Tensorflow  Getting Started.ipynb

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   "source": [
    "import tensorflow as tf\n",
    "import matplotlib"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "hello = tf.constant('Hello, TensorFlow!')\n",
    "sess = tf.Session()"
   ]
  },
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   "outputs": [
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     "text": [
      "b'Hello, TensorFlow!'\n"
     ]
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   "source": [
    "print(sess.run(hello))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prog 2"
   ]
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   "source": [
    "import tensorflow as tf\n",
    "\n",
    "x = tf.Variable(3, name=\"x\")\n",
    "y = tf.Variable(4, name=\"y\")\n",
    "f = x*x*y + y + 2"
   ]
  },
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     "data": {
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       "<tf.Tensor 'add_1:0' shape=() dtype=int32>"
      ]
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    "f"
   ]
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   "source": [
    "# creating session\n",
    "sess = tf.Session()"
   ]
  },
  {
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   "execution_count": 11,
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   "source": [
    "#Init the variables\n",
    "sess.run(x.initializer)"
   ]
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   "source": [
    "#Same here\n",
    "sess.run(y.initializer)"
   ]
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   "source": [
    "#evaluates\n",
    "res = sess.run(f)"
   ]
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    "res"
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   "source": [
    "sess.close()"
   ]
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   "execution_count": 14,
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   "source": [
    "with tf.Session() as sess:\n",
    "    x.initializer.run()\n",
    "    y.initializer.run()\n",
    "    result = f.eval()"
   ]
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    "result"
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   "source": [
    "init = tf.global_variables_initializer()  # prepare an init node\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()  # actually initialize all the variables\n",
    "    result = f.eval()"
   ]
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   "source": [
    "### Tensorflow executes in two phases\n",
    "* Building computation graph - construction\n",
    "* running it - execution"
   ]
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   "source": [
    "x1 = tf.Variable(1)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 19,
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     "data": {
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       "True"
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    "x1.graph is tf.get_default_graph()"
   ]
  },
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   "execution_count": 20,
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   "source": [
    "#New graph that is getting created, which is different from global space\n",
    "graph = tf.Graph()"
   ]
  },
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   "execution_count": 25,
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   "source": [
    "#whatever code you write here goes to the newly created graph 'graph'\n",
    "with graph.as_default():\n",
    "    x2 = tf.Variable(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
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     "data": {
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       "True"
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    "x2.graph is graph"
   ]
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   "source": [
    "#checking wether newly created graph is part of default graph\n",
    "x2.graph is tf.get_default_graph()"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "### Lifecycle of a Node value\n",
    "When you evaluate a node, Tensorflow automatically determines the set of nodes that it depends on & it evaluates these nodes first"
   ]
  },
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     "text": [
      "10\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "w = tf.constant(3)\n",
    "x = w + 2\n",
    "y = x + 5\n",
    "z = x * 3\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    print(y.eval())  \n",
    "    # 10 - At this stage, tensorflow identifies that it is \n",
    "    # dependent on x & x is dependent on w \n",
    "    print(z.eval())  # 15"
   ]
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     "text": [
      "10\n",
      "15\n"
     ]
    }
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   "source": [
    "## Passing multiple nodes for running\n",
    "with tf.Session() as sess:\n",
    "    y_val, z_val = sess.run([y, z])\n",
    "    print(y_val)  # 10\n",
    "    print(z_val)  # 15"
   ]
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