basic_Tensors.ipynb

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      "name": "basic_Tensors.ipynb",
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      "authorship_tag": "ABX9TyNoL1WkoyM2jKHj0ADWoDQY",
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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/armeh429/A-journey-for-learning-advanced-tensorflow/blob/main/basic_Tensors.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q2TxBtnWHPiR"
      },
      "source": [
        "#Tensors\n",
        "\n",
        "a tensor is simply a very flexible way of storing data such as scaler, vector, matrix and fially a 3-dimentioanl tensor.\n",
        "at this notebook, you will try some of the basic operations you can perform on tensors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nk_5avlIHMbH"
      },
      "source": [
        "try:\n",
        "    # %tensorflow_version only exists in Colab.\n",
        "    %tensorflow_version 2.x\n",
        "except Exception:\n",
        "    pass\n",
        "\n",
        "import tensorflow as tf\n",
        "import numpy as np"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qeMof3WcH76F"
      },
      "source": [
        "##**Exercise on basic Tensor operations**\n",
        "\n",
        "Lets create a single dimension numpy array on which you can perform some operation. You'll make an array of size 25, holding values from 0 to 24."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5j5lALSCH2kF",
        "outputId": "ae48b638-4f86-49f2-e017-75a117cdbe65"
      },
      "source": [
        "# Create a 1D uint8 NumPy array comprising of first 25 natural numbers\n",
        "x = np.arange(0, 25)\n",
        "x"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "       17, 18, 19, 20, 21, 22, 23, 24])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "shmZJjbSIJqV"
      },
      "source": [
        "Now that you have your 1-D array, next you'll change that array into a tensor. After running the code block below, take a moment to inspect the information of your tensor."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6BsIeoBVICej",
        "outputId": "47b169d4-34e9-41b7-aebf-48683f50f4ce"
      },
      "source": [
        "# convert numPy array to Tensor using 'tf.constant'\n",
        "x = tf.constant(x)\n",
        "x"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(25,), dtype=int64, numpy=\n",
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "       17, 18, 19, 20, 21, 22, 23, 24])>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VbVEoOj0Ifj8"
      },
      "source": [
        "As the first operation to be performed, you'll square (element-wise) all the values in the tensor x"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ijUb0XxoIbpz",
        "outputId": "c252a3f7-4b4b-4712-9f2f-047b832c84dc"
      },
      "source": [
        "# square the input tensor x\n",
        "x = tf.square(x)\n",
        "x"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(25,), dtype=int64, numpy=\n",
              "array([  0,   1,   4,   9,  16,  25,  36,  49,  64,  81, 100, 121, 144,\n",
              "       169, 196, 225, 256, 289, 324, 361, 400, 441, 484, 529, 576])>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5jDoRGPeIvZ9"
      },
      "source": [
        "One feature of tensors is that they can be reshaped. When reshpaing, make sure you consider dimensions that will include all of the values of the tensor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Crj7XG31IsMz",
        "outputId": "a1b835fe-7e4d-4194-f9f7-644790c01981"
      },
      "source": [
        "#reshape tensor x into a 5*5 matrix \n",
        "x = tf.reshape(x,(5,5))\n",
        "x"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(5, 5), dtype=int64, numpy=\n",
              "array([[  0,   1,   4,   9,  16],\n",
              "       [ 25,  36,  49,  64,  81],\n",
              "       [100, 121, 144, 169, 196],\n",
              "       [225, 256, 289, 324, 361],\n",
              "       [400, 441, 484, 529, 576]])>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9wirWAgFJBjM"
      },
      "source": [
        "Notice that you'll get an error message if you choose a shape that cannot be exactly filled with the values of the given tensor.\n",
        "\n",
        "1. Run the cell below and look at the messageerror \n",
        "2. Try to change the tuple that is passed to shape to avoid an error.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "sCwbHsEGI8km",
        "outputId": "862e3230-528b-4a48-cd36-9782246e663a"
      },
      "source": [
        "#try this and look at the error \n",
        "#try to change the tuple that is passed to shape to avoid an error \n",
        "tmp = tf.constant([1,2,3,4])\n",
        "tf.reshape(tmp, shape=(2,3))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "error",
          "ename": "InvalidArgumentError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-a221e77dc820>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#try to change the tuple that is passed to shape to avoid an error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\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----> 4\u001b[0;31m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\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/util/dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    204\u001b[0m     \u001b[0;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    207\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\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[1;32m    208\u001b[0m       \u001b[0;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/array_ops.py\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(tensor, shape, name)\u001b[0m\n\u001b[1;32m    194\u001b[0m     \u001b[0mA\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mHas\u001b[0m \u001b[0mthe\u001b[0m \u001b[0msame\u001b[0m \u001b[0mtype\u001b[0m \u001b[0;32mas\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m   \"\"\"\n\u001b[0;32m--> 196\u001b[0;31m   \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen_array_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\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    197\u001b[0m   \u001b[0mtensor_util\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_set_static_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gen_array_ops.py\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(tensor, shape, name)\u001b[0m\n\u001b[1;32m   8397\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8398\u001b[0m       return reshape_eager_fallback(\n\u001b[0;32m-> 8399\u001b[0;31m           tensor, shape, name=name, ctx=_ctx)\n\u001b[0m\u001b[1;32m   8400\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_SymbolicException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8401\u001b[0m       \u001b[0;32mpass\u001b[0m  \u001b[0;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gen_array_ops.py\u001b[0m in \u001b[0;36mreshape_eager_fallback\u001b[0;34m(tensor, shape, name, ctx)\u001b[0m\n\u001b[1;32m   8422\u001b[0m   \u001b[0m_attrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"T\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_attr_T\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Tshape\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_attr_Tshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8423\u001b[0m   _result = _execute.execute(b\"Reshape\", 1, inputs=_inputs_flat, attrs=_attrs,\n\u001b[0;32m-> 8424\u001b[0;31m                              ctx=ctx, name=name)\n\u001b[0m\u001b[1;32m   8425\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0m_execute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmust_record_gradient\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[1;32m   8426\u001b[0m     _execute.record_gradient(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\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[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mInvalidArgumentError\u001b[0m: Input to reshape is a tensor with 4 values, but the requested shape has 6 [Op:Reshape]"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fPM3X2JrJxPD"
      },
      "source": [
        "Like reshaping, you can also change the data type of the values within the tensor. Run the cell below to change the data type from int to float"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pIAy_H0sJmn7",
        "outputId": "de5414c0-6337-4554-ae2e-03b61498af0b"
      },
      "source": [
        "#cast tensor x into float32. Notice the change in the dtpye.\n",
        "x = tf.cast(x , tf.float32)\n",
        "x"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(5, 5), dtype=float32, numpy=\n",
              "array([[  0.,   1.,   4.,   9.,  16.],\n",
              "       [ 25.,  36.,  49.,  64.,  81.],\n",
              "       [100., 121., 144., 169., 196.],\n",
              "       [225., 256., 289., 324., 361.],\n",
              "       [400., 441., 484., 529., 576.]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SBu3KWpHKJsM"
      },
      "source": [
        "Next, you'll create a single value float tensor by the help of which you'll see broadcasting in action"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vj7UBLMSKGOp",
        "outputId": "d75ba8fa-c32f-4267-bacc-944ebbc66acb"
      },
      "source": [
        "#lets define a constant and see how broadcasting works in the following cell\n",
        "y = tf.constant(2, dtype=tf.float32)\n",
        "y"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=2.0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3pix4xiQKnPq"
      },
      "source": [
        "Multiply the tensors x and y together, and notice how multiplication was done and its result."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UIaRq-1CKkzx",
        "outputId": "f35141db-ae3c-4511-bc13-175e8736f293"
      },
      "source": [
        "# Multiply tensor `x` and `y`. `y` is multiplied to each element of x.\n",
        "result = tf.multiply(x, y)\n",
        "result"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(5, 5), dtype=float32, numpy=\n",
              "array([[   0.,    2.,    8.,   18.,   32.],\n",
              "       [  50.,   72.,   98.,  128.,  162.],\n",
              "       [ 200.,  242.,  288.,  338.,  392.],\n",
              "       [ 450.,  512.,  578.,  648.,  722.],\n",
              "       [ 800.,  882.,  968., 1058., 1152.]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oDeOuTxkKtux"
      },
      "source": [
        "Re-Initialize y to a tensor having more values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oVd4zTt9KqCo",
        "outputId": "bbb08d1d-2630-4d0a-af92-56e6c137f11f"
      },
      "source": [
        "# Now let's define an array that matches the number of row elements in the `x` array.\n",
        "y = tf.constant([1, 2, 3, 4, 5], dtype=tf.float32)\n",
        "y"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(5,), dtype=float32, numpy=array([1., 2., 3., 4., 5.], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9IZWHn4cKwGw",
        "outputId": "2482ee07-6e12-4f0f-fbd4-927d7ccaa402"
      },
      "source": [
        "# Let's see first the contents of `x` again.\n",
        "x"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(5, 5), dtype=float32, numpy=\n",
              "array([[  0.,   1.,   4.,   9.,  16.],\n",
              "       [ 25.,  36.,  49.,  64.,  81.],\n",
              "       [100., 121., 144., 169., 196.],\n",
              "       [225., 256., 289., 324., 361.],\n",
              "       [400., 441., 484., 529., 576.]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5omE53KbK1oD"
      },
      "source": [
        "Add the tensors x and y together, and notice how addition was done and its result."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_nel9TrPK0UI",
        "outputId": "91055647-804a-495e-bae9-619b54ef3a1a"
      },
      "source": [
        "# Add tensor `x` and `y`. `y` is added element wise to each row of `x`.\n",
        "result = x + y \n",
        "result"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(5, 5), dtype=float32, numpy=\n",
              "array([[  1.,   3.,   7.,  13.,  21.],\n",
              "       [ 26.,  38.,  52.,  68.,  86.],\n",
              "       [101., 123., 147., 173., 201.],\n",
              "       [226., 258., 292., 328., 366.],\n",
              "       [401., 443., 487., 533., 581.]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5GmtizhTLC17"
      },
      "source": [
        "##**The shape parameter for tf.constant**\n",
        "\n",
        "When using tf.constant(), you can pass in a 1D array (a vector) and set the shape parameter to turn this vector into a multi-dimensional array."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WssL23hOLAGw",
        "outputId": "d37dfaf5-be45-4a80-b5bd-84da2542e17e"
      },
      "source": [
        "tf.constant([1,2,3,4], shape=(2,2))"
      ],
      "execution_count": 17,
      "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": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eVenTzyXLTai"
      },
      "source": [
        "##**The shape parameter for tf.Variable¶**\n",
        "\n",
        "Note, however, that for tf.Variable(), the shape of the tensor is derived from the shape given by the input array. Setting shape to something other than None will not reshape a 1D array into a multi-dimensional array, and will give a ValueError."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4VqCI_KfLP9a",
        "outputId": "7bd40ad3-c403-4fc0-fbae-d7b9d5ff4432"
      },
      "source": [
        "try:\n",
        "  #this will produce a ValueError\n",
        "  tf.Variable([1,2,3,4], shape=(2,2))\n",
        "except ValueError as v :\n",
        "  #see what the ValueError says\n",
        "  print(v)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The initial value's shape ((4,)) is not compatible with the explicitly supplied `shape` argument ((2, 2)).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jNPTuZnuL7MB"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}