NN_SentimentAnalysis_ResNet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyPEpeCbVuaxZnPqiblZrJTy",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kedimonmuo/Keras-image_detection/blob/main/NN_SentimentAnalysis_ResNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ResNet50 Model for Sentiment Analysis"
      ],
      "metadata": {
        "id": "7dZiIvXSn8eO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import layers, models\n",
        "from keras.preprocessing.image import load_img\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.applications import ResNet50\n",
        "from keras.applications.resnet50 import preprocess_input\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "import os"
      ],
      "metadata": {
        "id": "XXbwR-HYeaL2"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fetching Kaggle.json file and getting datasset with API\n",
        "from google.colab import files\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "sDcDQ7YBeaO8",
        "outputId": "de1ce539-3a73-4828-ff1c-28d540602030"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-dc349986-fec5-4856-b8ba-8294d0fd49cd\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-dc349986-fec5-4856-b8ba-8294d0fd49cd\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"eceturgut\",\"key\":\"78dbc486d9cfc36523e5b809fac83200\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q kaggle"
      ],
      "metadata": {
        "id": "iSLHv8qCgiXo"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n"
      ],
      "metadata": {
        "id": "fj-uwJWZgiZ8"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d jonathanoheix/face-expression-recognition-dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jQSQeJB2gidV",
        "outputId": "47c58b12-f572-4f97-cf99-67aeec4c2519"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading face-expression-recognition-dataset.zip to /content\n",
            "100% 120M/121M [00:02<00:00, 60.4MB/s]\n",
            "100% 121M/121M [00:02<00:00, 55.4MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "zip_path = '/content/face-expression-recognition-dataset.zip'\n",
        "# we still need to unzzip to make it work"
      ],
      "metadata": {
        "id": "vtFyaVdkg-mz"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "\n",
        "# Define the path to the uploaded ZIP file\n",
        "zip_path = '/content/face-expression-recognition-dataset.zip'\n",
        "\n",
        "# Define the directory where you want to extract the contents\n",
        "extract_path = '/content/extracted_data/'\n",
        "\n",
        "# Create the extraction directory if it doesn't exist\n",
        "if not os.path.exists(extract_path):\n",
        "    os.makedirs(extract_path)\n",
        "\n",
        "# Extract the ZIP file\n",
        "with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n",
        "    zip_ref.extractall(extract_path)"
      ],
      "metadata": {
        "id": "9pitD26vhN44"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import os"
      ],
      "metadata": {
        "id": "4oARriyouMoJ"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " #lets itirate over happy faced images in path\n",
        "images = \"/content/extracted_data/images\"\n",
        "expression = \"happy\"\n",
        "\n",
        "for i in range(min(9, len(images))):\n",
        "  img_path = os.path.join(images, \"images\", \"train\", expression, images[i])\n",
        "  img = load_img(images+ \"/train/\" +expression+\"/\"+\n",
        "    os.listdir(images+ \"/train/\"+expression)[i], target_size=(36,36))\n",
        "  plt.subplot(3, 3, i + 1)\n",
        "  plt.imshow(img)\n",
        "  plt.axis(\"off\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "jHhzoayat2a-",
        "outputId": "6caa4b38-d912-48ed-e7cd-3b5ac8669477"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 9 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_387BK0st70S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train test split and Data Augmentation (Generators)"
      ],
      "metadata": {
        "id": "enoRoHvMiYok"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "ObgfHhWh7nfB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcee8e9d-2f48-4315-e7fa-d045cdd069c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 23060 images belonging to 7 classes.\n",
            "Found 0 images belonging to 7 classes.\n"
          ]
        }
      ],
      "source": [
        "# Define sizeas and path variable\n",
        "images = \"/content/extracted_data/images\"\n",
        "image_size = (224, 224)  # Adjust based on input size expected by ResNet50\n",
        "batch_size = 32\n",
        "no_of_classes =7\n",
        "\n",
        "#classes=['happy', 'sad', 'angry', 'fear', 'disgust', 'neutral', 'suprise']\n",
        "# But no need re-defining because they are One-hot encoded as in categorical class.\n",
        "\n",
        "# Data Augmentation\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    fill_mode= \"nearest\",\n",
        "    width_shift_range= 0.2,\n",
        "    validation_split=0.2,\n",
        ")\n",
        "\n",
        "# Load and split the dataset\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    images+ \"/train\",\n",
        "    target_size=image_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical',\n",
        "    subset='training')\n",
        "\n",
        "\n",
        "validation_generator = ImageDataGenerator(rescale= 1./255)\n",
        "validation_generator = validation_generator.flow_from_directory(\n",
        "    images+ \"/validation\",  # there was a mistake previous path, corrected\n",
        "    target_size=image_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical',\n",
        "    subset='validation')\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " # ResNet50 Model building"
      ],
      "metadata": {
        "id": "tL_-bHfth35M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Editing model Hyperparameters and Optimizers"
      ],
      "metadata": {
        "id": "BXnwIgWYBbI9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.losses import CategoricalCrossentropy"
      ],
      "metadata": {
        "id": "lcKKA6iV5KNq"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image_size = (224, 224)  # Adjust based on input size expected by ResNet50\n",
        "batch_size = 32\n",
        "no_of_classes =7\n",
        "\n",
        "# Build the model (ResNet50 as the base)\n",
        "base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
        "base_model.trainable = False\n",
        "\n",
        "model = models.Sequential([\n",
        "    base_model,\n",
        "    layers.GlobalAveragePooling2D(),\n",
        "    layers.Dense(256, activation='relu'),\n",
        "    layers.Dropout(0.5),\n",
        "    layers.Dense(128, activation='relu'),  # Additional hidden layer\n",
        "    layers.Dropout(0.5),\n",
        "    layers.Dense(7, activation='softmax')  # 7 output classes for 7 sentiments\n",
        "])\n",
        "\n"
      ],
      "metadata": {
        "id": "K83mmHBXh2OM"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001),\n",
        "              loss= \"categorical_crossentropy\",\n",
        "              metrics=['accuracy']\n",
        "              )\n"
      ],
      "metadata": {
        "id": "geJmL4zHfXnY"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define early stopping\n",
        "early_stopping = EarlyStopping(\n",
        "    monitor='val_loss',\n",
        "    patience=5,\n",
        "    restore_best_weights=True,\n",
        "    verbose=1\n",
        ")"
      ],
      "metadata": {
        "id": "pCfBPYuffYG5"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model with early stopping\n",
        "history = model.fit(\n",
        "    train_generator,\n",
        "    epochs=30,\n",
        "    validation_data=validation_generator,\n",
        "    callbacks=[early_stopping]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GsygkvfQhuzN",
        "outputId": "80969d3e-e5b4-4cdb-ff0a-a0a822b6f046"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8446 - accuracy: 0.2327"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 344s 405ms/step - loss: 1.8446 - accuracy: 0.2327\n",
            "Epoch 2/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8148 - accuracy: 0.2485"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 293s 406ms/step - loss: 1.8148 - accuracy: 0.2485\n",
            "Epoch 3/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8135 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 294s 408ms/step - loss: 1.8135 - accuracy: 0.2486\n",
            "Epoch 4/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8133 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 298s 413ms/step - loss: 1.8133 - accuracy: 0.2486\n",
            "Epoch 5/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8124 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 299s 414ms/step - loss: 1.8124 - accuracy: 0.2486\n",
            "Epoch 6/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8142 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 297s 413ms/step - loss: 1.8142 - accuracy: 0.2486\n",
            "Epoch 7/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8124 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 289s 400ms/step - loss: 1.8124 - accuracy: 0.2486\n",
            "Epoch 8/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8131 - accuracy: 0.2484"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 294s 408ms/step - loss: 1.8131 - accuracy: 0.2484\n",
            "Epoch 9/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8120 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 289s 401ms/step - loss: 1.8120 - accuracy: 0.2486\n",
            "Epoch 10/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8121 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 290s 402ms/step - loss: 1.8121 - accuracy: 0.2486\n",
            "Epoch 11/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8119 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 288s 400ms/step - loss: 1.8119 - accuracy: 0.2486\n",
            "Epoch 12/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8123 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 296s 410ms/step - loss: 1.8123 - accuracy: 0.2486\n",
            "Epoch 13/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8121 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 291s 403ms/step - loss: 1.8121 - accuracy: 0.2486\n",
            "Epoch 14/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8116 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 299s 414ms/step - loss: 1.8116 - accuracy: 0.2486\n",
            "Epoch 15/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8122 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 298s 413ms/step - loss: 1.8122 - accuracy: 0.2486\n",
            "Epoch 16/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8117 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 306s 424ms/step - loss: 1.8117 - accuracy: 0.2486\n",
            "Epoch 17/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8120 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 289s 401ms/step - loss: 1.8120 - accuracy: 0.2486\n",
            "Epoch 18/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8116 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 296s 410ms/step - loss: 1.8116 - accuracy: 0.2486\n",
            "Epoch 19/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8120 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 284s 394ms/step - loss: 1.8120 - accuracy: 0.2486\n",
            "Epoch 20/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8114 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 300s 416ms/step - loss: 1.8114 - accuracy: 0.2486\n",
            "Epoch 21/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8118 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 304s 422ms/step - loss: 1.8118 - accuracy: 0.2486\n",
            "Epoch 22/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8114 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 285s 396ms/step - loss: 1.8114 - accuracy: 0.2486\n",
            "Epoch 23/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8120 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 288s 399ms/step - loss: 1.8120 - accuracy: 0.2486\n",
            "Epoch 24/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8120 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 286s 397ms/step - loss: 1.8120 - accuracy: 0.2486\n",
            "Epoch 25/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8119 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 289s 401ms/step - loss: 1.8119 - accuracy: 0.2486\n",
            "Epoch 26/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8116 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 284s 393ms/step - loss: 1.8116 - accuracy: 0.2486\n",
            "Epoch 27/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8114 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 290s 402ms/step - loss: 1.8114 - accuracy: 0.2486\n",
            "Epoch 28/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8117 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 287s 398ms/step - loss: 1.8117 - accuracy: 0.2486\n",
            "Epoch 29/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8115 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 287s 398ms/step - loss: 1.8115 - accuracy: 0.2486\n",
            "Epoch 30/30\n",
            "721/721 [==============================] - ETA: 0s - loss: 1.8112 - accuracy: 0.2486"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r721/721 [==============================] - 291s 403ms/step - loss: 1.8112 - accuracy: 0.2486\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history.history.keys()\n"
      ],
      "metadata": {
        "id": "vird-y1MnmqU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "471638e2-4bef-458c-8420-b71103060d45"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy'])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Metrics we have loss and accuracy\n",
        "loss = history.history['loss']\n",
        "accuracy = history.history.get('accuracy')\n",
        "\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "# Plotting Loss\n",
        "plt.figure(figsize=(12, 4))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs, loss, 'go-', label='Training Loss')  # green with circular markers connected by a solid line\n",
        "plt.title('Training Loss vs Epochs')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "\n",
        "# Plotting Accuracy\n",
        "if accuracy:\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(epochs, accuracy, 'mo-', label='Training Accuracy')\n",
        "    plt.title('Training Accuracy vs Epochs')   # magenta markers with solid line\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "8e46VuIP0d07",
        "outputId": "2b4dfe63-e12d-47ff-a245-04be65bf8d04"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Observe also validation loss - accuracy for better evaluation."
      ],
      "metadata": {
        "id": "rnoFvkkG4UO_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}