PyTorch Lab - 10 - Pytorch CUDA Semantics

demo10-TensorflowStaticComputationGraph.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
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   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- w is a tensorflow variable\n",
    "- x is a placeholder\n",
    "- b is a constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "W = tf.Variable(6, name = 'var_W')\n",
    "\n",
    "x = tf.placeholder(tf.int32, shape = [3] ,name = 'x')\n",
    "\n",
    "b = tf.constant(3, name = 'constant_b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'var_W_3:0' shape=() dtype=int32_ref>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'x_3:0' shape=(3,) dtype=int32>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'constant_b_3:0' shape=() dtype=int32>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = W*x + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can't see any values that these variables hold because the graph hasnt been executed yet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'add_3:0' shape=(3,) dtype=int32>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initialize global variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A Session object encapsulates the environment in which Operation objects are executed, and Tensor objects are evaluated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wx+b =  [ 63 123 183]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session( )as sess:\n",
    "    sess.run(init)\n",
    "    \n",
    "    y_result = sess.run(y, feed_dict = {x:[10, 20, 30]})\n",
    "    \n",
    "    print(\"Wx+b = \", y_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The FileWriter class provides a mechanism to create an event file in a given directory and add summaries and events to it\n",
    "graphs directory is created in the current working directory "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "writer = tf.summary.FileWriter('./graphs', sess.graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To go to the tensorboard run the following command in your Terminal\n",
    "\n",
    "#### tensorboard --logdir=graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### its a good practice to close the session and writer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "writer.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Returns the default graph for the current thread."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = tf.get_default_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<tf.Operation 'var_W/initial_value' type=Const>, <tf.Operation 'var_W' type=VariableV2>, <tf.Operation 'var_W/Assign' type=Assign>, <tf.Operation 'var_W/read' type=Identity>, <tf.Operation 'x' type=Placeholder>, <tf.Operation 'constant_b' type=Const>, <tf.Operation 'mul' type=Mul>, <tf.Operation 'add' type=Add>, <tf.Operation 'init' type=NoOp>, <tf.Operation 'var_W_1/initial_value' type=Const>, <tf.Operation 'var_W_1' type=VariableV2>, <tf.Operation 'var_W_1/Assign' type=Assign>, <tf.Operation 'var_W_1/read' type=Identity>, <tf.Operation 'x_1' type=Placeholder>, <tf.Operation 'constant_b_1' type=Const>, <tf.Operation 'mul_1' type=Mul>, <tf.Operation 'add_1' type=Add>, <tf.Operation 'init_1' type=NoOp>, <tf.Operation 'var_W_2/initial_value' type=Const>, <tf.Operation 'var_W_2' type=VariableV2>, <tf.Operation 'var_W_2/Assign' type=Assign>, <tf.Operation 'var_W_2/read' type=Identity>, <tf.Operation 'x_2' type=Placeholder>, <tf.Operation 'constant_b_2' type=Const>, <tf.Operation 'mul_2' type=Mul>, <tf.Operation 'add_2' type=Add>, <tf.Operation 'init_2' type=NoOp>, <tf.Operation 'var_W_3/initial_value' type=Const>, <tf.Operation 'var_W_3' type=VariableV2>, <tf.Operation 'var_W_3/Assign' type=Assign>, <tf.Operation 'var_W_3/read' type=Identity>, <tf.Operation 'x_3' type=Placeholder>, <tf.Operation 'constant_b_3' type=Const>, <tf.Operation 'mul_3' type=Mul>, <tf.Operation 'add_3' type=Add>, <tf.Operation 'init_3' type=NoOp>]\n"
     ]
    }
   ],
   "source": [
    "print(graph.get_operations())"
   ]
  },
  {
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