Callbacks_keras.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Callbacks_keras.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMmcH403B7hHPZ7PSjeaB1q",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jvishnuvardhan/Stackoverflow_Questions/blob/master/Callbacks_keras.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pID8EDFiO4uw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "outputId": "e37d0057-7579-4ed7-ed24-49b312b2bc17"
      },
      "source": [
        "!pip install tf-nightly"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tf-nightly in /usr/local/lib/python3.6/dist-packages (2.2.0.dev20200425)\n",
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            "Requirement already satisfied: scipy==1.4.1; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.4.1)\n",
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            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (1.0.1)\n",
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            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (0.4.1)\n",
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            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (3.2.1)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (46.1.3)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (2020.4.5.1)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (1.3.0)\n",
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            "Requirement already satisfied: cachetools<3.2,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (3.1.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (3.1.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (0.4.8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AT_ujXEHO-23",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VchL60s1O6wt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        },
        "outputId": "49b4ce4e-0b47-401e-f40f-f0b931789c93"
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten\n",
        "from matplotlib import pyplot as plt\n",
        "from tensorflow.keras.callbacks import Callback\n",
        "\n",
        "\n",
        "class MyCallback(Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('accuracy') > 0.9):\n",
        "      print(\"\\n Training stopping now. accuracy reached 90 !\")\n",
        "      self.model.stop_training = True\n",
        "\n",
        "\n",
        "callback = MyCallback()\n",
        "\n",
        "# Input data\n",
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "(training_data, training_labels), (testing_data, testing_labels) = fashion_mnist.load_data()\n",
        "training_data = training_data / 255.0\n",
        "testing_data = testing_data / 255.0\n",
        "plt.imshow(training_data[0], cmap='gray')\n",
        "\n",
        "# Network\n",
        "model = Sequential([\n",
        "    Flatten(input_shape=(28, 28)),\n",
        "    Dense(units=128, activation='relu'),\n",
        "    Dense(units=10, activation='softmax')])\n",
        "\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "model.fit(training_data, training_labels, epochs=25, callbacks=[callback])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.4954 - accuracy: 0.8263\n",
            "Epoch 2/25\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3715 - accuracy: 0.8657\n",
            "Epoch 3/25\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3346 - accuracy: 0.8785\n",
            "Epoch 4/25\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3122 - accuracy: 0.8852\n",
            "Epoch 5/25\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2949 - accuracy: 0.8918\n",
            "Epoch 6/25\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2818 - accuracy: 0.8950\n",
            "Epoch 7/25\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2702 - accuracy: 0.8984\n",
            "Epoch 8/25\n",
            "1854/1875 [============================>.] - ETA: 0s - loss: 0.2582 - accuracy: 0.9039\n",
            " Training stopping now. accuracy reached 90 !\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2582 - accuracy: 0.9039\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f44fe11c748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21IsiKlTVv2i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}