Welcome_To_Colaboratory.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ishant06/DSBDA/blob/main/Welcome_To_Colaboratory.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.datasets import imdb\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import cm\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import os\n",
        "import time"
      ],
      "metadata": {
        "id": "rVAOaZI5q5j5"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.preprocessing import sequence\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation\n",
        "from keras.layers import Embedding\n",
        "from keras.layers import Conv1D, GlobalMaxPooling1D\n",
        "from keras.callbacks import EarlyStopping\n",
        "from keras import models"
      ],
      "metadata": {
        "id": "Co4177QEq68a"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train, y_train), (X_test, y_test) = imdb.load_data()\n",
        "X = np.concatenate((X_train, X_test), axis=0)\n",
        "y = np.concatenate((y_train, y_test), axis=0)"
      ],
      "metadata": {
        "id": "dsuEcD-JrAYG",
        "outputId": "293c5dfd-a8ca-40cf-9b86-4389b24a95e4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "17464789/17464789 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Training data: \")\n",
        "print(X.shape)\n",
        "print(y.shape)\n",
        "print(\"Classes: \")\n",
        "print(np.unique(y))"
      ],
      "metadata": {
        "id": "TuEd2lFFreGl",
        "outputId": "90afaff7-9875-42c8-ee17-28efdad6b0e4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training data: \n",
            "(50000,)\n",
            "(50000,)\n",
            "Classes: \n",
            "[0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Number of words: \")\n",
        "print(len(np.unique(np.hstack(X))))"
      ],
      "metadata": {
        "id": "niMnjS8wrjU6",
        "outputId": "1f66ae9c-e1a5-41f3-ae82-694e17364be4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of words: \n",
            "88585\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Review length: \")\n",
        "result = [len(x) for x in X]\n",
        "print(\"Mean %.2f words (%f)\" % (np.mean(result), np.std(result)))\n",
        "# plot review length\n",
        "plt.boxplot(result)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ieM8gLH4rqT0",
        "outputId": "beb8a130-f3df-4b35-bef1-87815a69176f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Review length: \n",
            "Mean 234.76 words (172.911495)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=5000)"
      ],
      "metadata": {
        "id": "tt8A00ytrsln"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def vectorize_sequences(sequences, dimension=5000):\n",
        "    # Create an all-zero matrix of shape (len(sequences), dimension)\n",
        "    results = np.zeros((len(sequences), dimension))\n",
        "    for i, sequence in enumerate(sequences):\n",
        "        results[i, sequence] = 1.  # set specific indices of results[i] to 1s\n",
        "    return results"
      ],
      "metadata": {
        "id": "eSnTVrP3rwx0"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Our vectorized training data\n",
        "x_train = vectorize_sequences(train_data)\n",
        "# Our vectorized test data\n",
        "x_test = vectorize_sequences(test_data)"
      ],
      "metadata": {
        "id": "l98jhA5dr19z"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = np.asarray(train_labels).astype('float32')\n",
        "y_test = np.asarray(test_labels).astype('float32')"
      ],
      "metadata": {
        "id": "4XZVXQbhr6sK"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import layers\n",
        "from keras import models\n",
        "\n",
        "model = models.Sequential()\n",
        "model.add(layers.Dense(32, activation='relu', input_shape=(5000,)))\n",
        "model.add(layers.Dense(32, activation='relu',))\n",
        "model.add(layers.Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "73aNDOwnsDAy"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_val = x_train[:10000]\n",
        "partial_x_train = x_train[10000:]\n",
        "\n",
        "y_val = y_train[:10000]\n",
        "partial_y_train = y_train[10000:]"
      ],
      "metadata": {
        "id": "kB_crsJNsEzw"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['acc'])"
      ],
      "metadata": {
        "id": "jyzJHBYzsKMk"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "start_time_m1 = time.time()\n",
        "history = model.fit(partial_x_train,\n",
        "                    partial_y_train,\n",
        "                    epochs=20,\n",
        "                    batch_size=512,\n",
        "                    validation_data=(x_val, y_val))\n",
        "total_time_m1 = time.time() - start_time_m1"
      ],
      "metadata": {
        "id": "FjCmYq8hsQXE",
        "outputId": "b3c77da8-cda9-4269-c23b-c93e89b8f90f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "30/30 [==============================] - 3s 74ms/step - loss: 0.5514 - acc: 0.7400 - val_loss: 0.3794 - val_acc: 0.8552\n",
            "Epoch 2/20\n",
            "30/30 [==============================] - 3s 88ms/step - loss: 0.2878 - acc: 0.8915 - val_loss: 0.2882 - val_acc: 0.8851\n",
            "Epoch 3/20\n",
            "30/30 [==============================] - 1s 49ms/step - loss: 0.2054 - acc: 0.9245 - val_loss: 0.2900 - val_acc: 0.8829\n",
            "Epoch 4/20\n",
            "30/30 [==============================] - 1s 39ms/step - loss: 0.1641 - acc: 0.9419 - val_loss: 0.3084 - val_acc: 0.8804\n",
            "Epoch 5/20\n",
            "30/30 [==============================] - 1s 23ms/step - loss: 0.1354 - acc: 0.9524 - val_loss: 0.3353 - val_acc: 0.8734\n",
            "Epoch 6/20\n",
            "30/30 [==============================] - 1s 24ms/step - loss: 0.1124 - acc: 0.9629 - val_loss: 0.3650 - val_acc: 0.8713\n",
            "Epoch 7/20\n",
            "30/30 [==============================] - 1s 23ms/step - loss: 0.0961 - acc: 0.9681 - val_loss: 0.3973 - val_acc: 0.8667\n",
            "Epoch 8/20\n",
            "30/30 [==============================] - 1s 25ms/step - loss: 0.0727 - acc: 0.9786 - val_loss: 0.4333 - val_acc: 0.8648\n",
            "Epoch 9/20\n",
            "30/30 [==============================] - 1s 28ms/step - loss: 0.0547 - acc: 0.9865 - val_loss: 0.4739 - val_acc: 0.8637\n",
            "Epoch 10/20\n",
            "30/30 [==============================] - 1s 24ms/step - loss: 0.0405 - acc: 0.9928 - val_loss: 0.5184 - val_acc: 0.8648\n",
            "Epoch 11/20\n",
            "30/30 [==============================] - 1s 23ms/step - loss: 0.0277 - acc: 0.9973 - val_loss: 0.5623 - val_acc: 0.8599\n",
            "Epoch 12/20\n",
            "30/30 [==============================] - 1s 29ms/step - loss: 0.0183 - acc: 0.9986 - val_loss: 0.6064 - val_acc: 0.8599\n",
            "Epoch 13/20\n",
            "30/30 [==============================] - 1s 24ms/step - loss: 0.0123 - acc: 0.9997 - val_loss: 0.6483 - val_acc: 0.8599\n",
            "Epoch 14/20\n",
            "30/30 [==============================] - 1s 24ms/step - loss: 0.0085 - acc: 0.9999 - val_loss: 0.6827 - val_acc: 0.8582\n",
            "Epoch 15/20\n",
            "30/30 [==============================] - 1s 29ms/step - loss: 0.0060 - acc: 1.0000 - val_loss: 0.7143 - val_acc: 0.8581\n",
            "Epoch 16/20\n",
            "30/30 [==============================] - 1s 38ms/step - loss: 0.0046 - acc: 1.0000 - val_loss: 0.7461 - val_acc: 0.8581\n",
            "Epoch 17/20\n",
            "30/30 [==============================] - 1s 37ms/step - loss: 0.0035 - acc: 1.0000 - val_loss: 0.7725 - val_acc: 0.8569\n",
            "Epoch 18/20\n",
            "30/30 [==============================] - 1s 31ms/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.7990 - val_acc: 0.8576\n",
            "Epoch 19/20\n",
            "30/30 [==============================] - 1s 29ms/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.8249 - val_acc: 0.8585\n",
            "Epoch 20/20\n",
            "30/30 [==============================] - 1s 24ms/step - loss: 0.0018 - acc: 1.0000 - val_loss: 0.8473 - val_acc: 0.8577\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"The Dense Convolutional Neural Network 1 layer took %.4f seconds to train.\" % (total_time_m1))"
      ],
      "metadata": {
        "id": "EaqlX1HZscHl",
        "outputId": "d037e61b-37ce-412e-eefa-da90a8fad2a9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Dense Convolutional Neural Network 1 layer took 42.9976 seconds to train.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history_dict = history.history\n",
        "history_dict.keys()"
      ],
      "metadata": {
        "id": "tlQhKc0fshjf",
        "outputId": "a3b46efc-df0a-41e7-b844-7a3eef1fe175",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "# \"bo\" is for \"blue dot\"\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "# b is for \"solid blue line\"\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "D7uOssiNskyh",
        "outputId": "e63eb7ff-31f5-48a2-f468-038fba1ef7d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.clf()   # clear figure\n",
        "acc_values = history_dict['acc']\n",
        "val_acc_values = history_dict['val_acc']\n",
        "\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "2mTgXDGyspfQ",
        "outputId": "467a2886-14c2-4bc2-debe-6127caf2a3f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "2GQVaFRqstje",
        "outputId": "fbc0a95a-15b8-4150-84f5-1cb9961b4ce3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 32)                160032    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 32)                1056      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 33        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 161,121\n",
            "Trainable params: 161,121\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score, auc\n",
        "#predictions\n",
        "pred = model.predict(x_test)\n",
        "classes_x=np.argmax(pred,axis=1)\n",
        "\n",
        "#accuracy\n",
        "accuracy_score(y_test,classes_x)"
      ],
      "metadata": {
        "id": "DdSV44JPsxm0",
        "outputId": "9092b16a-038f-491d-a101-301ea1762625",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "782/782 [==============================] - 2s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conf_mat = confusion_matrix(y_test, classes_x)\n",
        "print(conf_mat)\n",
        "\n",
        "conf_mat_normalized = conf_mat.astype('float') / conf_mat.sum(axis=1)[:, np.newaxis]\n",
        "sns.heatmap(conf_mat_normalized)\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "id": "WykYSK4ms3Is",
        "outputId": "ceb4526b-47f8-4876-9987-327592712ba6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        }
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[12500     0]\n",
            " [12500     0]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 15.0, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = models.Sequential()\n",
        "model2.add(layers.Dense(32, activation='relu', input_shape=(5000,)))\n",
        "model2.add(layers.Dense(32, activation='relu'))\n",
        "model2.add(layers.Dense(32, activation='relu'))\n",
        "model2.add(layers.Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "DmufMCeYs7I7"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model2.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['acc'])"
      ],
      "metadata": {
        "id": "f8MFtVXrtA43"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "start_time_m2 = time.time()\n",
        "history= model2.fit(partial_x_train,\n",
        "                    partial_y_train,\n",
        "                    epochs=20,\n",
        "                    batch_size=512,\n",
        "                    validation_data=(x_val, y_val))\n",
        "total_time_m2 = time.time() - start_time_m2\n",
        "\n",
        "print(\"The Dense Convolutional Neural Network 2 layers took %.4f seconds to train.\" % (total_time_m2))"
      ],
      "metadata": {
        "id": "ksgRDfERtG6z",
        "outputId": "1f9f9afb-16b2-442b-f4c0-9e86eea85202",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "30/30 [==============================] - 2s 46ms/step - loss: 0.5917 - acc: 0.7343 - val_loss: 0.4146 - val_acc: 0.8464\n",
            "Epoch 2/20\n",
            "30/30 [==============================] - 1s 23ms/step - loss: 0.3031 - acc: 0.8854 - val_loss: 0.2895 - val_acc: 0.8827\n",
            "Epoch 3/20\n",
            "30/30 [==============================] - 1s 29ms/step - loss: 0.2043 - acc: 0.9238 - val_loss: 0.2958 - val_acc: 0.8821\n",
            "Epoch 4/20\n",
            "30/30 [==============================] - 1s 25ms/step - loss: 0.1577 - acc: 0.9450 - val_loss: 0.3284 - val_acc: 0.8780\n",
            "Epoch 5/20\n",
            "30/30 [==============================] - 1s 24ms/step - loss: 0.1280 - acc: 0.9537 - val_loss: 0.3638 - val_acc: 0.8713\n",
            "Epoch 6/20\n",
            "30/30 [==============================] - 1s 25ms/step - loss: 0.0999 - acc: 0.9673 - val_loss: 0.4145 - val_acc: 0.8677\n",
            "Epoch 7/20\n",
            "30/30 [==============================] - 1s 39ms/step - loss: 0.0760 - acc: 0.9759 - val_loss: 0.5127 - val_acc: 0.8515\n",
            "Epoch 8/20\n",
            "30/30 [==============================] - 1s 41ms/step - loss: 0.0646 - acc: 0.9790 - val_loss: 0.5316 - val_acc: 0.8573\n",
            "Epoch 9/20\n",
            "30/30 [==============================] - 1s 29ms/step - loss: 0.0413 - acc: 0.9901 - val_loss: 0.5725 - val_acc: 0.8612\n",
            "Epoch 10/20\n",
            "30/30 [==============================] - 1s 24ms/step - loss: 0.0232 - acc: 0.9969 - val_loss: 0.6259 - val_acc: 0.8587\n",
            "Epoch 11/20\n",
            "30/30 [==============================] - 1s 24ms/step - loss: 0.0130 - acc: 0.9989 - val_loss: 0.6842 - val_acc: 0.8587\n",
            "Epoch 12/20\n",
            "30/30 [==============================] - 1s 24ms/step - loss: 0.0075 - acc: 0.9997 - val_loss: 0.7370 - val_acc: 0.8583\n",
            "Epoch 13/20\n",
            "30/30 [==============================] - 1s 29ms/step - loss: 0.0045 - acc: 0.9999 - val_loss: 0.7849 - val_acc: 0.8576\n",
            "Epoch 14/20\n",
            "30/30 [==============================] - 1s 24ms/step - loss: 0.0031 - acc: 0.9999 - val_loss: 0.8212 - val_acc: 0.8559\n",
            "Epoch 15/20\n",
            "30/30 [==============================] - 1s 24ms/step - loss: 0.0019 - acc: 1.0000 - val_loss: 0.8585 - val_acc: 0.8573\n",
            "Epoch 16/20\n",
            "30/30 [==============================] - 1s 24ms/step - loss: 0.0014 - acc: 1.0000 - val_loss: 0.8891 - val_acc: 0.8566\n",
            "Epoch 17/20\n",
            "30/30 [==============================] - 1s 24ms/step - loss: 0.0010 - acc: 1.0000 - val_loss: 0.9149 - val_acc: 0.8561\n",
            "Epoch 18/20\n",
            "30/30 [==============================] - 1s 25ms/step - loss: 8.2436e-04 - acc: 1.0000 - val_loss: 0.9397 - val_acc: 0.8567\n",
            "Epoch 19/20\n",
            "30/30 [==============================] - 1s 29ms/step - loss: 6.6785e-04 - acc: 1.0000 - val_loss: 0.9596 - val_acc: 0.8570\n",
            "Epoch 20/20\n",
            "30/30 [==============================] - 1s 23ms/step - loss: 5.5454e-04 - acc: 1.0000 - val_loss: 0.9806 - val_acc: 0.8565\n",
            "The Dense Convolutional Neural Network 2 layers took 22.3265 seconds to train.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "# \"bo\" is for \"blue dot\"\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "# b is for \"solid blue line\"\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('DNN 2 layer Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "b1FCmhXmtPCS",
        "outputId": "6a0617e7-87bc-44fb-96d7-4b8edb13ec8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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+e5g6Fe68Ezp3To0kAGlYIhg6NLQJJNZsVK8elsdpw4YNNGjQAICnn3661Pd/xBFHsGzZMpYvX07Tpk158cUXC92mS5cujBkzhjvuuIMpU6ZQr149ateuzRdffEHr1q1p3bo1M2fOZPHixRxwwAE0bNiQK6+8km3btjFnzhz69OlT6r+HSHmQc8U/ZUp4zJgRrvgrV4aMjHDi79YtnOxr1UpysEWQdokgpx2gNHsNFcUtt9xC3759ue+++zjzzDNLff8HHHAAjz32GKeffjo1atSgY8eOhW6T0zh9zDHHUL16dZ555hkAHn30Ud555x0qVarEUUcdRffu3Rk7diwPPfQQVatWpWbNmioRSFrZsQOmTdtd1TNjBmzfHk78HTrAwIG7T/y1ayc72uIrd3MWZ2RkeO6JaRYtWsTPf/7zJEWUOjZv3kzNmjVxd37729/SokULBg4cmOyw9qK/l5QHmzfDf/8L48bB66/D+vW7T/zdupW/E7+ZzXb3PPuqp12JoCJ7/PHHeeaZZ9i+fTvt2rXj6quvTnZIIuXKqlXw2mvh5D95MmzbBnXrwrnnQs+eoS9/eTnxF4cSQQUycODAlCwBiKSypUvDiX/cuHDnrjs0bRq6dJ5zDpxwQujtU5FV8F9PRGRP7qEff87Jf8GCsLxdu9Ct85xzoHXr5PTnTxYlAhGp8HbsgHffDSf+f/8bMjNDfX/XrqEXYY8eoRSQrpQIRKRC2rYt1PO/8ko4+a9bBwccAKedBvfdB2edFer/RYlARCqQrCyYNCmc/F97DTZuDI27PXqE8Xp+9atw35DsKe3uLI7DySefzKRJk/ZY9uijj3LNNdfku023bt3I6QZ7xhlnsH79+r3WGTJkCMOGDSvw2OPGjWPhwoW7Xt95551Mnjy5GNHnTcNVS3mxaRO8+CJceCHUrw/nnQcTJ8IFF4Run6tXw3PPhbp/JYG8qURQCnr16sXYsWM57bTTdi0bO3YsDz74YJG2nzBhwj4fe9y4cZx11lm0atUKgHvuuWef9yVSXqxfD+PHhyv/SZNCNdChh0KfPuHK/6STUmf4hvJAJYJScMEFF/D666/vmoRm+fLlfPPNN3Tp0oVrrrmGjIwMjjrqKO666648t2/atCnfffcdAEOHDqVly5aceOKJu4aqhnCPQMeOHWnTpg3nn38+WVlZvP/++4wfP56bb76Ztm3b8sUXX9CvXz/++c9/AvDWW2/Rrl07WrduTf/+/dm2bduu49111120b9+e1q1bs3jx4gJ/Pw1XLangu+/giSege3c45BDo2xfmzIGrrw7j93z9dRiy+dRTlQSKq8KVCG64AaI5YkpN27ZhtqD81KlTh2OPPZaJEyfSs2dPxo4dy0UXXYSZMXToUOrUqcOPP/7IKaecwty5cznmmGPy3M/s2bMZO3Ysn3zyCdnZ2bRv354OHToAcN5553HllVcC8Ic//IEnn3yS6667jh49enDWWWdxwQUX7LGvrVu30q9fP9566y1atmxJnz59GDlyJDfccAMA9erVY86cOTz22GMMGzaMJ554It/fT8NVS7JkZ4fB2kaPhv/8B378EZo1C//n558PHTtCJV3Olpg+wlKSUz0EoVooZz6Al156ifbt29OuXTsWLFiwR31+bu+99x7nnnsu1atXp3bt2vTo0WPXe/Pnz6dLly60bt2aMWPGsCCn83M+lixZQrNmzWjZsiUAffv2ZerUqbveP++88wDo0KEDy5cvL3Bf06ZN49e//jWQ93DVw4cPZ/369VSpUoWOHTvy1FNPMWTIEObNm0et8jDilqScJUvg1luhUaNwR++HH4aB3ObMgS++gAcfhE6dlARKS4UrERR05R6nnj17MnDgQObMmUNWVhYdOnTgyy+/ZNiwYcycOZODDz6Yfv365Tv8dGH69evHuHHjaNOmDU8//TRTpkwpUbw5Q1mXZBjrQYMGceaZZzJhwgQ6d+7MpEmTdg1X/frrr9OvXz9uvPFGjVIqRbJ5M7z8crj6nzYt9PM/88wwc9cZZ6i6J07Kp6WkZs2anHzyyfTv339XaWDjxo3UqFGDAw88kFWrVjFx4sQC99G1a1fGjRvHli1b2LRpE6+99tqu9zZt2sRhhx3Gjh07GJMwr2atWrXYtGnTXvs64ogjWL58OUuXLgXgueee46STTtqn3y1nuGogz+Gqb731Vjp27MjixYtZsWIFhx56KFdeeSX/93//x5w5c/bpmJIe3MNwzldeCYcdFk76q1fDAw/AV1+F/v89eyoJxK3ClQiSqVevXpx77rm7qojatGlDu3btOPLII2nUqBGdO3cucPv27dtz8cUX06ZNGw455JA9hpK+99576dSpE/Xr16dTp067Tv6XXHIJV155JcOHD9/VSAxQrVo1nnrqKS688EKys7Pp2LEjAwYM2KffS8NVS2nL6dL55JOwaBHUqAEXXQRXXBHG9kmn4R1SgYahljKnv1d6ys4OwzqPHh1u9srODtM2XnFFSAJqToqXhqEWkaTZuBH+9jf4y1/gm29C188bbgjVQLoeSA1KBCISi1Wrwsn/scdgw4bQv3/EiNAArDr/1FJhEoG7Y6pYTHnlrSpSiu/LL2HYsFAFtG1b6O8/aFCY2UtSU4VIBNWqVWPt2rXUrVtXySCFuTtr166lWrVqyQ5FYjBvHtx/fxj3p1KlcOfvzTdDdCuLpLAKkQgaNmxIZmYma9asSXYoUohq1arRsGHDZIchpWjatJAAXn8datYM9f8DB0KDBsmOTIqqQiSCqlWr0qxZs2SHIZI23MOJ//77Yfp0qFcP7r03TO9Yp06yo5PiqhCJQETKRnZ2qPq5/36YPx8aN4bhw0MXUA3xXH4pEYhIobKy4KmnQiPw8uXQqhU8+yxccol6AFUESgQikq81a0KXzxEjwjDQxx0XuoSedZYGfKtIlAhEZC9Ll8LDD4dSwNatcPbZcNNN0KWLhn+oiGLN6WZ2upktMbOlZjYoj/cbm9k7Zvaxmc01szPijEdECjZjRpjisWXLMA5Q796wcGGYDaxrVyWBiiq2EoGZVQZGAL8EMoGZZjbe3RMH5P8D8JK7jzSzVsAEoGlcMYnI3nbuDD2AHnoI3nsPDjoo3AB23XVhRFCp+OKsGjoWWOruywDMbCzQE0hMBA7Ujp4fCHwTYzwikmDbNnj++dAAvHhx6AH0yCOhB5AGgEsvcSaCBsBXCa8zgU651hkCvGFm1wE1gFPz2pGZXQVcBdC4ceNSD1QknXz/fRgEbvhw+N//oE2bkBAuukg9gNJVstv9ewFPu3tD4AzgOTPbKyZ3H+XuGe6eUb9+/TIPUqQiWLEi3PHbqBHcfju0bg1vvAEffxzaApQE0lecJYKvgUYJrxtGyxJdAZwO4O4fmFk1oB6wOsa4RNLKpk1wzz1hGlf30Pf/ppugbdtkRyapIs4SwUyghZk1M7P9gEuA8bnWWQmcAmBmPweqARowSKQUuIe7gI88MrQD9O0Ly5aFaiAlAUkUW4nA3bPN7FpgElAZGO3uC8zsHmCWu48Hfg88bmYDCQ3H/VzjFIuU2KJFcO218Pbb0L49vPJKuBlMJC+x3lDm7hMIXUITl92Z8HwhUPBEviJSZJs3h8HfHn44jAQ6YgRcfTVUrpzsyCSV6c5ikQrAPVz1DxwImZlw+eVhYLhDDkl2ZFIeJLvXkIiU0JIlcNppcOGFULduGBZ69GglASk6JQKRcuqHH3Z3A50xI9wXMGsWnHBCsiOT8kZVQyLljDuMGxdmAlu5Evr0gQcfhEMPTXZkUl4pEYiUI0uXwvXXw8SJoSQwdWoYEVSkJFQ1JFIObN0Kd94JRx0V5gh+5BGYM0dJQEqHSgQiKW7GDOjXLwwM17t3GCVUo4JKaVKJQCRFbd0Kt9wSGn9/+AEmTQp3BSsJSGlTiUAkBX34YbgXYPFiuOqqUAqoXbvw7UT2hUoEIilkyxa4+Wbo3DmUAt54A/7+dyUBiZdKBCIp4oMPQilgyRKVAqRsqUQgkmRbtoRhoTt3Ds/ffFOlAClbKhGIJNH774dSwGefhcHhHnpI00RK2VOJQCQJckoBJ54Y5g5+880wfaSSgCSDSgQiZWz6dOjfP5QCBgwIw0MoAUgyqUQgUkaysuD3vw93A2/bBpMnw8iRSgKSfCoRiJSBmTPDXcGffw7XXAMPPKAEIKlDJQKRGO3cCX/+c7g7eOtWeOsteOwxJQFJLSoRiMRk9eowRtDEiXDuufDEE1CnTrKjEtmbSgQiMXj7bWjbNvwcMSJMI6kkIKlKiUCkFGVnwx/+AKeeGm4ImzEDfvMbMEt2ZCL5U9WQSClZuRIuvXR399Dhw6FGjWRHJVI4JQKRUvDqq3DFFaFEMGZMSAgi5YWqhkRKYOtWuPZaOO88aN48zBqmJCDljRKByD5avBg6dQqNwb//fRg36Gc/S3ZUIsWnqiGRYnKHp58OJYHq1eH11+GMM5Idlci+U4lApBg2boTLLguNwZ06waefKglI+adEIFJEs2ZB+/Ywdizce28YMfTww5MdlUjJKRGIFCI7G/70pzBMxPbt8O674V6BypWTHZlI6VAbgUgBFi2Cvn3DoHEXXhjmDNAdwlLRqEQgkocffwyzhbVrB8uWwYsvwksvKQlIxRRrIjCz081siZktNbNB+axzkZktNLMFZvaPOOMRKYrPPgtzBtxyS2gIXrAALroo2VGJxCe2qiEzqwyMAH4JZAIzzWy8uy9MWKcFcBvQ2d2/N7ND4opHpDA7d4ZhIW67DQ44INwh3KuXxgmSii/OEsGxwFJ3X+bu24GxQM9c61wJjHD37wHcfXUcgYwZA02bQqVK4eeYMXEcRcqzL76Abt1g4MAwYNyCBeEOYSUBSQdxJoIGwFcJrzOjZYlaAi3NbLqZfWhmp+e1IzO7ysxmmdmsNWvWFCuIMWPgqqtgxYpwI9CKFeG1koFAKAWMGAHHHANz54YbxcaPh8MOS3ZkImUn2Y3FVYAWQDegF/C4mR2UeyV3H+XuGe6eUb9+/WIdYPDgMFdsoqyssFzS2/Ll4er/2muha1eYPz/0EFIpQNJNnInga6BRwuuG0bJEmcB4d9/h7l8CnxESQ6lZubJ4y6Xic4dRo6B163CT2BNPwIQJ0LBhsiMTSY44E8FMoIWZNTOz/YBLgPG51hlHKA1gZvUIVUXLSjOIxo2Lt1wqtq++gtNOg6uvDkNEzJsXho9WKUDSWWyJwN2zgWuBScAi4CV3X2Bm95hZj2i1ScBaM1sIvAPc7O5rSzOOoUPDwGCJqlcPyyV9uMNTT8HRR4dRQkeODENENGmS7MhEks/cPdkxFEtGRobPmjWrWNuMGRPaBFauDCWBoUOhd++YApSU4g6TJsEdd4RqoG7dYPRoaNYs2ZGJlC0zm+3uGXm9lxZDTPTurRN/OpoyJYwJNH166Db81FPQp0/oRiwiu+lfQiqc99+HU06Bk08OPYNGjoQlS6BfPyUBkbzo30IqjNmzw5AQnTuHrqCPPgpLl8KAAbDffsmOTiR1KRFIuTdvXpgzOCMDZsyA++8PA8X97ndQrVqyoxNJfWnRRiAV05IlMGRIGBm0Vi24+2644QaoXTvZkYmUL0oEUu58+SXccw88+2wYHO6228Lk8RoiWmTfKBFIuZGZCffdB08+CVWqhKv/W2+FQzRmrUiJKBFIytq0CT74AN57Lzw++CDcF3D11XD77ZovWKS0FCkRmFkNYIu77zSzlsCRwER33xFrdJJWVq+GadN2n/g/+STMFFapUpgp7LrrwkN3A4uUrqKWCKYCXczsYOANwjhCFwO6TUv2iXuo68856b/3XpgZDEJPn06dQt1/ly5w/PGhMVhE4lHURGDunmVmVwCPufuDZvZJjHFJBbRgQbjbN+fE/803YflBB4W+//37hxN/hw6w//7JjFQkvRQ5EZjZ8YQSwBXRssrxhCQV0YMPhoZdgAYNwvj/XbqEx1FH6Y5fkWQqaiK4gTC38KvRCKLNCaOFihRq4kQYNAjOPx8eeiiM+6Nhn0VSR5ESgbu/C7wLYGaVgO/c/fo4A5OK4bPPwgTwxxwDzzwDNWokOyIRya1IBXIz+4eZ1Y56D80HFprZzfGGJuXdxo1wzjlQtSqMG6ckIJKqiloz28rdNwLnABOBZsCv4wpKyr+dO+Gyy0KJ4OWXQ3WQiKSmoiaCqmZWlZAIxkf3D5SvGW2kTA0ZAq+9Bo88EiaDEZHUVdRE8HdgOVADmGpmTYCNcQUl5du//gX33guXXw7XXpvsaESkMPs8VaWZVYnmJS5T+zJVpZSdefPCDWBHHw3vvqv7AURSRUFTVRa1sfhAM3vYzGZFjz8TSgciu6xbFxqHa9cOpQIlAZHyoahVQ6OBTcBF0WMj8FRcQUn5k50NF18cRgj91780IJxIeVLUG8p+6u7nJ7y+W0NMSKJbb4XJk8MQ0ccdl+xoRKQ4iloi2GJmJ+a8MLPOwJZ4QpLy5rnn4OGHQ8Nw//7JjkZEiquoJYIBwLNmdmD0+nugbzwhSXkyaxZceWXoIvrww8mORkT2RVGHmPgUaGNmtaPXG83sBmBujLFJilu1Cs49F37yE3jppXAHsYiUP8Ua89HdN0Z3GAPcGEM8Uk5s3w4XXABr14bhI+rXT3ZEIrKvSjJVpcaPTGO/+12YTWzsWGjbNtnRiEhJlGQUeA0xkaZGjYK//S30FLr44mRHIyIlVWCJwMw2kfcJ34ADYolIUtr06aF3UPfuMHRosqMRkdJQYCJwd80UK7tkZobJZZo2hX/8AyprjjqRCiFtJghcuzZMjCL7Jisr9BDKyoJ//zvMMywiFUOsicDMTjezJWa21MwGFbDe+WbmZpbngEilYfhw6NcvDIQmxfPWW2GGsdmz4fnn4ec/T3ZEIlKaYksEZlYZGAF0B1oBvcysVR7r1QJ+B8yIKxYIDZvNm8NVV8HWrXEeqeJYuzYMJX3qqWGO4cmToUePZEclIqUtzhLBscBSd1/m7tuBsUDPPNa7F3gAiPX0XL166Ony2Wdq5CyMO7zwQrjyf+45uO02mDsXfvGLZEcmInGIMxE0AL5KeJ0ZLdvFzNoDjdz99YJ2ZGZX5QyBvWbNmn0O6Je/hF//Gu6/H+bP3+fdVGgrV8JZZ8Gll4ZG4dmz4Y9/hAPUR0ykwkpaY7GZVQIeBn5f2LruPsrdM9w9o34Jb2F9+GE48MBQRbRzZ4l2VaH8+CP85S/QqlVoR3nkEfjgA2jTJtmRiUjc4kwEXwONEl43jJblqAUcDUwxs+XAccD4OBuMAerV232SGzkyziOVH3PnwgknwA03QNeusGBBeK7uoSLpIc5EMBNoYWbNzGw/4BJgfM6b7r7B3eu5e1N3bwp8CPRw99jnobzsslBNdNttoW98utqyBW6/HTp0gC+/DPcGvP46NGmS7MhEpCzFlgii+YyvBSYBi4CX3H2Bmd1jZknte2IWGo6zs8Ndsvs4bXO59s47oUvon/4UEuOiRdCrV/hsRCS9xNpG4O4T3L2lu//U3YdGy+509/F5rNutLEoDOZo3h7vvDjdHvfpqWR01+datgyuuCD2Adu6EN9+Ep56CunWTHZmIJEva3Fmcl4EDw8iZ114L69cnO5p4ucOLL4Yuoc88A7fcAvPmhXsERCS9pXUiqFIFnngiTLBy223JjiYeO3aEuv+OHeGSS6BRI5g5Ex54INxbISKS1okAQkPpDTeENoNp05IdTelZvx4eeihUgfXuDZs3w9//Dh9+CO3aJTs6EUklaZ8IILQVNGkS7i3Yti3Z0ZTMsmVh0piGDUP1T8uW8J//wMKF4ferUpKpiESkQlIiAGrWDPcULFoU7jouj95/P0wd2aIFPPYYnHcefPxxGDDuzDOhkv7SIpIPnR4i3buH7pN//GNICOVBdja8/DIcfzx07gxvvx0G11u+HJ59VlNIikjRKBEkePRRqFEj9Yef2Lgx3B39s5/BRRfBmjXw17/CV1+FRNagQeH7EBHJoUSQ4JBD4M9/Do3GTzyR7Gj2tnIl3HRT6Plz443QuHG4B2LJEvjtb0MSExEpLiWCXPr1g5NPDg2t336b7GhCyWTy5DBFZPPmodRy5pnw0UcwdSqcc47GBBKRklEiyMUsdLPcuhWuvz55caxbF0ZKPfLIMC7Su++GUsCyZbvvCxARKQ1KBHlo0QLuugv++U8Yv9dgGPFxhxkzQqmkQQP4/e+hfv0wOUxmJjz4YKgOEhEpTUoE+bjpJmjdGn7zG3j88TBJS6VK4eeYMaV7rB9+CMfo0AGOOw5eeSUkg08/henTw6Bw1aqV7jFFRHLo9qJ8VK0aTs7HHx+SQXZ2WL5iRehVBOGO3ZJYuDDcv/Dss6EnUOvW4R6Ayy6DWrVKtm8RkaJSiaAAnTqFm81ykkCOrCwYPHjf9rl9O4wdCyedBEcdBaNGhQnhp00LJYBrrlESEJGypRJBITZtynv5ypUFb5edDWvXhj7+330XHrNnw+jRsHp16AH04INw+eVh1jQRkWRRIihEkyahOii3OnVg2LA9T/SJz7//fu9tKlWCs88OV/2//KWGfRCR1KBEUIihQ0ObQFbWnsvXroWbb4b99gtX9PXrh58dOoSfictyfjZoEBKIiEgqUSIoRE6D8G23hSEc6tcPE9lcdll4XrOmpncUkfJNiaAIevcueQ8hEZFUpVpqEZE0p0QgIpLmlAhERNKcEoGISJpTIhARSXNKBCIiaU6JQEQkzSkRiIikOSUCEZE0p0QgIpLmlAhERNKcEoGISJpTIigDY8bEO+exiEhJxJoIzOx0M1tiZkvNbFAe799oZgvNbK6ZvWVmTeKMJxnGjAnzGaxYAe675zxWMhCRVBFbIjCzysAIoDvQCuhlZq1yrfYxkOHuxwD/BB6MK55kGTx470ltSjLnsYhIaYuzRHAssNTdl7n7dmAs0DNxBXd/x91zTpMfAg1jjCcp8pvbuLA5j0VEykqciaAB8FXC68xoWX6uACbm9YaZXWVms8xs1po1a0oxxPg1bly85SIiZS0lGovN7DIgA3gor/fdfZS7Z7h7Rv369cs2uBIaOhSqV99zWfXqYbmISCqIMxF8DTRKeN0wWrYHMzsVGAz0cPdtMcaTFL17w6hR0KRJmNu4SZPwWlNfikiqiHPO4plACzNrRkgAlwCXJq5gZu2AvwOnu/vqGGNJKs15LCKpLLYSgbtnA9cCk4BFwEvuvsDM7jGzHtFqDwE1gZfN7BMzGx9XPCIikrdY2wjcfYK7t3T3n7r70GjZne4+Pnp+qrsf6u5to0ePgveYnnRDmojEKc6qISkFOTek5dyLkHNDGqi6SURKR0r0GpL86YY0EYmbEkGK0w1pIhI3JYIUpxvSRCRuSgQpTjekiUjclAhSXGnckKZeRyJSEPUaKgdKckOaeh2JSGFUIqjg1OtIRAqjRFDBqdeRiBRGiaCCU68jESmMEkEFp15HIlIYJYIKTr2ORKQw6jWUBtTrSEQKohKBFEi9jkQqPiUCKZB6HYlUfEoEUiD1OhKp+JQIpECl0etIjc0iqU2JQApU0l5HOY3NK1aA++7GZiUDkdShRCCF6t0bli+HnTvDz+L0FiqNxmaVKETipe6jEquSNjar+6pI/FQikFiVtLFZ3VdF4qdEILEqaWOzuq+KxE+JQGJV0sZmdV8ViZ8SgcSuJI3N6r4qEj8lAklp6r4qEj9z92THUCwZGRk+a9asZIch5UTTpuHkn1uTJqF0IpIuzGy2u2fk9Z5KBFKhlUZjs6qWpKJTIpAKraSNzapaknSgRCAVWkkbm3VntKQDJQKp0Era2Fxad0arRCGpTIlAKrySdF9NlTujS1qqSPb2kuLcPbYHcDqwBFgKDMrj/f2BF6P3ZwBNC9tnhw4dXKSsPP+8e/Xq7uF6PjyqVw/Li8Jsz21zHmZlF0Oyt8/ZR5Mm4fdu0qR422r7km/v7g7M8vzO1fm9UdIHUBn4AmgO7Ad8CrTKtc5vgL9Fzy8BXixsv0oEUtZK8k/YpEneiaBJk7LbR7K3T3YiSvftcyQrERwPTEp4fRtwW651JgHHR8+rAN8R3duQ30OJQMqT0vgnLmmpItnbJzsRpfv2OQpKBHG2ETQAvkp4nRkty3Mdd88GNgB1c+/IzK4ys1lmNmvNmjUxhStS+kraWA0lb6dI9vYlbXDX9iXbvijKRWOxu49y9wx3z6hfv36ywxEplpI0VkPJu8Ame/tkJ6J0375I8isqlPSBqoZESk2yGxtLsn2y68jTffscJKmNoAqwDGjG7sbio3Kt81v2bCx+qbD9KhGIlD/lOZFVhO3dC04EsQ46Z2ZnAI8SehCNdvehZnZPFNB4M6sGPAe0A9YBl7j7soL2qUHnRESKr6BB52Kds9jdJwATci27M+H5VuDCOGMQEZGClYvGYhERiY8SgYhImlMiEBFJc0oEIiJprtxNVWlma4A8Jh9MCfUI90KkKsVXMqkeH6R+jIqvZEoSXxN3z/OO3HKXCFKZmc3Kr3tWKlB8JZPq8UHqx6j4Siau+FQ1JCKS5pQIRETSnBJB6RqV7AAKofhKJtXjg9SPUfGVTCzxqY1ARCTNqUQgIpLmlAhERNKcEkExmVkjM3vHzBaa2QIz+10e63Qzsw1m9kn0uDOvfcUY43Izmxcde6+hWi0YbmZLzWyumbUvw9iOSPhcPjGzjWZ2Q651yvzzM7PRZrbazOYnLKtjZm+a2efRz4Pz2bZvtM7nZta3jGJ7yMwWR3+/V83soHy2LfC7EHOMQ8zs64S/4xn5bHu6mS2Jvo+DyjC+FxNiW25mn+SzbayfYX7nlDL9/uU3PrUe+c6zcBjQPnpeC/gMaJVrnW7Af5IY43KgXgHvnwFMBAw4DpiRpDgrA/8j3OiS1M8P6Aq0B+YnLHsQGBQ9HwQ8kMd2dQjzbtQBDo6eH1wGsf0KqBI9fyCv2IryXYg5xiHATUX4DnwBNGf3vCWtyiK+XO//GbgzGZ9hfueUsvz+qURQTO7+rbvPiZ5vAhax91zMqa4n8KwHHwIHmdlhSYjjFOALd0/6neLuPpUwJ0ainsAz0fNngHPy2PQ04E13X+fu3wNvAqfHHZu7v+Fhnm+AD4GGpXnM4srn8yuKY4Gl7r7M3bcDYwmfe6kqKD4zM+Ai4IXSPm5RFHBOKbPvnxJBCZhZU8KkOjPyePt4M/vUzCaa2VFlGxkOvGFms83sqjzebwB8lfA6k+Qks0vI/58vmZ9fjkPd/dvo+f+AQ/NYJxU+y/6EEl5eCvsuxO3aqPpqdD5VG6nw+XUBVrn75/m8X2afYa5zSpl9/5QI9pGZ1QReAW5w94253p5DqO5oA/w/YFwZh3eiu7cHugO/NbOuZXz8QpnZfkAP4OU83k7257cXD+XwlOtrbWaDgWxgTD6rJPO7MBL4KdAW+JZQ/ZKKelFwaaBMPsOCzilxf/+UCPaBmVUl/MHGuPu/cr/v7hvdfXP0fAJQ1czqlVV87v519HM18Cqh+J3oa6BRwuuG0bKy1B2Y4+6rcr+R7M8vwaqcKrPo5+o81knaZ2lm/YCzgN7RiWIvRfguxMbdV7n7j+6+E3g8n2Mn9btoZlWA84AX81unLD7DfM4pZfb9UyIopqg+8Ulgkbs/nM86P4nWw8yOJXzOa8sovhpmVivnOaFRcX6u1cYDfSw4DtiQUAQtK/lehSXz88tlPJDTC6Mv8O881pkE/MrMDo6qPn4VLYuVmZ0O3AL0cPesfNYpynchzhgT253OzefYM4EWZtYsKiVeQvjcy8qpwGJ3z8zrzbL4DAs4p5Td9y+ulvCK+gBOJBTR5gKfRI8zgAHAgGida4EFhB4QHwInlGF8zaPjfhrFMDhanhifASMIvTXmARll/BnWIJzYD0xYltTPj5CUvgV2EOpZrwDqAm8BnwOTgTrRuhnAEwnb9geWRo/Lyyi2pYS64Zzv4N+idQ8HJhT0XSjDz++56Ps1l3BSOyx3jNHrMwg9Zb6IK8a84ouWP53zvUtYt0w/wwLOKWX2/dMQEyIiaU5VQyIiaU6JQEQkzSkRiIikOSUCEZE0p0QgIpLmlAhEImb2o+05MmqpjYRpZk0TR74USSVVkh2ASArZ4u5tkx2ESFlTiUCkENF49A9GY9J/ZGY/i5Y3NbO3o0HV3jKzxtHyQy3MEfBp9Dgh2lVlM3s8GnP+DTM7IFr/+mgs+rlmNjZJv6akMSUCkd0OyFU1dHHCexvcvTXwV+DRaNn/A55x92MIg74Nj5YPB971MGhee8IdqQAtgBHufhSwHjg/Wj4IaBftZ0A8v5pI/nRnsUjEzDa7e808li8HfuHuy6LBwf7n7nXN7DvCsAk7ouXfuns9M1sDNHT3bQn7aEoYN75F9PpWoKq732dm/wU2E0ZZHefRgHsiZUUlApGi8XyeF8e2hOc/sruN7kzC2E/tgZnRiJgiZUaJQKRoLk74+UH0/H3CaJkAvYH3oudvAdcAmFllMzswv52aWSWgkbu/A9wKHAjsVSoRiZOuPER2O8D2nMD8v+6e04X0YDObS7iq7xUtuw54ysxuBtYAl0fLfweMMrMrCFf+1xBGvsxLZeD5KFkYMNzd15fS7yNSJGojEClE1EaQ4e7fJTsWkTioakhEJM2pRCAikuZUIhARSXNKBCIiaU6JQEQkzSkRiIikOSUCEZE09/8BTO6CFsMY2WAAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.clf()   # clear figure\n",
        "acc_values = history_dict['acc']\n",
        "val_acc_values = history_dict['val_acc']\n",
        "\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('DNN 2 layer Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "S5f-mfLstaPt",
        "outputId": "91ad13fe-4cc5-4fb0-8b2d-175a18f286dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2.summary()"
      ],
      "metadata": {
        "id": "RHQL-zkDthdZ",
        "outputId": "c3dc141e-90f5-4a6e-8447-fa0f82d4f715",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_7 (Dense)             (None, 32)                160032    \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 32)                1056      \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 32)                1056      \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 1)                 33        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 162,177\n",
            "Trainable params: 162,177\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy.ma.core import argmax\n",
        "pred = model2.predict(x_test)\n",
        "classes_x=argmax(pred,axis=-1)\n",
        "#accuracy\n",
        "accuracy_score(y_test,classes_x)"
      ],
      "metadata": {
        "id": "842ZVBoOtlGu",
        "outputId": "aeed84cd-ffae-4f20-96cd-6a8bdb442112",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "782/782 [==============================] - 2s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Welcome To Colaboratory",
      "toc_visible": true,
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}