of the Constant op.\n",
    "matrix1 = tf.constant([[3., 3.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
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   "source": [
    "# Create another Constant that produces a 2x1 matrix.\n",
    "matrix2 = tf.constant([[2.],[2.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
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   "source": [
    "# Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.\n",
    "# The returned value, 'product', represents the result of the matrix\n",
    "# multiplication.\n",
    "product = tf.matmul(matrix1, matrix2)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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     "text": [
      "[[ 12.]]\n"
     ]
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   "source": [
    "# To run the matmul op we call the session 'run()' method, passing 'product'\n",
    "# which represents the output of the matmul op.  This indicates to the call\n",
    "# that we want to get the output of the matmul op back.\n",
    "#\n",
    "# All inputs needed by the op are run automatically by the session.  They\n",
    "# typically are run in parallel.\n",
    "#\n",
    "# The call 'run(product)' thus causes the execution of threes ops in the\n",
    "# graph: the two constants and matmul.\n",
    "#\n",
    "# The output of the op is returned in 'result' as a numpy `ndarray` object.\n",
    "with tf.Session() as sess:\n",
    "    result = sess.run(product)\n",
    "    print result"
   ]
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   "execution_count": null,
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