keras_basic.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPVS+WRRpz+6aFNGD2sOGTH",
      "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/redom13/ML/blob/main/keras_basic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "sm1iVW_ZHH8V"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Dense,Input"
      ],
      "metadata": {
        "id": "JHtRNGGIHNNi"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.losses import MeanSquaredError, BinaryCrossentropy\n",
        "from tensorflow.keras import Sequential"
      ],
      "metadata": {
        "id": "oEbBAjotHmgu"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.activations import sigmoid"
      ],
      "metadata": {
        "id": "1Jz-yyGfH9W4"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "linear_layer=tf.keras.layers.Dense(units=1,activation='linear')"
      ],
      "metadata": {
        "id": "othUhRbvIKB-"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "linear_layer.get_weights()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aoYuBeEsI3dy",
        "outputId": "7770f4af-f143-4016-b313-ef92fc5c69ed"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = np.array([[1.0], [2.0]], dtype=np.float32)           #(size in 1000 square feet)\n",
        "Y_train = np.array([[300.0], [500.0]], dtype=np.float32)\n",
        "print(X_train.shape)     #(price in 1000s of dollars)\n",
        "# X_train has a single feature. Hence w will have a single wt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LAsRdjAZJBk-",
        "outputId": "7beecf9a-6b97-4deb-9915-cfa0b9e78b1d"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a1=linear_layer(X_train)\n",
        "print(a1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CUoBkjiMOCK9",
        "outputId": "62df2dac-ee67-4866-ef08-943039ebe0f0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[0.629604]\n",
            " [1.259208]], shape=(2, 1), dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "w,b=linear_layer.get_weights()\n",
        "print(f'w:{w},b:{b}')\n",
        "print(f'w.shape:{w.shape},b.shape:{b.shape}')\n",
        "# So w is a 2D array"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5uf0URPHObzr",
        "outputId": "3fa2e71e-6305-4959-f7b3-789a49050ea5"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "w:[[0.629604]],b:[0.]\n",
            "w.shape:(1, 1),b.shape:(1,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "set_w=np.array([[200.0]])\n",
        "set_b=np.array([100.0])\n",
        "linear_layer.set_weights([set_w,set_b])\n",
        "a2=linear_layer(X_train)\n",
        "print(a2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7wa2ZPwqOf-4",
        "outputId": "382a4a27-e936-456e-f89e-bcdf84a5f15b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[300.]\n",
            " [500.]], shape=(2, 1), dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model=Sequential(\n",
        "    [\n",
        "        tf.keras.layers.Dense(1,input_dim=1,activation='sigmoid',name='L1')\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "id": "eEynUNTDQ_hZ"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HtQBDymYUX7x",
        "outputId": "3c1aee9f-ab5f-4fb0-8d3c-b3de00127429"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " L1 (Dense)                  (None, 2)                 4         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4 (16.00 Byte)\n",
            "Trainable params: 4 (16.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logistic_layer=model.get_layer('L1')"
      ],
      "metadata": {
        "id": "LaPYVPAtUa3l"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "w,b=logistic_layer.get_weights()\n",
        "print(w,b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YKryCOkBVYnr",
        "outputId": "83f9072e-dba7-479f-efcf-a4c9d89ab1dc"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.8009861]] [0.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = np.array([0., 1, 2, 3, 4, 5], dtype=np.float32).reshape(-1,1)  # 2-D Matrix\n",
        "Y_train = np.array([0,  0, 0, 1, 1, 1], dtype=np.float32).reshape(-1,1)  # 2-D Matrix"
      ],
      "metadata": {
        "id": "kladhCOGVke-"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "set_w = np.array([[2]])\n",
        "set_b = np.array([-4.5])"
      ],
      "metadata": {
        "id": "K0LNIYSNVvVr"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "logistic_layer.set_weights([set_w,set_b])\n",
        "print(logistic_layer.get_weights())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wlAJRmgEV3xO",
        "outputId": "fa574b0d-9c06-4936-b36a-ce869217b044"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([[2.]], dtype=float32), array([-4.5], dtype=float32)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a1=model.predict(X_train)\n",
        "print(a1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1fv8Yx2HWKU9",
        "outputId": "8df2f321-6c20-4fdf-f2ff-54776f045aa3"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 183ms/step\n",
            "[[0.01098694]\n",
            " [0.07585818]\n",
            " [0.37754068]\n",
            " [0.8175745 ]\n",
            " [0.97068775]\n",
            " [0.99592984]]\n"
          ]
        }
      ]
    }
  ]
}