COMPARISION_THREE_MODEL_CURVES.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "COMPARISION_THREE_MODEL_CURVES.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdYxnXikoh5-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PfNwx4mWonxV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "36acc216-d571-45ee-f00b-eb62b36695dd"
      },
      "source": [
        "NUM_WORDS = 10000\n",
        "(train_data,train_labels),(test_data,test_labels)=keras.datasets.imdb.load_data(num_words=NUM_WORDS)\n",
        "def multi_hot_sequences(sequences,dimension):\n",
        "    #create all-zero matrix of shape (len (sequences),dimension)\n",
        "    results = np.zeros((len(sequences),dimension))\n",
        "    for i, word_indices in enumerate (sequences):\n",
        "        results[i, word_indices]=1.0\n",
        "    return results\n",
        "\n",
        "train_data = multi_hot_sequences(train_data,dimension=NUM_WORDS)\n",
        "test_data = multi_hot_sequences(test_data,dimension=NUM_WORDS)\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "17465344/17464789 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ic5FamUeoqQ1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "650eab9d-ee9b-40b1-a9e1-ea3f2f8b9e86"
      },
      "source": [
        "plt.plot(train_data[0])"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f3be6962400>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "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": "PM28WC1Uow8J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "c9a00c99-72e1-41e6-9575-50a47b3fdb0c"
      },
      "source": [
        "plt.plot(test_data[0])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f3bdf67c7b8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAASrUlEQVR4nO3dfZBddX3H8feXLIlAUB6yYEgCG+rqkFIruENhdFqmoAS05I8+TDJ1QEvNTFtaW5y2YexQpX/Uh46tTFM1PtTWqSBax6YYJ7ZIp9YRymYQTIDIyoMkglkQCEghiX77xz3By3J3701y7t6c375fMzs553d+997f2d/ZT879nafITCRJzXfEoBsgSaqHgS5JhTDQJakQBrokFcJAl6RCDA3qgxctWpQjIyOD+nhJaqQtW7Y8lpnDnZYNLNBHRkYYHx8f1MdLUiNFxEPTLXPIRZIKYaBLUiEMdEkqhIEuSYUw0CWpEF0DPSI+HRG7ImLrNMsjIq6LiImIuCsizq6/mZKkbnrZQ/8MsHKG5RcDo9XPWuCjh94sSdKB6noeemb+d0SMzFBlFfDP2boP760RcVxELM7MR2pq44s8/dxefuG9XwPgE5eN8clv3M9bXruYD23eznmnn8gpxx3FW1+7mPlDR/Dks3v59zt/wAVnnMyS447ig5vv5Yo3Luf815zE5NPPs+WhJ1h55isBmNj1DJNPP895P3fijJ9/y/ZdjJ60kKXHH33I63Lvo7t55rl9jI2ccMjvpWb48h07uXDFySxcMLBLQGq15aEnOHr+PM5Y/PIDet3Erqd57Jk9nHv6zH9vOjDRy/3Qq0C/KTPP7LDsJuD9mfk/1fzNwJ9n5kuuGoqItbT24jn11FNf/9BD054fP60Pf20713194oBf1+7B97+Fiz/yDe55ZDfb3ncRxywYYmTdV15YNpORdV9h4YIhtr7vokNqw/736uUzVYY7H36SVeu/yarXncJHVp816ObU4mC3Ybf9gxcRWzJzrNOyWT0ompkbMnMsM8eGhzteudrV4z/eU0tbdvzoWQB+chAP+Hjm+X21tEFzy4/3tLabH+5+bsAtUanqCPSdwLK2+aVVmSRpFtUR6BuBy6qzXc4FnurX+LkkaXpdj8xExPXA+cCiiNgB/CVwJEBmfgzYBFwCTADPAu/oV2MlSdPr5SyXNV2WJ/AHtbVIknRQvFJUkgphoEtSIQx0SSqEgS5JhTDQJakQBrokFaJxgR5R7/sdxJX/knRYalyg16bm/xikXrkToX6Zu4EuzbJwL0J9ZqBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHQvw9YsSTc29VnjAr2uy6e9CFuDUvcN5qT9GhfoUtN5cy71i4EuzRJvzqV+a1yg1zUO6U6SpNI0LtBr506TpEIY6JJUCANdkgphoEtSIQx0SSqEgS5JhTDQJakQBrokFaKnQI+IlRGxPSImImJdh+WnRsQtEXFHRNwVEZfU39Q+8QojSYXoGugRMQ9YD1wMrADWRMSKKdX+ArgxM88CVgP/UHdDX2iPN+eSpI562UM/B5jIzPszcw9wA7BqSp0EXl5NvwL4QX1NlMril0L1Sy+BvgR4uG1+R1XW7r3A2yJiB7AJ+MNObxQRayNiPCLGJycnD6K5UnN521z1W10HRdcAn8nMpcAlwGcj4iXvnZkbMnMsM8eGh4dr+mhJEvQW6DuBZW3zS6uydlcANwJk5reAlwGL6migJKk3vQT67cBoRCyPiPm0DnpunFLn+8AFABFxBq1Ad0xFkmZR10DPzH3AlcBm4B5aZ7Nsi4hrI+LSqtq7gXdGxJ3A9cDbM30uiyTNpqFeKmXmJloHO9vLrmmbvht4Q71NkyQdCK8UlaRCGOiSVAgDXZIKMecDva6HTkvdeJqA+q1xgV7X1XbhZXsaELc89UvjAl2S1JmBLs0yR17ULwa6NEsc5VO/GeiSVAgDXZIK0bhAr+vUL281I6k0zQv0mg8p1fVIO0katMYFuiSpMwNdkgphoEtSIeZ8oHsvF0mlmPOBLkmlaFyg13VWijfnklSaxgW6QySS1FnjAl1qPPdJ1CcGuiQVwkCXZolHbdRvBrokFcJAl6RCNC7QvUmiJHXWuECXJHVmoEtSIQx0SSrEnA90x+Q1W9zU1G+NC/S6bsHirVw0MG576pOeAj0iVkbE9oiYiIh109T5rYi4OyK2RcTn6m2mJKmboW4VImIesB54E7ADuD0iNmbm3W11RoGrgTdk5hMRcVK/GuwQiSR11sse+jnARGben5l7gBuAVVPqvBNYn5lPAGTmrnqbKUnqppdAXwI83Da/oypr92rg1RHxzYi4NSJWdnqjiFgbEeMRMT45OXlwLZaazm+Z6pO6DooOAaPA+cAa4BMRcdzUSpm5ITPHMnNseHi4po+WmsFjoeq3XgJ9J7CsbX5pVdZuB7AxM/dm5gPAd2kFvCRplvQS6LcDoxGxPCLmA6uBjVPqfJnW3jkRsYjWEMz9NbZTktRF10DPzH3AlcBm4B7gxszcFhHXRsSlVbXNwOMRcTdwC/Cnmfl4Pxrs8KMkddb1tEWAzNwEbJpSdk3bdAJXVT99Vddpi57+KKk0jbtStG5eMSqpFHM+0N1Tl1SKxgW693KRpM4aF+iSpM4aF+gOkUhSZ40LdElSZwa6JBXCQJdmWXp5nPrEQJdmSXhqlfrMQJekQhjoklSIBga644+S1EkDA12S1MmcD3T39zVb0qvi1GdzPtAlqRQNDPR6Tv3yBDINSrj1qU8aGOiSpE4aGOiOQ0pSJw0MdElSJwa6JBXCQJdmmTfnUr8Y6NIs8eZc6jcDXZIKYaBLUiEaF+hePS1JnTUu0CVJnc35QPeGSZJK0bhA90QBSeqscYHuDrUkdda4QK+b5wZLKkXjAt09dEnqrKdAj4iVEbE9IiYiYt0M9X49IjIixuproiSpF10DPSLmAeuBi4EVwJqIWNGh3rHAu4Db6m6kJKm7XvbQzwEmMvP+zNwD3ACs6lDvr4APAM/V2D6pOA4bql96CfQlwMNt8zuqshdExNnAssz8ykxvFBFrI2I8IsYnJycPuLFSk3n8Xf12yAdFI+II4MPAu7vVzcwNmTmWmWPDw8OH+tGSpDa9BPpOYFnb/NKqbL9jgTOB/4qIB4FzgY39OjDqvaQlqbNeAv12YDQilkfEfGA1sHH/wsx8KjMXZeZIZo4AtwKXZuZ4X1osSeqoa6Bn5j7gSmAzcA9wY2Zui4hrI+LSfjew37yXi2aLm5r6baiXSpm5Cdg0peyaaeqef+jNml5Qz5ElrxDVoLjpqV8ad6WoJKkzA12SCmGgS1IhGhfonrYoSZ01LtAlSZ0Z6JJUCANdmmWej65+MdAlqRAGujRLvKBI/da4QPfrqiR11rhAr5v/P0gqxZwPdEkqReMCva5xSIczJZWmcYEuSerMQJekQszZQPdgqKTSNC7QPW1RkjprXqDX/H4eHJVUisYFuiSpMwNdmmWOGqpfDHRJKoSBLs0Sj9eo3xoX6HWf5eLXX80WtzX1W+MCXZLUWeMC3Xu5qOnc9tQvjQt0SVJnBrokFcJAl6RCNC7QvZeLJHXWuECXJHXWU6BHxMqI2B4RExGxrsPyqyLi7oi4KyJujojT6m+qJGkmXQM9IuYB64GLgRXAmohYMaXaHcBYZr4W+CLwwbobKkmaWS976OcAE5l5f2buAW4AVrVXyMxbMvPZavZWYGm9zZTK4WEg9Usvgb4EeLhtfkdVNp0rgK92WhARayNiPCLGJycne2+lJKmrWg+KRsTbgDHgQ52WZ+aGzBzLzLHh4eE6P1o67HmFqPptqIc6O4FlbfNLq7IXiYgLgfcAv5KZz9fTvP7zNEhJpehlD/12YDQilkfEfGA1sLG9QkScBXwcuDQzd9XfzPrVdU8YSTpcdA30zNwHXAlsBu4BbszMbRFxbURcWlX7ELAQ+EJEfDsiNk7zdpKkPullyIXM3ARsmlJ2Tdv0hTW3S5J0gLxSVJIKYaBLUiEaF+jpZRmS1FHjAl2S1JmBLkmFaF6g1zTi4gVFkkrTvECvmRcYabalexPqkzkf6JJUijkf6O4sSSrFnA90SSrFnA10x841KOHGpz6Zs4EuSaUx0CWpEAa6JBXCQJekQjQu0D3LUJI6a1ygS5I6M9AlqRAGuiQVwkCXpELM+UD3CUiabd5tUf0y5wNdmi1e8a9+m8OB7l+XpLLM4UCXpLIY6JJUCANdkgphoEtSIQx0SSpE4wLdc3glqbPGBbokqTMDXZIK0VOgR8TKiNgeERMRsa7D8gUR8flq+W0RMVJ3QyVJM+sa6BExD1gPXAysANZExIop1a4AnsjMVwF/C3yg7oZKkmYW3Q4yRsR5wHsz86Jq/mqAzPzrtjqbqzrfiogh4FFgOGd487GxsRwfHz/gBl/1+W/zpTt2HvDr2o2etJD7dj0DwPJFxzB0RLwwP3rSwhlf22u9XtT5Xjr8TT7zPE8+uxcop88Pdhue69v+H10wyq/94ikH9dqI2JKZY52WDfXw+iXAw23zO4Bfmq5OZu6LiKeAE4HHpjRkLbAW4NRTT+2p8VOtPPOVPQX6vCOCn/z0Z/+fzB86gj37fgrA6MkLWXDkEWzduZszFh8LwO7n9vLD3c8zenL3QD/txKO71uvF/j/wOt5Lh79XnbSQr259lNefdjwnv3zBoJtTi/t2PcPR8+cd8Db85P/tZfLp7n9vpXrFUUf25X17CfTaZOYGYAO09tAP5j3e/POv5MH3v6XWdklSCXo5KLoTWNY2v7Qq61inGnJ5BfB4HQ2UJPWml0C/HRiNiOURMR9YDWycUmcjcHk1/RvA12caP5ck1a/rkEs1Jn4lsBmYB3w6M7dFxLXAeGZuBD4FfDYiJoAf0Qp9SdIs6mkMPTM3AZumlF3TNv0c8Jv1Nk2SdCC8UlSSCmGgS1IhDHRJKoSBLkmF6Hrpf98+OGISeOggX76IKVehzgGu89zgOs8Nh7LOp2XmcKcFAwv0QxER49Pdy6BUrvPc4DrPDf1aZ4dcJKkQBrokFaKpgb5h0A0YANd5bnCd54a+rHMjx9AlSS/V1D10SdIUBrokFaJxgd7tgdVNERHLIuKWiLg7IrZFxLuq8hMi4j8i4r7q3+Or8oiI66r1visizm57r8ur+vdFxOXTfebhIiLmRcQdEXFTNb+8erj4RPWw8flV+bQPH4+Iq6vy7RFx0WDWpDcRcVxEfDEi7o2IeyLivNL7OSL+pNqut0bE9RHxstL6OSI+HRG7ImJrW1lt/RoRr4+I71SvuS4iomujMrMxP7Ru3/s94HRgPnAnsGLQ7TrIdVkMnF1NHwt8l9ZDuD8IrKvK1wEfqKYvAb4KBHAucFtVfgJwf/Xv8dX08YNevy7rfhXwOeCmav5GYHU1/THg96rp3wc+Vk2vBj5fTa+o+n4BsLzaJuYNer1mWN9/An63mp4PHFdyP9N6JOUDwFFt/fv20voZ+GXgbGBrW1lt/Qr8b1U3qtde3LVNg/6lHOAv8Dxgc9v81cDVg25XTev2b8CbgO3A4qpsMbC9mv44sKat/vZq+Rrg423lL6p3uP3QeuLVzcCvAjdVG+tjwNDUPqZ1D/7zqumhql5M7ff2eofbD62ndz1AdQLC1P4rsZ/52TOGT6j67SbgohL7GRiZEui19Gu17N628hfVm+6naUMunR5YvWRAbalN9RXzLOA24OTMfKRa9ChwcjU93bo37Xfyd8CfAT+t5k8EnszMfdV8e/tf9PBxYP/Dx5u0zsuBSeAfq2GmT0bEMRTcz5m5E/gb4PvAI7T6bQtl9/N+dfXrkmp6avmMmhboxYmIhcC/An+cmbvbl2Xrv+ZiziuNiLcCuzJzy6DbMouGaH0t/2hmngX8mNZX8RcU2M/HA6to/Wd2CnAMsHKgjRqAQfRr0wK9lwdWN0ZEHEkrzP8lM79UFf8wIhZXyxcDu6ry6da9Sb+TNwCXRsSDwA20hl0+AhwXrYeLw4vbP93Dx5u0zjuAHZl5WzX/RVoBX3I/Xwg8kJmTmbkX+BKtvi+5n/erq193VtNTy2fUtEDv5YHVjVAdsf4UcE9mfrhtUfsDty+nNba+v/yy6mj5ucBT1Ve7zcCbI+L4as/ozVXZYSczr87MpZk5Qqvvvp6Zvw3cQuvh4vDSde708PGNwOrq7IjlwCitA0iHncx8FHg4Il5TFV0A3E3B/UxrqOXciDi62s73r3Ox/dymln6tlu2OiHOr3+Flbe81vUEfVDiIgxCX0Doj5HvAewbdnkNYjzfS+jp2F/Dt6ucSWmOHNwP3Af8JnFDVD2B9td7fAcba3ut3gInq5x2DXrce1/98fnaWy+m0/lAngC8AC6ryl1XzE9Xy09te/57qd7GdHo7+D3hdXweMV339ZVpnMxTdz8D7gHuBrcBnaZ2pUlQ/A9fTOkawl9Y3sSvq7FdgrPr9fQ/4e6YcWO/046X/klSIpg25SJKmYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQvw/h8TjXnlBsfYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tAgiZvhZpOhf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#CREATE A BASE MODEL"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nu4zBLP6oxoA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "outputId": "df17feb5-d7ef-47fe-9c65-7b7d4416e9ba"
      },
      "source": [
        "baseline_model= keras.Sequential([\n",
        "    keras.layers.Dense(16,activation=tf.nn.relu,input_shape=(NUM_WORDS,)),\n",
        "    keras.layers.Dense(16,activation=tf.nn.relu),\n",
        "    keras.layers.Dense(1,activation=tf.nn.sigmoid)\n",
        "])\n",
        "baseline_model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy','binary_crossentropy'])\n",
        "baseline_model.summary()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_3 (Dense)              (None, 16)                160016    \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 160,305\n",
            "Trainable params: 160,305\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CECHag5so09n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "outputId": "da6d1f6b-4819-46a7-e877-31ce360de03d"
      },
      "source": [
        "baseline_history = baseline_model.fit(train_data,train_labels,epochs=20,batch_size=512,validation_data=(test_data,test_labels),verbose=2)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "49/49 - 2s - loss: 0.4629 - accuracy: 0.8197 - binary_crossentropy: 0.4629 - val_loss: 0.3361 - val_accuracy: 0.8707 - val_binary_crossentropy: 0.3361\n",
            "Epoch 2/20\n",
            "49/49 - 1s - loss: 0.2449 - accuracy: 0.9133 - binary_crossentropy: 0.2449 - val_loss: 0.2848 - val_accuracy: 0.8872 - val_binary_crossentropy: 0.2848\n",
            "Epoch 3/20\n",
            "49/49 - 1s - loss: 0.1820 - accuracy: 0.9368 - binary_crossentropy: 0.1820 - val_loss: 0.2876 - val_accuracy: 0.8856 - val_binary_crossentropy: 0.2876\n",
            "Epoch 4/20\n",
            "49/49 - 1s - loss: 0.1468 - accuracy: 0.9520 - binary_crossentropy: 0.1468 - val_loss: 0.3108 - val_accuracy: 0.8782 - val_binary_crossentropy: 0.3108\n",
            "Epoch 5/20\n",
            "49/49 - 1s - loss: 0.1210 - accuracy: 0.9616 - binary_crossentropy: 0.1210 - val_loss: 0.3319 - val_accuracy: 0.8760 - val_binary_crossentropy: 0.3319\n",
            "Epoch 6/20\n",
            "49/49 - 1s - loss: 0.1001 - accuracy: 0.9691 - binary_crossentropy: 0.1001 - val_loss: 0.3617 - val_accuracy: 0.8724 - val_binary_crossentropy: 0.3617\n",
            "Epoch 7/20\n",
            "49/49 - 1s - loss: 0.0833 - accuracy: 0.9756 - binary_crossentropy: 0.0833 - val_loss: 0.3935 - val_accuracy: 0.8692 - val_binary_crossentropy: 0.3935\n",
            "Epoch 8/20\n",
            "49/49 - 1s - loss: 0.0680 - accuracy: 0.9824 - binary_crossentropy: 0.0680 - val_loss: 0.4328 - val_accuracy: 0.8648 - val_binary_crossentropy: 0.4328\n",
            "Epoch 9/20\n",
            "49/49 - 1s - loss: 0.0551 - accuracy: 0.9874 - binary_crossentropy: 0.0551 - val_loss: 0.4733 - val_accuracy: 0.8631 - val_binary_crossentropy: 0.4733\n",
            "Epoch 10/20\n",
            "49/49 - 1s - loss: 0.0440 - accuracy: 0.9914 - binary_crossentropy: 0.0440 - val_loss: 0.5116 - val_accuracy: 0.8604 - val_binary_crossentropy: 0.5116\n",
            "Epoch 11/20\n",
            "49/49 - 1s - loss: 0.0350 - accuracy: 0.9942 - binary_crossentropy: 0.0350 - val_loss: 0.5506 - val_accuracy: 0.8587 - val_binary_crossentropy: 0.5506\n",
            "Epoch 12/20\n",
            "49/49 - 1s - loss: 0.0285 - accuracy: 0.9960 - binary_crossentropy: 0.0285 - val_loss: 0.5938 - val_accuracy: 0.8576 - val_binary_crossentropy: 0.5938\n",
            "Epoch 13/20\n",
            "49/49 - 1s - loss: 0.0222 - accuracy: 0.9977 - binary_crossentropy: 0.0222 - val_loss: 0.6436 - val_accuracy: 0.8563 - val_binary_crossentropy: 0.6436\n",
            "Epoch 14/20\n",
            "49/49 - 1s - loss: 0.0168 - accuracy: 0.9986 - binary_crossentropy: 0.0168 - val_loss: 0.6877 - val_accuracy: 0.8540 - val_binary_crossentropy: 0.6877\n",
            "Epoch 15/20\n",
            "49/49 - 1s - loss: 0.0134 - accuracy: 0.9991 - binary_crossentropy: 0.0134 - val_loss: 0.7017 - val_accuracy: 0.8538 - val_binary_crossentropy: 0.7017\n",
            "Epoch 16/20\n",
            "49/49 - 1s - loss: 0.0103 - accuracy: 0.9995 - binary_crossentropy: 0.0103 - val_loss: 0.7372 - val_accuracy: 0.8536 - val_binary_crossentropy: 0.7372\n",
            "Epoch 17/20\n",
            "49/49 - 1s - loss: 0.0080 - accuracy: 0.9996 - binary_crossentropy: 0.0080 - val_loss: 0.7737 - val_accuracy: 0.8535 - val_binary_crossentropy: 0.7737\n",
            "Epoch 18/20\n",
            "49/49 - 1s - loss: 0.0063 - accuracy: 0.9998 - binary_crossentropy: 0.0063 - val_loss: 0.7991 - val_accuracy: 0.8526 - val_binary_crossentropy: 0.7991\n",
            "Epoch 19/20\n",
            "49/49 - 1s - loss: 0.0052 - accuracy: 0.9998 - binary_crossentropy: 0.0052 - val_loss: 0.8217 - val_accuracy: 0.8525 - val_binary_crossentropy: 0.8217\n",
            "Epoch 20/20\n",
            "49/49 - 1s - loss: 0.0042 - accuracy: 0.9999 - binary_crossentropy: 0.0042 - val_loss: 0.8507 - val_accuracy: 0.8522 - val_binary_crossentropy: 0.8507\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzTPJTaOo5vG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#CREATE A SMALLER MODEL"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1CNRWpiOpTGC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "outputId": "71a9c07f-718a-4593-b3a7-f6a3ea95f7fe"
      },
      "source": [
        "smaller_model = keras.Sequential([\n",
        "    keras.layers.Dense(4,activation=tf.nn.relu,input_shape=(NUM_WORDS,)),\n",
        "    keras.layers.Dense(4,activation=tf.nn.relu),\n",
        "    keras.layers.Dense(1,activation=tf.nn.sigmoid)\n",
        "])\n",
        "smaller_model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy','binary_crossentropy'])\n",
        "smaller_model.summary()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_6 (Dense)              (None, 4)                 40004     \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 4)                 20        \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 1)                 5         \n",
            "=================================================================\n",
            "Total params: 40,029\n",
            "Trainable params: 40,029\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hhNCiGuzpm5d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "outputId": "e7584af1-e56d-4d67-f618-aecaf6bd00ac"
      },
      "source": [
        "smaller_history = smaller_model.fit(train_data,train_labels,epochs=20,batch_size=512,validation_data=(test_data,test_labels),verbose=2)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "49/49 - 2s - loss: 0.5863 - accuracy: 0.7707 - binary_crossentropy: 0.5863 - val_loss: 0.4817 - val_accuracy: 0.8535 - val_binary_crossentropy: 0.4817\n",
            "Epoch 2/20\n",
            "49/49 - 1s - loss: 0.3855 - accuracy: 0.8886 - binary_crossentropy: 0.3855 - val_loss: 0.3591 - val_accuracy: 0.8786 - val_binary_crossentropy: 0.3591\n",
            "Epoch 3/20\n",
            "49/49 - 1s - loss: 0.2821 - accuracy: 0.9129 - binary_crossentropy: 0.2821 - val_loss: 0.3104 - val_accuracy: 0.8849 - val_binary_crossentropy: 0.3104\n",
            "Epoch 4/20\n",
            "49/49 - 1s - loss: 0.2281 - accuracy: 0.9277 - binary_crossentropy: 0.2281 - val_loss: 0.2903 - val_accuracy: 0.8870 - val_binary_crossentropy: 0.2903\n",
            "Epoch 5/20\n",
            "49/49 - 1s - loss: 0.1942 - accuracy: 0.9377 - binary_crossentropy: 0.1942 - val_loss: 0.2852 - val_accuracy: 0.8870 - val_binary_crossentropy: 0.2852\n",
            "Epoch 6/20\n",
            "49/49 - 1s - loss: 0.1699 - accuracy: 0.9450 - binary_crossentropy: 0.1699 - val_loss: 0.2854 - val_accuracy: 0.8866 - val_binary_crossentropy: 0.2854\n",
            "Epoch 7/20\n",
            "49/49 - 1s - loss: 0.1511 - accuracy: 0.9525 - binary_crossentropy: 0.1511 - val_loss: 0.2961 - val_accuracy: 0.8824 - val_binary_crossentropy: 0.2961\n",
            "Epoch 8/20\n",
            "49/49 - 1s - loss: 0.1356 - accuracy: 0.9578 - binary_crossentropy: 0.1356 - val_loss: 0.3001 - val_accuracy: 0.8826 - val_binary_crossentropy: 0.3001\n",
            "Epoch 9/20\n",
            "49/49 - 1s - loss: 0.1229 - accuracy: 0.9632 - binary_crossentropy: 0.1229 - val_loss: 0.3105 - val_accuracy: 0.8798 - val_binary_crossentropy: 0.3105\n",
            "Epoch 10/20\n",
            "49/49 - 1s - loss: 0.1114 - accuracy: 0.9680 - binary_crossentropy: 0.1114 - val_loss: 0.3214 - val_accuracy: 0.8779 - val_binary_crossentropy: 0.3214\n",
            "Epoch 11/20\n",
            "49/49 - 1s - loss: 0.1017 - accuracy: 0.9706 - binary_crossentropy: 0.1017 - val_loss: 0.3346 - val_accuracy: 0.8751 - val_binary_crossentropy: 0.3346\n",
            "Epoch 12/20\n",
            "49/49 - 1s - loss: 0.0934 - accuracy: 0.9742 - binary_crossentropy: 0.0934 - val_loss: 0.3484 - val_accuracy: 0.8728 - val_binary_crossentropy: 0.3484\n",
            "Epoch 13/20\n",
            "49/49 - 1s - loss: 0.0851 - accuracy: 0.9772 - binary_crossentropy: 0.0851 - val_loss: 0.3628 - val_accuracy: 0.8704 - val_binary_crossentropy: 0.3628\n",
            "Epoch 14/20\n",
            "49/49 - 1s - loss: 0.0783 - accuracy: 0.9800 - binary_crossentropy: 0.0783 - val_loss: 0.3807 - val_accuracy: 0.8685 - val_binary_crossentropy: 0.3807\n",
            "Epoch 15/20\n",
            "49/49 - 1s - loss: 0.0713 - accuracy: 0.9831 - binary_crossentropy: 0.0713 - val_loss: 0.3940 - val_accuracy: 0.8672 - val_binary_crossentropy: 0.3940\n",
            "Epoch 16/20\n",
            "49/49 - 1s - loss: 0.0651 - accuracy: 0.9856 - binary_crossentropy: 0.0651 - val_loss: 0.4117 - val_accuracy: 0.8647 - val_binary_crossentropy: 0.4117\n",
            "Epoch 17/20\n",
            "49/49 - 1s - loss: 0.0594 - accuracy: 0.9878 - binary_crossentropy: 0.0594 - val_loss: 0.4243 - val_accuracy: 0.8662 - val_binary_crossentropy: 0.4243\n",
            "Epoch 18/20\n",
            "49/49 - 1s - loss: 0.0548 - accuracy: 0.9894 - binary_crossentropy: 0.0548 - val_loss: 0.4446 - val_accuracy: 0.8624 - val_binary_crossentropy: 0.4446\n",
            "Epoch 19/20\n",
            "49/49 - 1s - loss: 0.0500 - accuracy: 0.9908 - binary_crossentropy: 0.0500 - val_loss: 0.4566 - val_accuracy: 0.8641 - val_binary_crossentropy: 0.4566\n",
            "Epoch 20/20\n",
            "49/49 - 1s - loss: 0.0451 - accuracy: 0.9924 - binary_crossentropy: 0.0451 - val_loss: 0.4772 - val_accuracy: 0.8608 - val_binary_crossentropy: 0.4772\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q_BkrW4ep0q3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#CREATE BIGGER MODEL"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k5PZE3yGp6zk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "outputId": "cb46b415-3c09-4d7f-aee6-96367a0bc15c"
      },
      "source": [
        "bigger_model = keras.Sequential([\n",
        "    keras.layers.Dense(512,activation=tf.nn.relu,input_shape=(NUM_WORDS,)),\n",
        "    keras.layers.Dense(512,activation=tf.nn.relu),\n",
        "    keras.layers.Dense(1,activation=tf.nn.sigmoid)\n",
        "])\n",
        "bigger_model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy','binary_crossentropy'])\n",
        "bigger_model.summary()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_9 (Dense)              (None, 512)               5120512   \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 1)                 513       \n",
            "=================================================================\n",
            "Total params: 5,383,681\n",
            "Trainable params: 5,383,681\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eCJ54zpIqFzD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "outputId": "22c92cdc-2b60-44ba-9cd6-2aeed1218310"
      },
      "source": [
        "bigger_history = bigger_model.fit(train_data,train_labels,epochs=20,batch_size=512,validation_data=(test_data,test_labels),verbose=2)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "49/49 - 2s - loss: 0.3491 - accuracy: 0.8483 - binary_crossentropy: 0.3491 - val_loss: 0.2923 - val_accuracy: 0.8812 - val_binary_crossentropy: 0.2923\n",
            "Epoch 2/20\n",
            "49/49 - 1s - loss: 0.1415 - accuracy: 0.9484 - binary_crossentropy: 0.1415 - val_loss: 0.3235 - val_accuracy: 0.8727 - val_binary_crossentropy: 0.3235\n",
            "Epoch 3/20\n",
            "49/49 - 1s - loss: 0.0428 - accuracy: 0.9880 - binary_crossentropy: 0.0428 - val_loss: 0.4488 - val_accuracy: 0.8703 - val_binary_crossentropy: 0.4488\n",
            "Epoch 4/20\n",
            "49/49 - 1s - loss: 0.0051 - accuracy: 0.9994 - binary_crossentropy: 0.0051 - val_loss: 0.5923 - val_accuracy: 0.8700 - val_binary_crossentropy: 0.5923\n",
            "Epoch 5/20\n",
            "49/49 - 1s - loss: 0.0015 - accuracy: 1.0000 - binary_crossentropy: 0.0015 - val_loss: 0.6599 - val_accuracy: 0.8715 - val_binary_crossentropy: 0.6599\n",
            "Epoch 6/20\n",
            "49/49 - 1s - loss: 2.3065e-04 - accuracy: 1.0000 - binary_crossentropy: 2.3065e-04 - val_loss: 0.7116 - val_accuracy: 0.8723 - val_binary_crossentropy: 0.7116\n",
            "Epoch 7/20\n",
            "49/49 - 1s - loss: 1.4166e-04 - accuracy: 1.0000 - binary_crossentropy: 1.4166e-04 - val_loss: 0.7426 - val_accuracy: 0.8724 - val_binary_crossentropy: 0.7426\n",
            "Epoch 8/20\n",
            "49/49 - 1s - loss: 1.0221e-04 - accuracy: 1.0000 - binary_crossentropy: 1.0221e-04 - val_loss: 0.7666 - val_accuracy: 0.8723 - val_binary_crossentropy: 0.7666\n",
            "Epoch 9/20\n",
            "49/49 - 1s - loss: 7.8765e-05 - accuracy: 1.0000 - binary_crossentropy: 7.8765e-05 - val_loss: 0.7856 - val_accuracy: 0.8728 - val_binary_crossentropy: 0.7856\n",
            "Epoch 10/20\n",
            "49/49 - 1s - loss: 6.2784e-05 - accuracy: 1.0000 - binary_crossentropy: 6.2784e-05 - val_loss: 0.8023 - val_accuracy: 0.8725 - val_binary_crossentropy: 0.8023\n",
            "Epoch 11/20\n",
            "49/49 - 1s - loss: 5.1525e-05 - accuracy: 1.0000 - binary_crossentropy: 5.1525e-05 - val_loss: 0.8168 - val_accuracy: 0.8727 - val_binary_crossentropy: 0.8168\n",
            "Epoch 12/20\n",
            "49/49 - 1s - loss: 4.3066e-05 - accuracy: 1.0000 - binary_crossentropy: 4.3066e-05 - val_loss: 0.8298 - val_accuracy: 0.8727 - val_binary_crossentropy: 0.8298\n",
            "Epoch 13/20\n",
            "49/49 - 1s - loss: 3.6592e-05 - accuracy: 1.0000 - binary_crossentropy: 3.6592e-05 - val_loss: 0.8417 - val_accuracy: 0.8725 - val_binary_crossentropy: 0.8417\n",
            "Epoch 14/20\n",
            "49/49 - 1s - loss: 3.1470e-05 - accuracy: 1.0000 - binary_crossentropy: 3.1470e-05 - val_loss: 0.8531 - val_accuracy: 0.8722 - val_binary_crossentropy: 0.8531\n",
            "Epoch 15/20\n",
            "49/49 - 1s - loss: 2.7393e-05 - accuracy: 1.0000 - binary_crossentropy: 2.7393e-05 - val_loss: 0.8632 - val_accuracy: 0.8725 - val_binary_crossentropy: 0.8632\n",
            "Epoch 16/20\n",
            "49/49 - 1s - loss: 2.4040e-05 - accuracy: 1.0000 - binary_crossentropy: 2.4040e-05 - val_loss: 0.8728 - val_accuracy: 0.8724 - val_binary_crossentropy: 0.8728\n",
            "Epoch 17/20\n",
            "49/49 - 1s - loss: 2.1240e-05 - accuracy: 1.0000 - binary_crossentropy: 2.1240e-05 - val_loss: 0.8820 - val_accuracy: 0.8723 - val_binary_crossentropy: 0.8820\n",
            "Epoch 18/20\n",
            "49/49 - 1s - loss: 1.8915e-05 - accuracy: 1.0000 - binary_crossentropy: 1.8915e-05 - val_loss: 0.8908 - val_accuracy: 0.8722 - val_binary_crossentropy: 0.8908\n",
            "Epoch 19/20\n",
            "49/49 - 1s - loss: 1.6930e-05 - accuracy: 1.0000 - binary_crossentropy: 1.6930e-05 - val_loss: 0.8988 - val_accuracy: 0.8724 - val_binary_crossentropy: 0.8988\n",
            "Epoch 20/20\n",
            "49/49 - 1s - loss: 1.5255e-05 - accuracy: 1.0000 - binary_crossentropy: 1.5255e-05 - val_loss: 0.9067 - val_accuracy: 0.8722 - val_binary_crossentropy: 0.9067\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j2qppnpVrUVw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 932
        },
        "outputId": "b3642af6-5fbc-4024-b556-f6151f2b7594"
      },
      "source": [
        "def plot_history(histories,key='binary_crossentropy'):\n",
        "  plt.figure(figsize=(16,16))\n",
        "\n",
        "  for name, history in histories:\n",
        "    val = plt.plot(history.epoch, history.history['val_'+key],\n",
        "                   '--',label=name.title()+ 'Val')\n",
        "    plt.plot(history.epoch,history.history[key],color=val[0].get_color(),label=name.title()+'Train')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Train')\n",
        "    plt.legend()\n",
        "    plt.xlim([0,max(history.epoch)])\n",
        "plot_history([('baseline',baseline_history),('smaller',smaller_history),('bigger',bigger_history)])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iUezAmQqrHcY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 16,
      "outputs": []
    }
  ]
}