Tensorflow_Fundamentals.ipynb

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      "name": "Tensorflow Fundamentals.ipynb",
      "version": "0.3.2",
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      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AtharKharal/Tensorflow_Python_GPUsTPUs/blob/master/Tensorflow_Fundamentals.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "WArtF1XeE5lm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Loading Tensorflow"
      ]
    },
    {
      "metadata": {
        "id": "Ii14zuo9ELOe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uxTC8X0nFB-m",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Basic Tensor Constructions"
      ]
    },
    {
      "metadata": {
        "id": "bvzFE0SWE299",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "t1= tf.constant([1,2,3.3])\n",
        "t2= tf.constant([10,20,30])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "s-P8OeY2Ezco",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "t3= tf.range(1,10,.3)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YVWUcn8SFe8L",
        "colab_type": "code",
        "outputId": "432ea973-7dac-4b44-c0d6-8d7c0ca4616c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "t1.get_shape()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([Dimension(3)])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "FYhmTUg1ced9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Creating Operations"
      ]
    },
    {
      "metadata": {
        "id": "f8tC5vzVIV5P",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "a = tf.constant([3., 3., 3.])\n",
        "b = tf.constant([2., 2., 2.])\n",
        "sm = tf.add(a, b)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HB6X7gGtJX1S",
        "colab_type": "code",
        "outputId": "377027c7-27ee-4971-985a-8c5cebb151be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "dv= tf.div(a,b)\n",
        "dv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'div:0' shape=(3,) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "Sci_lZgucyl0",
        "colab_type": "code",
        "outputId": "70c6c221-116e-46af-aa51-78794fafb9f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "tf.subtract(a,b)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'Sub:0' shape=(3,) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "metadata": {
        "id": "kHjdwB0zeZbE",
        "colab_type": "code",
        "outputId": "79e9b2dd-0df2-4741-f978-02384b0a54bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "with tf.Session() as sess:\n",
        "    print(\"Sum : \", sess.run(sm))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sum :  [5. 5. 5.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "z5pyE2RVhCoq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Graphs"
      ]
    },
    {
      "metadata": {
        "id": "wbAyQGLdenUj",
        "colab_type": "code",
        "outputId": "d2df28a5-261a-4625-d1f6-37c10b6f1827",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "graph= tf.get_default_graph()\n",
        "graph"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.framework.ops.Graph at 0x7fb5b4752a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "metadata": {
        "id": "T1JIIA6-h7jp",
        "colab_type": "code",
        "outputId": "5af27098-a223-4617-e03a-4c89968bda79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "newgraph= tf.Graph()\n",
        "newgraph"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.framework.ops.Graph at 0x7fb5b3c667f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "metadata": {
        "id": "MvJlDff_j6od",
        "colab_type": "code",
        "outputId": "69d7a34c-d8cc-4c7d-c168-862e17926ad1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "newgraph.get_operations()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "metadata": {
        "id": "iHKaTY-OkAOw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "a=tf.constant(2.5,name='first_val')\n",
        "b=tf.constant(4.5,name='second_val')\n",
        "sm=a+b\n",
        "print(tf.get_default_graph().get_operations())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xQshiOV8fr3S",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Linear Regression: the TF way"
      ]
    },
    {
      "metadata": {
        "id": "Lf3B4lBpf5UA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We start by loading the necessary libraries, creating a graph, and loading the data:"
      ]
    },
    {
      "metadata": {
        "id": "qKnZ1huHfzYo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from sklearn import datasets\n",
        "from tensorflow.python.framework import ops\n",
        "ops.reset_default_graph()\n",
        "sess = tf.Session()\n",
        "iris = datasets.load_iris()\n",
        "x_vals = np.array([x[3] for x in iris.data])\n",
        "y_vals = np.array([y[0] for y in iris.data])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jhX2PGCegVnQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We then declare our learning rate, batch size, placeholders, and model variables:"
      ]
    },
    {
      "metadata": {
        "id": "gDk_fBbqgYjQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learning_rate = 0.05\n",
        "batch_size = 25\n",
        "x_data = tf.placeholder(shape=[None, 1], dtype=tf.float32)\n",
        "y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)\n",
        "A = tf.Variable(tf.random_normal(shape=[1,1]))\n",
        "b = tf.Variable(tf.random_normal(shape=[1,1]))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zpJS3ggSgdpI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Next, we write the formula for the linear model, y=Ax+b:"
      ]
    },
    {
      "metadata": {
        "id": "gx-j03hfgeyA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model_output = tf.add(tf.matmul(x_data, A), b)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2XyEmDpCghp4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Then we declare our L2 loss function (which includes the mean over the batch), initialize the variables, and declare our optimizer. Note that we chose 0.05 as our learning rate:"
      ]
    },
    {
      "metadata": {
        "id": "irbkfJJ7gmU9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "loss = tf.reduce_mean(tf.square(y_target - model_output))\n",
        "init = tf.global_variables_initializer()\n",
        "sess.run(init)\n",
        "my_opt = tf.train.GradientDescentOptimizer(learning_rate)\n",
        "train_step = my_opt.minimize(loss)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mHZiZMzWgopI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We can now loop through and train the model on randomly selected batches. We will\n",
        "run it for 100 loops and print out the variable and loss values every 25 iterations.\n",
        "Note that here we are also saving the loss of every iteration so that we can view it\n",
        "afterwards:"
      ]
    },
    {
      "metadata": {
        "id": "EveBRb7xgs65",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        },
        "outputId": "f137aaa2-33ac-4e66-807e-f2f33d622d69"
      },
      "cell_type": "code",
      "source": [
        "loss_vec = []\n",
        "for i in range(100):\n",
        "    rand_index = np.random.choice(len(x_vals), size=batch_size)\n",
        "    rand_x = np.transpose([x_vals[rand_index]])\n",
        "    rand_y = np.transpose([y_vals[rand_index]])\n",
        "    sess.run(train_step, feed_dict={x_data: rand_x, y_target:\n",
        "    rand_y})\n",
        "    temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})\n",
        "    loss_vec.append(temp_loss)\n",
        "    if (i+1)%25==0:\n",
        "        print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)) + 'b = ' + str(sess.run(b)))\n",
        "        print('Loss = ''' + str(temp_loss))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Step #25 A = [[0.9475999]]b = [[4.718447]]\n",
            "Loss = 0.24975508\n",
            "Step #50 A = [[0.90370077]]b = [[4.73804]]\n",
            "Loss = 0.2348233\n",
            "Step #75 A = [[0.8829504]]b = [[4.741434]]\n",
            "Loss = 0.16852891\n",
            "Step #100 A = [[0.9232345]]b = [[4.7770777]]\n",
            "Loss = 0.2612387\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "NOrqAPfxg0Th",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Next we will extract the coefficients we found and create a best-fit line to put in the\n",
        "graph:"
      ]
    },
    {
      "metadata": {
        "id": "7z_XsyqHg1A_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "[slope] = sess.run(A)\n",
        "[y_intercept] = sess.run(b)\n",
        "best_fit = []\n",
        "for i in x_vals: \n",
        "    best_fit.append(slope*i+y_intercept)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mFM3ktzAg5CI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Here we will create two plots. The first will be the data with the found line overlaid.\n",
        "The second is the L2 loss function over the 100 iterations:"
      ]
    },
    {
      "metadata": {
        "id": "AcBdWD4Ug72P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "outputId": "7696138d-b44d-4e2c-c621-9b97946d6281"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(x_vals, y_vals, 'o', label='Data Points')\n",
        "plt.plot(x_vals, best_fit, 'r-', label='Best fit line',linewidth=3)\n",
        "plt.legend(loc='upper left')\n",
        "plt.title('Sepal Length vs Pedal Width')\n",
        "plt.xlabel('Pedal Width')\n",
        "plt.ylabel('Sepal Length')\n",
        "plt.show()\n",
        "plt.plot(loss_vec, 'k-')\n",
        "plt.title('L2 Loss per Generation')\n",
        "plt.xlabel('Generation')\n",
        "plt.ylabel('L2 Loss')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eeBw9dg1v/voJOvx90uTYrCWxyB85xipxWQuLNxFJlqlecRZv27H1dnE5dgRT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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fe7ffa3b390>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wDIUaZK+tRDoOu4S6qKnzQt2sXz2XyyGVSmFgoJ9M9xJIqNcQO8zvbIIoN3c1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\nxNRVQ+5ET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n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fe7ff9f7dd8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}