00_tensorflow_fundamentals.ipynb

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      "name": "00_tensorflow_fundamentals.ipynb",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kokahonta/tensorflow/blob/main/00_tensorflow_fundamentals.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# In this notebook, we're going to cover some of the most fundamental concepts of tensor using Tensorflow\n",
        "\n",
        "More specifically, we're going to cover:\n",
        "* Introduction to tensors\n",
        "* Getting information from tensors\n",
        "* Manipulation tensors\n",
        "* Tesnors & NumPy\n",
        "* Using @tf.function(a way to speed up your regular Python function)\n",
        "* Using GPUs with TensorFlow(or TPUs)\n",
        "* Excercises to try yourself!"
      ],
      "metadata": {
        "id": "9sqmhF3AIeT8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Introduction to Tensors"
      ],
      "metadata": {
        "id": "FxboiBKlI2fZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import TensorFlow\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jWaPfktUQ24i",
        "outputId": "43c18bb1-9a9e-49cf-b456-cbd19999a1ea"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create tensors with tf.constant()\n",
        "scalar =tf.constant(7)\n",
        "scalar"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iRb4jrDCQ-2a",
        "outputId": "56400965-dab2-48db-c718-da9bc6ae4017"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=int32, numpy=7>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the number of dimensions of a tensor (ndim stand for number of dimensions)\n",
        "scalar.ndim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "76j5UZ_rRdVY",
        "outputId": "d12d3890-d874-4e09-fb62-c2cc9a1dfc56"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a vector\n",
        "vector =tf.constant([10,10])\n",
        "vector"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DwyzZvDeR13P",
        "outputId": "d00c0cae-5e82-4ba7-b839-098679e24a8d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2,), dtype=int32, numpy=array([10, 10])>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the dimenstion of our vercto\n",
        "vector.ndim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D4bejNc8R-8_",
        "outputId": "b07c1156-36f7-4c2a-f33d-e21caa3427b0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a matrix (has more than 1 dimension)\n",
        "matrix =tf.constant([[10,7],[7,10]])\n",
        "matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82Fo_2nWSHXH",
        "outputId": "77396c78-de1b-441f-8661-39b8545e6ef9"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 2), dtype=int32, numpy=\n",
              "array([[10,  7],\n",
              "       [ 7, 10]])>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the dimenstion of our Matrix\n",
        "matrix.ndim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b3rSS-hhSW3X",
        "outputId": "f7e0f7b1-1c95-4954-8997-418df08cc645"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create another matrix\n",
        "another_matrix=tf.constant([[10.,7],\n",
        "                            [5.,1.],\n",
        "                            [12.,5.]],dtype=tf.float16) # specify the data type with float\n",
        "another_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3r8eguxVSiV3",
        "outputId": "3c0e206f-17de-4500-d3c4-f0a133b4999d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(3, 2), dtype=float16, numpy=\n",
              "array([[10.,  7.],\n",
              "       [ 5.,  1.],\n",
              "       [12.,  5.]], dtype=float16)>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Whats the number dimensional of another_matrix\n",
        "another_matrix.ndim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HgJcd1_YTJnX",
        "outputId": "040edadd-24d9-4189-d5ce-f5ab76110124"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's create a tensor"
      ],
      "metadata": {
        "id": "9VMzc-n4TV_2"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tensor=tf.constant([[[1,2,3],\n",
        "                     [4,5,6],\n",
        "                     [8,9,10]],\n",
        "                    [[11,2,12],\n",
        "                     [12,32,12],\n",
        "                     [123,121,1]]])\n",
        "tensor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A5_oGxkcTePm",
        "outputId": "e22867cc-9b15-41cc-df9c-e1491a275052"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 3, 3), dtype=int32, numpy=\n",
              "array([[[  1,   2,   3],\n",
              "        [  4,   5,   6],\n",
              "        [  8,   9,  10]],\n",
              "\n",
              "       [[ 11,   2,  12],\n",
              "        [ 12,  32,  12],\n",
              "        [123, 121,   1]]])>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tensor.ndim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ewDGbzWT4b8",
        "outputId": "f44d8bb3-d774-42d5-f1ba-08880b4a849e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What we've coreated so far:\n",
        "\n",
        "\n",
        "1.   Scalar: a single number\n",
        "2.   Vector: a number with direction (e.g) wind speed and direction\n",
        "3. Matrix: a 2-dimensional array of numbers\n",
        "4. Tensor: an n-dimensional array of numbers (when n can be any number, a 0-dimensional tensor is a scalar, a 1-dimensional tensor is a vector, a 2-dimensional tensor is a matrix)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "xu0mTxHLUDbU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating tensors with `tf.variable`"
      ],
      "metadata": {
        "id": "_S_FaPhPWa0_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the same tensor with tf.Variable() as above\n",
        "changeable_tensor=tf.Variable([10,5])\n",
        "unchangebale_tensor=tf.constant([5,10])\n",
        "changeable_tensor, unchangebale_tensor\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "86JquQLvWh9Z",
        "outputId": "e19e9563-3dda-4e81-dcd5-15d1d90c994f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Variable 'Variable:0' shape=(2,) dtype=int32, numpy=array([10,  5])>,\n",
              " <tf.Tensor: shape=(2,), dtype=int32, numpy=array([ 5, 10])>)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's try chnage one of the elemets in our changeable tensor\n",
        "changeable_tensor[0]=5\n",
        "changeable_tensor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "id": "abHEr2TmXDUi",
        "outputId": "1c7e5bf4-2055-4cf6-9bf3-7bcfc429a75d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "Input \u001b[1;32mIn [15]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Let's try chnage one of the elemets in our changeable tensor\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m changeable_tensor[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m\n\u001b[0;32m      3\u001b[0m changeable_tensor\n",
            "\u001b[1;31mTypeError\u001b[0m: 'ResourceVariable' object does not support item assignment"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# How about we try .assign()\n",
        "changeable_tensor[0].assign(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KNyaA4o0Xo4g",
        "outputId": "6a602947-f079-45e8-a28f-8f0c35ba7682"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'UnreadVariable' shape=(2,) dtype=int32, numpy=array([5, 5])>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's try change our unchangeable tensor\n",
        "unchangebale_tensor[0].assign(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        },
        "id": "8evYY0eAYG2v",
        "outputId": "185ae056-1254-4cfe-bdc4-fe25cf6ee507"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "Input \u001b[1;32mIn [17]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Let's try change our unchangeable tensor\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43munchangebale_tensor\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massign\u001b[49m(\u001b[38;5;241m5\u001b[39m)\n",
            "\u001b[1;31mAttributeError\u001b[0m: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'assign'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "🔑<b>Note:</b> Rarely in practice will you need to decide whether to use `t.constant` or `tf.variabl` to create tensors, as TensorFlow done this for you. However, if in doubt, use `tf.contant` and change it later if needed."
      ],
      "metadata": {
        "id": "aqGUN-ixYxTN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create random tensors\n",
        "\n",
        "Random tensors are tensors of some abitrary size which contain random numbers\n"
      ],
      "metadata": {
        "id": "8xwGuJoPYXUH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "EGimhSjSZi6j"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}