Tensorflow_Intro.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Tensorflow-Intro.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOsLZjdmhfwPb4UuhNhLvUY",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Pepcoders/Data-Science-February/blob/main/Tensorflow_Intro.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "jPzd2Hp8FpsH"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tf.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ciW5a_skHtOw",
        "outputId": "976857a8-a745-4dc0-f0d7-9d7a44d954a9"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.8.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "0Y8fWuLEHvFR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "==> Tensorflow (Basic Functions)\n",
        "1. ANN Build\n",
        "2. How to select number of hidden layers and neurons\n",
        "3. Keras-Tuner (DL=>Randomized Search CV)\n",
        " "
      ],
      "metadata": {
        "id": "73BwATNsIIc5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "print(\"TensorFlow version: {}\".format(tf.__version__))\n",
        "print(\"Keras version: {}\".format(tf.keras.__version__))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sxCVMUYOIg1q",
        "outputId": "6c8b1429-0d3b-4df3-9347-f1be72cf39f1"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TensorFlow version: 2.8.2\n",
            "Keras version: 2.8.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.test.is_gpu_available()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qixnS6sIJGQo",
        "outputId": "ab9730e4-ca70-4468-831b-7883f36928b4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:From <ipython-input-4-17bb7203622b>:1: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.config.list_physical_devices('GPU')` instead.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k=tf.constant(42)"
      ],
      "metadata": {
        "id": "U0hoVyeKJf1G"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "k=tf.constant(1, dtype = tf.int64)"
      ],
      "metadata": {
        "id": "7tzDsLgIJ6fz"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "k.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LAc_gL8rJ8-I",
        "outputId": "6bf9b492-bddb-46b5-fa1b-382f03676c75"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=tf.constant([[4,2],[9,5]])"
      ],
      "metadata": {
        "id": "3KjvYDj6KHJE"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "b=tf.constant(a)"
      ],
      "metadata": {
        "id": "uweEavUpKKLN"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(tf.ones(shape=(2,3)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EwBLOpubKOjz",
        "outputId": "a29df96c-e257-4391-f332-f7def0a043dd"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[1. 1. 1.]\n",
            " [1. 1. 1.]], shape=(2, 3), dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tf.zeros(shape=(3,2)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KiRI444ZKW3x",
        "outputId": "270842aa-41dc-45c2-d437-b34828a064d9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[0. 0.]\n",
            " [0. 0.]\n",
            " [0. 0.]], shape=(3, 2), dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a1 = tf.constant([[3,4,5], [3,4,5]])\n",
        "a2= tf.constant([[1,2,3], [1,2,3]])\n",
        "\n",
        "c=tf.add(a1,a2)\n"
      ],
      "metadata": {
        "id": "zdnqNTx3Kqrg"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "c"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XP9ooVTpLFBA",
        "outputId": "0dfcedb6-3a61-4c1d-f2ce-ffb567772f4b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 3), dtype=int32, numpy=\n",
              "array([[4, 6, 8],\n",
              "       [4, 6, 8]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.normal(shape=(2,2),mean=0,stddev=1.0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mGTqAbUXLFqi",
        "outputId": "3ad0137e-97a7-4759-f62a-71c05637076f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
              "array([[-0.57768255,  0.6809504 ],\n",
              "       [-0.44325253, -0.33938992]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.uniform(shape=(2,2),minval=0,maxval=10,dtype=tf.int32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lTInbTS7LQSd",
        "outputId": "375ebad5-f364-46e5-d9b1-b8f78b9a34e8"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 2), dtype=int32, numpy=\n",
              "array([[8, 5],\n",
              "       [1, 4]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=tf.Variable(23)"
      ],
      "metadata": {
        "id": "jcKY8ygKLZtC"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8XwZ_EEFLsob",
        "outputId": "6e7f2f59-901b-4c7b-8fd4-4f4ac6ce57f8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'Variable:0' shape=() dtype=int32, numpy=23>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a2=tf.Variable([23])"
      ],
      "metadata": {
        "id": "E3uQ3xw0LtUs"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GU0_wDCLLxbh",
        "outputId": "49b34c0d-be69-4eb3-c3dd-55533ddf3bea"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'Variable:0' shape=(1,) dtype=int32, numpy=array([23], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tensor = tf.Variable([ [ [10., 11., 12.], [13., 14., 15.] ], [ [16., 17., 18.], [19., 20., 21.] ] ]) # tensor variable\n",
        "print(tensor.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZhF9IpI0L0av",
        "outputId": "a040d143-262a-482d-a99d-d50161025898"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 2, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tensor1 = tf.reshape(tensor,[2,6])"
      ],
      "metadata": {
        "id": "I0tOw5-XMDUK"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tensor1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dgr7dM0gMH_c",
        "outputId": "cf7a1c7e-20c3-46fd-aa72-6d58195e471e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 6), dtype=float32, numpy=\n",
              "array([[10., 11., 12., 13., 14., 15.],\n",
              "       [16., 17., 18., 19., 20., 21.]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tensor1.numpy()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LsEFXrTVMJEL",
        "outputId": "55f37840-ef85-48b5-9a3c-b84d2a513596"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10., 11., 12., 13., 14., 15.],\n",
              "       [16., 17., 18., 19., 20., 21.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "4AJPeDtZMfSa"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}