Copy_of_NLP_Course_Week_3_Exercise_Answer.ipynb

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      "name": "Copy of NLP Course - Week 3 Exercise Answer.ipynb",
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      "include_colab_link": true
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    "kernelspec": {
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RhuthuHegde/Tensorflow-coursera/blob/master/Copy_of_NLP_Course_Week_3_Exercise_Answer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zX4Kg8DUTKWO",
        "colab": {}
      },
      "source": [
        "#@title 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",
        "# https://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."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YPr8vzGMV4OP",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/TensorFlow%20In%20Practice/Course%203%20-%20NLP/NLP%20Course%20-%20Week%203%20Exercise%20Answer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "hmA6EzkQJ5jt",
        "colab": {}
      },
      "source": [
        "import json\n",
        "import tensorflow as tf\n",
        "import csv\n",
        "import random\n",
        "import numpy as np\n",
        "\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras import regularizers\n",
        "\n",
        "\n",
        "embedding_dim = 100\n",
        "max_length = 16\n",
        "trunc_type='post'\n",
        "padding_type='post'\n",
        "oov_tok = \"<OOV>\"\n",
        "training_size=160000\n",
        "test_portion=.1\n",
        "\n",
        "corpus = []\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bM0l_dORKqE0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "e26d16ec-73e8-4475-baa3-fd31f70412a2"
      },
      "source": [
        "# Note that I cleaned the Stanford dataset to remove LATIN1 encoding to make it easier for Python CSV reader\n",
        "# You can do that yourself with:\n",
        "# iconv -f LATIN1 -t UTF8 training.1600000.processed.noemoticon.csv -o training_cleaned.csv\n",
        "# I then hosted it on my site to make it easier to use in this notebook\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/training_cleaned.csv \\\n",
        "    -O /tmp/training_cleaned.csv\n",
        "\n",
        "num_sentences = 0\n",
        "\n",
        "with open(\"/tmp/training_cleaned.csv\") as csvfile:\n",
        "    reader = csv.reader(csvfile, delimiter=',')\n",
        "    for row in reader:\n",
        "        list_item=[]\n",
        "        list_item.append(row[5])\n",
        "        this_label=row[0]\n",
        "        if this_label=='0':\n",
        "            list_item.append(0)\n",
        "        else:\n",
        "            list_item.append(1)\n",
        "        num_sentences = num_sentences + 1\n",
        "        corpus.append(list_item)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-08-04 07:20:50--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/training_cleaned.csv\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 66.102.1.128, 64.233.166.128, 74.125.133.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|66.102.1.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 238942690 (228M) [application/octet-stream]\n",
            "Saving to: ‘/tmp/training_cleaned.csv’\n",
            "\n",
            "\r          /tmp/trai   0%[                    ]       0  --.-KB/s               \r         /tmp/train  10%[=>                  ]  23.08M   115MB/s               \r        /tmp/traini  20%[===>                ]  46.77M   117MB/s               \r       /tmp/trainin  33%[=====>              ]  76.36M   127MB/s               \r      /tmp/training  54%[=========>          ] 124.32M   155MB/s               \r     /tmp/training_  67%[============>       ] 154.50M   154MB/s               \r    /tmp/training_c  88%[================>   ] 201.81M   168MB/s               \r/tmp/training_clean 100%[===================>] 227.87M   173MB/s    in 1.3s    \n",
            "\n",
            "2020-08-04 07:20:51 (173 MB/s) - ‘/tmp/training_cleaned.csv’ saved [238942690/238942690]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3kxblBUjEUX-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "cdb35d09-6a16-42f2-8bf5-2257fb305734"
      },
      "source": [
        "print(num_sentences)\n",
        "print(len(corpus))\n",
        "print(corpus[1])\n",
        "\n",
        "# Expected Output:\n",
        "# 1600000\n",
        "# 1600000\n",
        "# [\"is upset that he can't update his Facebook by texting it... and might cry as a result  School today also. Blah!\", 0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1600000\n",
            "1600000\n",
            "[\"is upset that he can't update his Facebook by texting it... and might cry as a result  School today also. Blah!\", 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ohOGz24lsNAD",
        "colab": {}
      },
      "source": [
        "sentences=[]\n",
        "labels=[]\n",
        "random.shuffle(corpus)\n",
        "for x in range(training_size):\n",
        "    sentences.append(corpus[x][0])\n",
        "    labels.append(corpus[x][1])\n",
        "\n",
        "\n",
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(sentences)\n",
        "\n",
        "word_index = tokenizer.word_index\n",
        "vocab_size=len(word_index)\n",
        "\n",
        "sequences = tokenizer.texts_to_sequences(sentences)\n",
        "padded = pad_sequences(sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "\n",
        "split = int(test_portion * training_size)\n",
        "\n",
        "test_sequences = padded[0:split]\n",
        "training_sequences = padded[split:training_size]\n",
        "test_labels = labels[0:split]\n",
        "training_labels = labels[split:training_size]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "gIrtRem1En3N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "95c1df85-654f-474b-d81d-ef2c2ea51780"
      },
      "source": [
        "print(vocab_size)\n",
        "print(word_index['i'])\n",
        "# Expected Output\n",
        "# 138858\n",
        "# 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "138567\n",
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "C1zdgJkusRh0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "df83ad6a-b47e-42f2-a3b1-d4f7678ab047"
      },
      "source": [
        "# Note this is the 100 dimension version of GloVe from Stanford\n",
        "# I unzipped and hosted it on my site to make this notebook easier\n",
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/glove.6B.100d.txt \\\n",
        "    -O /tmp/glove.6B.100d.txt\n",
        "embeddings_index = {};\n",
        "with open('/tmp/glove.6B.100d.txt') as f:\n",
        "    for line in f:\n",
        "        values = line.split();\n",
        "        word = values[0];\n",
        "        coefs = np.asarray(values[1:], dtype='float32');\n",
        "        embeddings_index[word] = coefs;\n",
        "\n",
        "embeddings_matrix = np.zeros((vocab_size+1, embedding_dim));\n",
        "for word, i in word_index.items():\n",
        "    embedding_vector = embeddings_index.get(word);\n",
        "    if embedding_vector is not None:\n",
        "        embeddings_matrix[i] = embedding_vector;"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-08-04 07:21:36--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/glove.6B.100d.txt\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 173.194.76.128, 74.125.71.128, 74.125.133.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|173.194.76.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 347116733 (331M) [text/plain]\n",
            "Saving to: ‘/tmp/glove.6B.100d.txt’\n",
            "\n",
            "/tmp/glove.6B.100d. 100%[===================>] 331.04M  43.8MB/s    in 8.6s    \n",
            "\n",
            "2020-08-04 07:21:46 (38.5 MB/s) - ‘/tmp/glove.6B.100d.txt’ saved [347116733/347116733]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "71NLk_lpFLNt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ab223301-99ff-4825-d143-f7cec189d38e"
      },
      "source": [
        "print(len(embeddings_matrix))\n",
        "# Expected Output\n",
        "# 138859"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "138568\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "iKKvbuEBOGFz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7b600d6b-d385-4946-e43c-102bd503a03d"
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size+1, embedding_dim, input_length=max_length, weights=[embeddings_matrix], trainable=False),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Conv1D(64, 5, activation='relu'),\n",
        "    tf.keras.layers.MaxPooling1D(pool_size=4),\n",
        "    tf.keras.layers.LSTM(64),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.summary()\n",
        "\n",
        "num_epochs = 50\n",
        "\n",
        "training_padded = np.array(training_sequences)\n",
        "training_labels = np.array(training_labels)\n",
        "testing_padded = np.array(test_sequences)\n",
        "testing_labels = np.array(test_labels)\n",
        "\n",
        "history = model.fit(training_padded, training_labels, epochs=num_epochs, validation_data=(testing_padded, testing_labels), verbose=2)\n",
        "\n",
        "print(\"Training Complete\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 16, 100)           13856800  \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 16, 100)           0         \n",
            "_________________________________________________________________\n",
            "conv1d (Conv1D)              (None, 12, 64)            32064     \n",
            "_________________________________________________________________\n",
            "max_pooling1d (MaxPooling1D) (None, 3, 64)             0         \n",
            "_________________________________________________________________\n",
            "lstm (LSTM)                  (None, 64)                33024     \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 13,921,953\n",
            "Trainable params: 65,153\n",
            "Non-trainable params: 13,856,800\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "4500/4500 - 19s - loss: 0.5683 - accuracy: 0.6978 - val_loss: 0.5330 - val_accuracy: 0.7259\n",
            "Epoch 2/50\n",
            "4500/4500 - 19s - loss: 0.5295 - accuracy: 0.7302 - val_loss: 0.5145 - val_accuracy: 0.7404\n",
            "Epoch 3/50\n",
            "4500/4500 - 20s - loss: 0.5111 - accuracy: 0.7440 - val_loss: 0.5106 - val_accuracy: 0.7469\n",
            "Epoch 4/50\n",
            "4500/4500 - 20s - loss: 0.5008 - accuracy: 0.7520 - val_loss: 0.5070 - val_accuracy: 0.7491\n",
            "Epoch 5/50\n",
            "4500/4500 - 19s - loss: 0.4923 - accuracy: 0.7576 - val_loss: 0.5071 - val_accuracy: 0.7492\n",
            "Epoch 6/50\n",
            "4500/4500 - 20s - loss: 0.4855 - accuracy: 0.7608 - val_loss: 0.5073 - val_accuracy: 0.7505\n",
            "Epoch 7/50\n",
            "4500/4500 - 19s - loss: 0.4796 - accuracy: 0.7666 - val_loss: 0.5080 - val_accuracy: 0.7508\n",
            "Epoch 8/50\n",
            "4500/4500 - 19s - loss: 0.4734 - accuracy: 0.7693 - val_loss: 0.5072 - val_accuracy: 0.7478\n",
            "Epoch 9/50\n",
            "4500/4500 - 19s - loss: 0.4700 - accuracy: 0.7710 - val_loss: 0.5090 - val_accuracy: 0.7492\n",
            "Epoch 10/50\n",
            "4500/4500 - 19s - loss: 0.4653 - accuracy: 0.7738 - val_loss: 0.5077 - val_accuracy: 0.7466\n",
            "Epoch 11/50\n",
            "4500/4500 - 19s - loss: 0.4616 - accuracy: 0.7769 - val_loss: 0.5092 - val_accuracy: 0.7477\n",
            "Epoch 12/50\n",
            "4500/4500 - 19s - loss: 0.4592 - accuracy: 0.7798 - val_loss: 0.5115 - val_accuracy: 0.7498\n",
            "Epoch 13/50\n",
            "4500/4500 - 19s - loss: 0.4562 - accuracy: 0.7809 - val_loss: 0.5123 - val_accuracy: 0.7492\n",
            "Epoch 14/50\n",
            "4500/4500 - 19s - loss: 0.4543 - accuracy: 0.7810 - val_loss: 0.5156 - val_accuracy: 0.7492\n",
            "Epoch 15/50\n",
            "4500/4500 - 19s - loss: 0.4523 - accuracy: 0.7820 - val_loss: 0.5180 - val_accuracy: 0.7490\n",
            "Epoch 16/50\n",
            "4500/4500 - 19s - loss: 0.4495 - accuracy: 0.7840 - val_loss: 0.5292 - val_accuracy: 0.7396\n",
            "Epoch 17/50\n",
            "4500/4500 - 19s - loss: 0.4488 - accuracy: 0.7844 - val_loss: 0.5188 - val_accuracy: 0.7475\n",
            "Epoch 18/50\n",
            "4500/4500 - 19s - loss: 0.4477 - accuracy: 0.7855 - val_loss: 0.5160 - val_accuracy: 0.7484\n",
            "Epoch 19/50\n",
            "4500/4500 - 20s - loss: 0.4436 - accuracy: 0.7879 - val_loss: 0.5136 - val_accuracy: 0.7492\n",
            "Epoch 20/50\n",
            "4500/4500 - 20s - loss: 0.4437 - accuracy: 0.7878 - val_loss: 0.5157 - val_accuracy: 0.7509\n",
            "Epoch 21/50\n",
            "4500/4500 - 20s - loss: 0.4425 - accuracy: 0.7887 - val_loss: 0.5158 - val_accuracy: 0.7471\n",
            "Epoch 22/50\n",
            "4500/4500 - 19s - loss: 0.4413 - accuracy: 0.7897 - val_loss: 0.5165 - val_accuracy: 0.7443\n",
            "Epoch 23/50\n",
            "4500/4500 - 19s - loss: 0.4399 - accuracy: 0.7891 - val_loss: 0.5201 - val_accuracy: 0.7464\n",
            "Epoch 24/50\n",
            "4500/4500 - 19s - loss: 0.4384 - accuracy: 0.7909 - val_loss: 0.5314 - val_accuracy: 0.7427\n",
            "Epoch 25/50\n",
            "4500/4500 - 19s - loss: 0.4389 - accuracy: 0.7907 - val_loss: 0.5213 - val_accuracy: 0.7489\n",
            "Epoch 26/50\n",
            "4500/4500 - 19s - loss: 0.4381 - accuracy: 0.7912 - val_loss: 0.5236 - val_accuracy: 0.7474\n",
            "Epoch 27/50\n",
            "4500/4500 - 19s - loss: 0.4369 - accuracy: 0.7919 - val_loss: 0.5230 - val_accuracy: 0.7494\n",
            "Epoch 28/50\n",
            "4500/4500 - 19s - loss: 0.4352 - accuracy: 0.7929 - val_loss: 0.5252 - val_accuracy: 0.7462\n",
            "Epoch 29/50\n",
            "4500/4500 - 19s - loss: 0.4356 - accuracy: 0.7937 - val_loss: 0.5168 - val_accuracy: 0.7477\n",
            "Epoch 30/50\n",
            "4500/4500 - 19s - loss: 0.4346 - accuracy: 0.7934 - val_loss: 0.5213 - val_accuracy: 0.7466\n",
            "Epoch 31/50\n",
            "4500/4500 - 19s - loss: 0.4355 - accuracy: 0.7928 - val_loss: 0.5248 - val_accuracy: 0.7479\n",
            "Epoch 32/50\n",
            "4500/4500 - 19s - loss: 0.4334 - accuracy: 0.7934 - val_loss: 0.5222 - val_accuracy: 0.7474\n",
            "Epoch 33/50\n",
            "4500/4500 - 19s - loss: 0.4315 - accuracy: 0.7953 - val_loss: 0.5282 - val_accuracy: 0.7421\n",
            "Epoch 34/50\n",
            "4500/4500 - 19s - loss: 0.4318 - accuracy: 0.7953 - val_loss: 0.5301 - val_accuracy: 0.7448\n",
            "Epoch 35/50\n",
            "4500/4500 - 20s - loss: 0.4323 - accuracy: 0.7943 - val_loss: 0.5258 - val_accuracy: 0.7447\n",
            "Epoch 36/50\n",
            "4500/4500 - 20s - loss: 0.4314 - accuracy: 0.7952 - val_loss: 0.5287 - val_accuracy: 0.7441\n",
            "Epoch 37/50\n",
            "4500/4500 - 20s - loss: 0.4315 - accuracy: 0.7951 - val_loss: 0.5228 - val_accuracy: 0.7445\n",
            "Epoch 38/50\n",
            "4500/4500 - 19s - loss: 0.4305 - accuracy: 0.7956 - val_loss: 0.5280 - val_accuracy: 0.7414\n",
            "Epoch 39/50\n",
            "4500/4500 - 19s - loss: 0.4299 - accuracy: 0.7945 - val_loss: 0.5316 - val_accuracy: 0.7461\n",
            "Epoch 40/50\n",
            "4500/4500 - 19s - loss: 0.4289 - accuracy: 0.7968 - val_loss: 0.5266 - val_accuracy: 0.7462\n",
            "Epoch 41/50\n",
            "4500/4500 - 19s - loss: 0.4299 - accuracy: 0.7967 - val_loss: 0.5262 - val_accuracy: 0.7440\n",
            "Epoch 42/50\n",
            "4500/4500 - 19s - loss: 0.4293 - accuracy: 0.7964 - val_loss: 0.5298 - val_accuracy: 0.7454\n",
            "Epoch 43/50\n",
            "4500/4500 - 19s - loss: 0.4280 - accuracy: 0.7978 - val_loss: 0.5290 - val_accuracy: 0.7441\n",
            "Epoch 44/50\n",
            "4500/4500 - 19s - loss: 0.4279 - accuracy: 0.7970 - val_loss: 0.5259 - val_accuracy: 0.7455\n",
            "Epoch 45/50\n",
            "4500/4500 - 19s - loss: 0.4291 - accuracy: 0.7968 - val_loss: 0.5274 - val_accuracy: 0.7464\n",
            "Epoch 46/50\n",
            "4500/4500 - 19s - loss: 0.4288 - accuracy: 0.7973 - val_loss: 0.5266 - val_accuracy: 0.7465\n",
            "Epoch 47/50\n",
            "4500/4500 - 19s - loss: 0.4290 - accuracy: 0.7965 - val_loss: 0.5312 - val_accuracy: 0.7413\n",
            "Epoch 48/50\n",
            "4500/4500 - 19s - loss: 0.4293 - accuracy: 0.7973 - val_loss: 0.5300 - val_accuracy: 0.7421\n",
            "Epoch 49/50\n",
            "4500/4500 - 19s - loss: 0.4263 - accuracy: 0.7991 - val_loss: 0.5340 - val_accuracy: 0.7454\n",
            "Epoch 50/50\n",
            "4500/4500 - 19s - loss: 0.4276 - accuracy: 0.7976 - val_loss: 0.5319 - val_accuracy: 0.7393\n",
            "Training Complete\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qxju4ItJKO8F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "dfec9f8c-ffce-4a2f-e121-68158a8ed40e"
      },
      "source": [
        "import matplotlib.image  as mpimg\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#-----------------------------------------------------------\n",
        "# Retrieve a list of list results on training and test data\n",
        "# sets for each training epoch\n",
        "#-----------------------------------------------------------\n",
        "acc=history.history['accuracy']\n",
        "val_acc=history.history['val_accuracy']\n",
        "loss=history.history['loss']\n",
        "val_loss=history.history['val_loss']\n",
        "\n",
        "epochs=range(len(acc)) # Get number of epochs\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation accuracy per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs, acc, 'r')\n",
        "plt.plot(epochs, val_acc, 'b')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend([\"Accuracy\", \"Validation Accuracy\"])\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation loss per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs, loss, 'r')\n",
        "plt.plot(epochs, val_loss, 'b')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend([\"Loss\", \"Validation Loss\"])\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "\n",
        "# Expected Output\n",
        "# A chart where the validation loss does not increase sharply!"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "liFIKoTwagYk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}