elements_in_tf.ipynb

{
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  "metadata": {
    "colab": {
      "name": "elements in tf.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMtk9gOzRZ8swgqbEX7fnTs",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/adarshnitt/Deep-Learning/blob/main/elements_in_tf.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": "mSB_8GhUt7JK"
      },
      "outputs": [],
      "source": [
        "# elemts in tensorflow\n",
        "# shift+tab: put cursor over function, shift+tab will give documentation about that function\n",
        "# shift+enter: run the notebook"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1> Constant </h1>"
      ],
      "metadata": {
        "id": "2aPKQwXDB-NP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# constant: create a constant for tensor like object.\n",
        "import tensorflow as tf \n",
        "cons1=tf.constant(10)\n",
        "# note: if a tensor is created u cant be able to alter it"
      ],
      "metadata": {
        "id": "bOrojf-EuAMs"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.__version__ # __version__ to check version of any python "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "NjA2L4ExCuSD",
        "outputId": "2af60da9-507b-41fc-aee6-1e9c738e3cbf"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.7.0'"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.test.is_gpu_available() # to check if gpu is available or not"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wTdLZoqCC3WG",
        "outputId": "ba2b8949-d5f3-4939-ad42-4597278d9f6c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:From <ipython-input-4-0b9d68cceeae>: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": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.test?"
      ],
      "metadata": {
        "id": "anJzCg48DEXL"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.constant(12.0) # we cannot modify its value ater.\n",
        "#show putput below\n",
        "#tf.contant(value,dtype,shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJSmwljJ5mpq",
        "outputId": "ea21394d-8b63-4e00-b165-d4206f1218df"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=12.0>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.constant([1,2,3] ) # see log"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W6y9N6Al5mr0",
        "outputId": "99a93276-3e5d-4ea2-bd16-a7362834bb79"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 2, 3], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np #\n",
        "np_array=tf.constant(np.array([1,2,3])) #np.array is def for array formation "
      ],
      "metadata": {
        "id": "RfRhSBh75mt0"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.array([1,2,3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5_PygYAT5mvf",
        "outputId": "5e4ffb31-fc5f-4094-8dee-dd3302c55dbb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# method-1 for 2d tensor\n",
        "twod_array=tf.constant(np.array([[1,2],[2,3]]))\n",
        "twod_array # 2d tensor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zheBAG_y5myr",
        "outputId": "720ed4d3-c57b-41eb-e37d-00e5606ddd5c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 2), dtype=int64, numpy=\n",
              "array([[1, 2],\n",
              "       [2, 3]])>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(twod_array.shape,\"-----\",twod_array.dtype)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AM2gg083G1A3",
        "outputId": "5378f5fc-c454-4f04-d1fd-377d3cb403cb"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 2) ----- <dtype: 'int64'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to get exact value of tensor\n",
        "twod_array.numpy()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bGWpx3uxHB0K",
        "outputId": "ae08dd16-1e2b-4976-e289-51e015fa5fe0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2],\n",
              "       [2, 3]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to get exact value of tensor with index\n",
        "twod_array.numpy()[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KpuqTtE2HKz7",
        "outputId": "20f565a3-7b10-4705-fb1e-6548d79b1677"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# method-2 for 2d tensor\n",
        "tf.constant([1,2,3,4],shape=[2,2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHpYVWp1F8sQ",
        "outputId": "5263187f-8cff-4ed7-a4c8-0b01767a7254"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 2), dtype=int32, numpy=\n",
              "array([[1, 2],\n",
              "       [3, 4]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " # method-2 for 2d tensor with float dtype\n",
        "tf.constant([1,2,3,4],shape=[2,2], dtype=\"float64\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "53Pxwkt3F8vL",
        "outputId": "2c1f3714-dd6f-4714-946b-c185851f8ebb"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 2), dtype=float64, numpy=\n",
              "array([[1., 2.],\n",
              "       [3., 4.]])>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# N-dimentional array\n",
        "tf.constant(np.array([[[1],[2]]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hN1rakGSGMY-",
        "outputId": "11331681-d7e9-4d5b-a487-26ed100d7d83"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 2, 1), dtype=int64, numpy=\n",
              "array([[[1],\n",
              "        [2]]])>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# N-dimentional array\n",
        "tf.constant([1,2,3],shape=[1,1,3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dhUVps6cGlXF",
        "outputId": "f05316d1-b4c6-4356-eb47-f6349392b5fb"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 1, 3), dtype=int32, numpy=array([[[1, 2, 3]]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# complex data type: use j for imaginary part-'j'\n",
        "tf.constant([1+3j])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ufUHmG69PAvG",
        "outputId": "a1beaa24-81df-40a7-b1f5-963a47e15de4"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1,), dtype=complex128, numpy=array([1.+3.j])>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1> Variables ------------\n",
        "</h"
      ],
      "metadata": {
        "id": "-fogcwQDHj6W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# variables: allow us to new variable parameter in graph(tensorflow)\n",
        "var1=tf.Variable(2)\n",
        "\n",
        "'''initial value defines the type and shape of the variable. \n",
        "After construction, the type and shape of the variable are fixed. The value can be changed'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "WOymuIpFuAOC",
        "outputId": "8ed43eb7-68cd-4085-db85-1922dac8a6c0"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'initial value defines the type and shape of the variable. \\nAfter construction, the type and shape of the variable are fixed. The value can be changed'"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "var1=tf.Variable([1,2,3]) # press shift+tab for more documentation"
      ],
      "metadata": {
        "id": "PuEB4fdKuAPk"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "var1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s3GiXYcVOG9I",
        "outputId": "0c458b54-1c34-41c4-be8e-cd08981bd5fb"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([3])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "var1.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PRnFYinAOF31",
        "outputId": "46035d0d-654f-4540-fca7-25194dc82bc4"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tf.int32"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "var1.numpy() # converting data into numpy array"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HwTHPcLVOF6J",
        "outputId": "504fdb62-d4a3-4262-dffe-27d8e01a55e9"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(var1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UxPuNrgNOF9Y",
        "outputId": "0555e053-1966-4405-9394-c648cd3d01a8"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensorflow.python.ops.resource_variable_ops.ResourceVariable"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "var2=tf.Variable([1,2,3.0])\n",
        "var2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EOzk_olUOF_8",
        "outputId": "fdf871e6-ffd6-4755-b568-031f69d59f09"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'Variable:0' shape=(3,) dtype=float32, numpy=array([1., 2., 3.], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "var3=tf.Variable([\"a\",\"b\",\"c\"])\n",
        "var3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L-kglQOhOGBr",
        "outputId": "3f072c63-4d26-4983-9ba0-3b494ce6fff6"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'Variable:0' shape=(3,) dtype=string, numpy=array([b'a', b'b', b'c'], dtype=object)>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# complex datatype\n",
        "tf.Variable([1+2j])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pquuLZ_HOGD-",
        "outputId": "e7686ae1-cbb8-427a-d44b-d9142cc4e369"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'Variable:0' shape=(1,) dtype=complex128, numpy=array([1.-2.j])>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# variable tensor generation using constant tensor.\n",
        "c1=tf.constant([12])\n",
        "v_derived=tf.Variable(c1)"
      ],
      "metadata": {
        "id": "byuVS9HzOGIF"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# varivble with different shape\n",
        "tf.Variable([[1,2],[3,4]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EUH3r9VVOGK8",
        "outputId": "91cbfd68-68fe-4f72-96f9-d71bc701df77"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'Variable:0' shape=(2, 2) dtype=int32, numpy=\n",
              "array([[1, 2],\n",
              "       [3, 4]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# reshaping an existing variable\n",
        "v1=tf.Variable([1,2,3,4,5,9])\n",
        "v2=tf.reshape(v1,[2,3])\n",
        "v2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1PAtY2SLOGNE",
        "outputId": "e4054ab6-a51c-43f4-b035-4d69a2f7609f"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 3), dtype=int32, numpy=\n",
              "array([[1, 2, 3],\n",
              "       [4, 5, 9]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get index of highest value\n",
        "index=tf.argmax(v2).numpy()\n",
        "index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TyDuggfFRYrp",
        "outputId": "6771270d-33ce-4823-d1b6-3e18f494fc46"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert to tensor\n",
        "n1=np.array([1,2,3])\n",
        "tf.convert_to_tensor(n1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CdNXTSO5RY01",
        "outputId": "375050a0-d284-4dfd-9a41-21747e39bf2d"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(3,), dtype=int64, numpy=array([1, 2, 3])>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# assign a new value in tensor, in variable, shape and dtype will be same but value can be chyange\n",
        "x=tf.Variable([1,2,3])\n",
        "x.assign( [111,222,333]).numpy()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hh8HrlIgS-Iy",
        "outputId": "f68ad304-3257-44f1-c5fe-e214bb9088cd"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([111, 222, 333], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# assign same variable with new memory\n",
        "x=tf.Variable([1,2,3])\n",
        "x.assign_add([9,9,9])  # allotted new memory location(new_value must be same data and same data type)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8yhC0rAxS-OK",
        "outputId": "816c0373-ce85-46a4-e1c9-605ee2313dcd"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'UnreadVariable' shape=(3,) dtype=int32, numpy=array([10, 11, 12], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Placeholder\n",
        "<br>\n",
        "type of variable and used to assign data at later stage."
      ],
      "metadata": {
        "id": "Gtz1pf7OUrbJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "Vsbt_03fT_2D"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p1=tf.compat.v1.placeholder()\n",
        "#tf.compat: aloow code  to work in many versions.\n",
        "# tf.compat.v1, tf.compat.v2       ,version1 and version 2."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "id": "hHo82WOiT_36",
        "outputId": "f0c624a2-8b23-4e09-d44e-ba37cae90186"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-eac15d872eec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mp1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mv1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplaceholder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: placeholder() missing 1 required positional argument: 'dtype'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.placeholder(dtype=tf.float32,shape=(20,20))\n",
        "# i will get error bcz in v2, there is no such thing like placeholder"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "id": "RFQY7ttgf8yY",
        "outputId": "778fb923-c3f5-4a7d-9453-6234706bba86"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-bc610c29352b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplaceholder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: module 'tensorflow' has no attribute 'placeholder'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# solution of above error\n",
        "tf.compat.v1.placeholder(dtype=tf.float32,shape=(20,20))\n",
        "# now again error will come saying that eager execution is not supportable."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "5ZwMay22f80x",
        "outputId": "d163fce6-e5c8-42a4-e642-f265f2e25130"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-cdb5165d1029>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# solution of above error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mv1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplaceholder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/array_ops.py\u001b[0m in \u001b[0;36mplaceholder\u001b[0;34m(dtype, shape, name)\u001b[0m\n\u001b[1;32m   3284\u001b[0m   \"\"\"\n\u001b[1;32m   3285\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3286\u001b[0;31m     raise RuntimeError(\"tf.placeholder() is not compatible with \"\n\u001b[0m\u001b[1;32m   3287\u001b[0m                        \"eager execution.\")\n\u001b[1;32m   3288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: tf.placeholder() is not compatible with eager execution."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# actually in tf, all code implemented qucikly, eagerly executed.\n",
        "# we will postpone eager execution\n",
        "tf.compat.v1.disable_eager_execution()"
      ],
      "metadata": {
        "id": "vmtcdvWff82U"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p1=tf.compat.v1.placeholder(dtype=tf.uint64, shape=(20,20))\n",
        "# now place holder will create after resusing eager execution.\n",
        "p1 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EGiNoPJAf85b",
        "outputId": "f143f808-9bc5-4e20-e44f-73bbb4c92f8f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'Placeholder_2:0' shape=(20, 20) dtype=uint64>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# place holder is used to stor data in future\n",
        "p2=tf.compat.v1.placeholder(dtype=tf.uint64, shape=(20,20))\n",
        "p2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cMxvxYafT_6F",
        "outputId": "ad7b58d6-0d6b-4eda-c512-cf9e840c2d5b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'Placeholder_4:0' shape=(20, 20) dtype=uint64>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# arithmatic operation using placeholder\n",
        "out1=tf.add(p1,p2)  # press shift+tab for more info\n",
        "out1 # formed a graph"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3UqAcpPBT_9f",
        "outputId": "e473b4ef-717c-4e0e-c91e-fdf6bd156aec"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'Add:0' shape=(20, 20) dtype=uint64>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# data prep for placeholders\n",
        "import numpy as np\n",
        "d1=np.ones(shape=(20,20),dtype=np.uint64)"
      ],
      "metadata": {
        "id": "RO3Q7HMTS-TY"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# execution and stopping execution are available only in tensorflow 1\n",
        "# so we need to run \"session\" for things in tensorflow 1.x"
      ],
      "metadata": {
        "id": "Z9ZthJ2tuAWJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# execute tensorflow palceholder with sessions\n",
        "with tf.compat.v1.Session() as sess:\n",
        "  result=sess.run(out1,feed_dict={p1:d1,p2:d1})"
      ],
      "metadata": {
        "id": "cegwm1louAX-"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NPZQzgy4uAbb",
        "outputId": "1df696ab-7893-4070-f285-1a4c92092911"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]],\n",
              "      dtype=uint64)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1> Sparse and dense Tensor</h1>"
      ],
      "metadata": {
        "id": "rgcJjEeDEUOR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# sparse : contain lot of zeros\n",
        "s1=tf.SparseTensor(indices=[[1,2],[2,2]],values=[11,22],dense_shape=[2,2]) \n",
        "# indices: at which cordinates things will not be sparse and its respective values\n",
        "s1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FJdn1kkHELqa",
        "outputId": "45c56eff-c5e0-48a4-be57-2dc2bf892bb6"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7f797451b750>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(s1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RzOeyVqFELsb",
        "outputId": "45745dfa-bc71-471c-f09c-1d897fd64dbe"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SparseTensor(indices=Tensor(\"SparseTensor_1/indices:0\", shape=(2, 2), dtype=int64), values=Tensor(\"SparseTensor_1/values:0\", shape=(2,), dtype=int32), dense_shape=Tensor(\"SparseTensor_1/dense_shape:0\", shape=(2,), dtype=int64))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# covert a dense into sparse\n",
        "a=np.array([[1,0],\n",
        "            [0,1]])\n",
        "out=tf.sparse.from_dense(a)"
      ],
      "metadata": {
        "id": "UWmrq6SvELuM"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# indices of sparce tensor where non 0\n",
        "print(out.indices)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XzBMMOs6ELz_",
        "outputId": "2dd1ebbc-628b-4cca-d763-b10b7d425696"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tensor(\"dense_to_sparse_2/Where:0\", shape=(None, 2), dtype=int64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sparse to dense\n",
        "dense1=tf.sparse.to_dense(s1)\n",
        "dense1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LrZHTv1wEL3P",
        "outputId": "ff46b7bc-61fc-406a-a4a3-be4a1ccd72af"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'SparseToDense_3:0' shape=(2, 2) dtype=int32>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.config.run_functions_eagerly(True)\n",
        "dense1.numpy()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "uCsZ0hiDEL6i",
        "outputId": "59f7d002-b531-4f5f-af6d-c20bd2b6661f"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-63-da6b2d707d4d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_functions_eagerly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdense1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    440\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy_ops\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnp_config\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m         np_config.enable_numpy_behavior()\"\"\".format(type(self).__name__, name))\n\u001b[0;32m--> 442\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    444\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'Tensor' object has no attribute 'numpy'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ad=tf.sparse.add(s1,s1)\n",
        "print(ad)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5hcHqT3tEL9b",
        "outputId": "75dc716e-1ad7-445f-9fa5-7aafe8f7fca5"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SparseTensor(indices=Tensor(\"SparseAdd_4:0\", shape=(None, 2), dtype=int64), values=Tensor(\"SparseAdd_4:1\", shape=(None,), dtype=int32), dense_shape=Tensor(\"SparseTensor_6/dense_shape:0\", shape=(2,), dtype=int64))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ones vs ones_like vs Ones_initializer"
      ],
      "metadata": {
        "id": "Qb1UKP3_Riei"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# tf.ones(): all elemets with given shape and dtype will be one\n",
        "o1=tf.ones(shape=(10,10),dtype=tf.float32)\n",
        "o1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XolCAjCEMqEq",
        "outputId": "6caeb632-3f8f-4e6d-e542-1613ad9df1a3"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'ones_2:0' shape=(10, 10) dtype=float32>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "o2=tf.ones(shape=(2,3,4),dtype=tf.float32)\n",
        "print(o2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HY05pAiCMqGX",
        "outputId": "f1a8fcec-5d1b-4ded-e45b-2e15c24a97e2"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tensor(\"ones_5:0\", shape=(2, 3, 4), dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ones_like() :  create a tensor of ones same as \"shape of given input\"\n",
        "#only dtata known, no  shape is known.\n",
        "# create tensor like same as given data."
      ],
      "metadata": {
        "id": "2TWT2iigMqIQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# creating a sample data for ones_like\n",
        "c1=tf.constant([[9,8,800,78]])"
      ],
      "metadata": {
        "id": "ZMj89AgcdZEI"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "same1=tf.ones_like(c1,dtype=tf.int32) # create ones array of given shape input with our decided dtype.\n",
        "same1  # shape is same as input"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B7q2WpVuMqMK",
        "outputId": "0eebe394-2d90-4849-fc18-0eeab45ef007"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'ones_like_1:0' shape=(1, 4) dtype=int32>"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.ones_like(same1,dtype=tf.float32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aXE6yvbudSMn",
        "outputId": "2018cfdd-5856-4308-873e-38cd2c0d3141"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'ones_like_3:0' shape=(1, 4) dtype=float32>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# generate sample data\n",
        "d1=tf.Variable([9,7,5],dtype=tf.int64)\n",
        "same2=tf.ones_like(d1,dtype=tf.float32)\n",
        "same2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m00yCFjNdSOU",
        "outputId": "0a492105-139c-4469-bed8-c3a85bde12c5"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'ones_like_5:0' shape=(3,) dtype=float32>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ones_initializer() # this time neither shape nor type also know\n",
        "# provide a data with shape of given input and type of given input\n"
      ],
      "metadata": {
        "id": "inOZqKAmdSP2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#m-1\n",
        "def one_init(shape,dtype,initlizer):\n",
        "  return tf.Variable(initlizer(shape,dtype))"
      ],
      "metadata": {
        "id": "K_twposXdSRo"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "one_init_exam=one_init((2,2),tf.float32,tf.ones_initializer())\n",
        "one_init_exam\n",
        " # note it will return tf.Variable not ki tf.tensor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z_qzXbZwdSTM",
        "outputId": "ee1dd41a-ddc6-4d40-c32b-5692afb4eefa"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'Variable_6:0' shape=(2, 2) dtype=float32>"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#m-2\n",
        "tf.Variable(tf.ones_initializer()(shape=(2,2),dtype=tf.bool))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YhsILssr1Bj-",
        "outputId": "3f153b27-8608-4156-a6c9-15bbbecf8263"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'Variable_10:0' shape=(2, 2) dtype=bool>"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.zerod_initializer() \n",
        "# initializer took no arguments."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "id": "kIwHekQb1OTE",
        "outputId": "3225c931-d572-4f8f-fd02-653a23a03136"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-121-1fdbfd13e99d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzerod_initializer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: module 'tensorflow' has no attribute 'zerod_initializer'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Wtpv646r1NVH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# tf.ones: generate nay tensor with known shape and dtype\n",
        "# tf.one_like(): generate tensor with known dtype. shape will get from known input data\n",
        "# tf.ones_initializer() : whith unknown shape and data. everything will get from input data.\n",
        "     # pre initialization tech, without known "
      ],
      "metadata": {
        "id": "rD1ahEu2dSU5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1> Zeros vs Zeros_like vs Zeros_initializer </h1>"
      ],
      "metadata": {
        "id": "EKDYUPCAi8GC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# tf.zeros() :  everything known\n",
        "#create a tensor with all elemetvto zero"
      ],
      "metadata": {
        "id": "6KlWEcoDdSZi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z1=tf.zeros(shape=(2,3),dtype=tf.float32)\n",
        "z1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HVmEQoVui4fK",
        "outputId": "b7852338-67b0-49d2-9276-67812144a65d"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'zeros_7:0' shape=(2, 3) dtype=float32>"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.zeros() # press shift+tab, to know about function "
      ],
      "metadata": {
        "id": "q5frXIjQi4iu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# tf.zeros_like():   data shape is unknown.\n",
        "# a tensor with all elemt to zero"
      ],
      "metadata": {
        "id": "wQXhinA5i4nL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=tf.zeros(shape=(1,2,4),dtype=tf.float32)\n",
        "x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XhvSx57ai4qP",
        "outputId": "04b6885e-3c58-4150-ff81-f748fe04c791"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([1, 2, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# tf.zero_like()  # shape will dedtermine through input data.\n",
        "# input data ki  tarah banao.\n",
        "# set all elemest set to zero"
      ],
      "metadata": {
        "id": "Wj0PP3sni4up"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# generating sample data\n",
        "c1=tf.constant([1,2,3])"
      ],
      "metadata": {
        "id": "rQK9q8Jjw6mt"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# tf.zeros_like\n",
        "z1=tf.zeros_like(c1,dtype=tf.float32)"
      ],
      "metadata": {
        "id": "omyYZz6jw6of"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.zeros_like(c1, dtype=tf.bool)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sExAQcGxyB7-",
        "outputId": "0a075730-621d-49b7-d3d1-b8ccfc89885e"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'zeros_like_2:0' shape=(3,) dtype=bool>"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# zeros_initializer: neither know shape nor dtype.\n",
        "# generate data like same as input data\n",
        "# helps to define a pre initialization strategy without knowing shape and dtype of variable initialized.\n",
        "var1=tf.Variable([1,2,3,4])"
      ],
      "metadata": {
        "id": "mTXfvsTPw6qW"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# m-1\n",
        "def f1(shape, dtype, inite):\n",
        "  \"initialize with given shape and dtype\"\n",
        "  return tf.Variable(inite(shape,dtype=dtype))"
      ],
      "metadata": {
        "id": "Sx58LOKRzrZC"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f1((2,2),tf.float32,tf.zeros_initializer())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K1KbbxJvw6sh",
        "outputId": "751fa5a6-f39d-4b23-b25e-2acc228504fc"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'Variable_8:0' shape=(2, 2) dtype=float32>"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.zeros_initializer()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lozZKcVCw607",
        "outputId": "dcd53469-00b0-4535-aa8c-f354f49bee9d"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.ops.init_ops_v2.Zeros at 0x7f796c4fdfd0>"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# m-2\n",
        "tf.Variable(tf.zeros_initializer()((2,2),dtype=tf.int32))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vnf6lB3ai4wd",
        "outputId": "6c852c04-236e-47f1-80d3-3bd17c1f19d0"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'Variable_9:0' shape=(2, 2) dtype=int32>"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "oh95XMobi4yX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "PVW_iQtEi4z9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "tKnZyWfLi41v"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}