tensorflow_basic_operation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Basic Operations example using TensorFlow library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% tf.Graph represents a collection of tf.Operations\n",
    "# You can create operations by writing out equations.\n",
    "# By default, there is a graph: tf.get_default_graph()\n",
    "# and any new operations are added to this graph.\n",
    "# The result of a tf.Operation is a tf.Tensor, which holds\n",
    "# the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% First a tf.Tensor\n",
    "n_values = 32\n",
    "x = tf.linspace(-3.0, 3.0, n_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.         -2.80645156 -2.61290312 -2.41935492 -2.22580647 -2.03225803\n",
      " -1.83870971 -1.64516139 -1.45161295 -1.25806451 -1.06451619 -0.87096786\n",
      " -0.67741942 -0.48387098 -0.29032278 -0.09677434  0.0967741   0.29032254\n",
      "  0.48387098  0.67741919  0.87096763  1.06451607  1.25806427  1.45161295\n",
      "  1.64516115  1.83870935  2.03225803  2.22580624  2.41935444  2.61290312\n",
      "  2.80645132  3.        ]\n"
     ]
    }
   ],
   "source": [
    "# %% Construct a tf.Session to execute the graph.\n",
    "sess = tf.Session()\n",
    "result = sess.run(x)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.        , -2.80645156, -2.61290312, -2.41935492, -2.22580647,\n",
       "       -2.03225803, -1.83870971, -1.64516139, -1.45161295, -1.25806451,\n",
       "       -1.06451619, -0.87096786, -0.67741942, -0.48387098, -0.29032278,\n",
       "       -0.09677434,  0.0967741 ,  0.29032254,  0.48387098,  0.67741919,\n",
       "        0.87096763,  1.06451607,  1.25806427,  1.45161295,  1.64516115,\n",
       "        1.83870935,  2.03225803,  2.22580624,  2.41935444,  2.61290312,\n",
       "        2.80645132,  3.        ], dtype=float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %% Alternatively pass a session to the eval fn:\n",
    "x.eval(session=sess)\n",
    "# x.eval() does not work, as it requires a session!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% We can setup an interactive session if we don't\n",
    "# want to keep passing the session around:\n",
    "sess.close()\n",
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.        , -2.80645156, -2.61290312, -2.41935492, -2.22580647,\n",
       "       -2.03225803, -1.83870971, -1.64516139, -1.45161295, -1.25806451,\n",
       "       -1.06451619, -0.87096786, -0.67741942, -0.48387098, -0.29032278,\n",
       "       -0.09677434,  0.0967741 ,  0.29032254,  0.48387098,  0.67741919,\n",
       "        0.87096763,  1.06451607,  1.25806427,  1.45161295,  1.64516115,\n",
       "        1.83870935,  2.03225803,  2.22580624,  2.41935444,  2.61290312,\n",
       "        2.80645132,  3.        ], dtype=float32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %% Now this will work!\n",
    "x.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% Now a tf.Operation\n",
    "# We'll use our values from [-3, 3] to create a Gaussian Distribution\n",
    "sigma = 1.0\n",
    "mean = 0.0\n",
    "z = (tf.exp(tf.neg(tf.pow(x - mean, 2.0) /\n",
    "                   (2.0 * tf.pow(sigma, 2.0)))) *\n",
    "     (1.0 / (sigma * tf.sqrt(2.0 * 3.1415))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# %% By default, new operations are added to the default Graph\n",
    "assert z.graph is tf.get_default_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x18fbac55898>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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30y+4AFq0CJ0ms+2zD7Rvr+YNkWQScyFhZl2A+4AhQGtgITDLzJpX8pa1wENAW+BQ4O/A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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18fb64dc6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %% Execute the graph and plot the result\n",
    "plt.plot(z.eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32,)\n"
     ]
    }
   ],
   "source": [
    "# %% We can find out the shape of a tensor like so:\n",
    "print(z.get_shape())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[32]\n"
     ]
    }
   ],
   "source": [
    "# %% Or in a more friendly format\n",
    "print(z.get_shape().as_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[32]\n"
     ]
    }
   ],
   "source": [
    "# %% Sometimes we may not know the shape of a tensor\n",
    "# until it is computed in the graph.  In that case\n",
    "# we should use the tf.shape fn, which will return a\n",
    "# Tensor which can be eval'ed, rather than a discrete\n",
    "# value of tf.Dimension\n",
    "print(tf.shape(z).eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[32]\n",
      " [32]\n",
      " [ 3]\n",
      " [ 4]]\n"
     ]
    }
   ],
   "source": [
    "# %% We can combine tensors like so:\n",
    "print(tf.pack([tf.shape(z), tf.shape(z), [3], [4]]).eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 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": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a=2 --> a=2\n",
      "b=3 --> 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 --> a=%i'%a.eval())\n",
    "    print('b=3 --> b=%i'%b.eval())\n",
    "    print (\"Addition with constants: %i\" % sess.run(a+b))\n",
    "    print (\"Multiplication with constants: %i\" % sess.run(a*b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 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": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define some operations\n",
    "add = tf.add(a, b)\n",
    "mul = tf.mul(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Const_2:0\", shape=(1, 2), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "print(matrix1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create another Constant that produces a 2x1 matrix.\n",
    "matrix2 = tf.constant([[2.],[2.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 12.]]\n"
     ]
    }
   ],
   "source": [
    "# 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% Let's multiply the two to get a 2d gaussian\n",
    "z_2d = tf.matmul(tf.reshape(z, [n_values, 1]), tf.reshape(z, [1, n_values]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x18fbc1419e8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1PFluhZFtRkINxB50D64jsw9deUTxeq/7Oq4B9+TkddUy+3lqmSQv758mLsdD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Ll0C7YlAFvgTYG1BOEWqNaAWB8HlV4TWsecl7W2E07Jrbj9KXjn7i2bhuqSY+INqnVJ5BTxEpPg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\n5gNQTT530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      "text/plain": [
       "<matplotlib.figure.Figure at 0x18fbac2ee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %% Execute the graph and store the value that `out` represents in `result`.\n",
    "plt.imshow(z_2d.eval())"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}