data-science-ipython-notebooks/deep-learning/tensor-flow-tutorials/1_tensorflow_basic.ipynb

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    "> This work comes from [LearningTensorFlow.com](http://learningtensorflow.com/), developed by [dataPipeline](http://datapipeline.co.au/), with whom the copyright remains.  \n",
    "For more tutorials and extended exercises, please see the website."
   ]
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   "source": [
    "# What does TensorFlow do?"
   ]
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   "source": [
    "TensorFlow is a way of representing computation without actually performing it until asked. In this sense, it is a form of lazy computing, and it allows for some great improvements to the running of code:\n",
    "\n",
    "- Faster computation of complex variables\n",
    "- Distributed computation across multiple systems, including GPUs.\n",
    "- Reduced redundency in some computations"
   ]
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   "source": [
    "Let’s have a look at this in action. First, a very basic python script:"
   ]
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      "40\n"
     ]
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    "x = 35\n",
    "y = x + 5\n",
    "print(y)"
   ]
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   "source": [
    "This script basically just says “create a variable x with value 35, set the value of a new variable y to that plus 5, which is currently 40, and print it out”. The value 40 will print out when you run this program. If you aren’t familiar with python, create a new text file called `basic_script.py`, and copy that code in. Save it on your computer and run it with:\n",
    "\n",
    "```bash\n",
    "python basic_script.py\n",
    "```\n",
    "\n",
    "Note that the path (i.e. `basic_script.py`) must reference the file, so if it is in the Code folder, you use:\n",
    "\n",
    "```\n",
    "python Code/basic_script.py\n",
    "```\n",
    "\n",
    "Also, make sure you have activated the Anaconda virtual environment. On Linux, this will make your prompt look something like:\n",
    "\n",
    "```\n",
    "(tensorenv)username@computername:~$\n",
    "```\n",
    "\n",
    "If that is working, let’s convert it to a TensorFlow equivalent."
   ]
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     "text": [
      "<tensorflow.python.ops.variables.Variable object at 0x10f2fe850>\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "x = tf.constant(35, name='x')\n",
    "y = tf.Variable(x + 5, name='y')\n",
    "\n",
    "print(y)"
   ]
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   "source": [
    "After running this, you’ll get quite a funny output, something like ```<tensorflow.python.ops.variables.Variable object at 0x10f2fe850>```. This is clearly not the value 40.\n",
    "\n",
    "The reason why, is that our program actually does something quite different to the previous one. The code here does the following:\n",
    "\n",
    "- Import the tensorflow module and call it `tf`\n",
    "- Create a constant value called x, and give it the numerical value 35\n",
    "- Create a Variable called y, and define it as being the equation x + 5\n",
    "- Print out the equation object for y\n",
    "\n",
    "The subtle difference is that y isn’t given “the current value of x + 5” as in our previous program. Instead, it is effectively an equation that means “when this variable is computed, take the value of x (as it is then) and add 5 to it”. The computation of the value of y is never actually performed in the above program.\n",
    "\n",
    "Let’s fix that:"
   ]
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    {
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     "text": [
      "40\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "x = tf.constant(35, name='x')\n",
    "y = tf.Variable(x + 5, name='y')\n",
    "\n",
    "model = tf.initialize_all_variables()\n",
    "\n",
    "with tf.Session() as session:\n",
    "    session.run(model)\n",
    "    print(session.run(y))"
   ]
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   "source": [
    "We have removed the `print(y)` statement, and instead we have code that creates a session, and actually computes the value of y. This is quite a bit of boilerplate, but it works like this:\n",
    "\n",
    "1. Import the tensorflow module and call it `tf`\n",
    "2. Create a constant value called x, and give it the numerical value 35\n",
    "3. Create a Variable called y, and define it as being the equation x + 5\n",
    "4. Initialize the variables with `initialize_all_variables` (we will go into more detail on this)\n",
    "5. Create a session for computing the values\n",
    "6. Run the model created in 4\n",
    "7. Run just the variable y and print out its current value\n",
    "\n",
    "The step 4 above is where some magic happens. In this step, a graph is created of the dependencies between the variables. In this case, the variable y depends on the variable x, and that value is transformed by adding 5 to it. Keep in mind that this value isn’t computed until step 7, as up until then, only equations and relations are computed."
   ]
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