3 - Deep Neural Networks.ipynb

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      "cell_type": "markdown",
      "source": [
        "# 3 - Deep Neural Networks"
      ],
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        "id": "Hy5B8pkrDTRr"
      }
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      "cell_type": "markdown",
      "source": [
        "## Classification using Deep Neural Networks"
      ],
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        "id": "OTi3EhvxDgTT"
      }
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    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
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      "execution_count": 1,
      "outputs": []
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      "source": [
        "### Dataset Used"
      ],
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      "cell_type": "markdown",
      "source": [
        "Titanic Dataset : https://www.tensorflow.org/datasets/catalog/titanic"
      ],
      "metadata": {
        "id": "V9BD_qXnEBPu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading Dataset"
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      "cell_type": "code",
      "source": [
        "titanic_file_path = tf.keras.utils.get_file(\"train.csv\", \"https://storage.googleapis.com/tf-datasets/titanic/train.csv\")"
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      "metadata": {
        "id": "PrVtyXnAFViS"
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      "execution_count": 2,
      "outputs": []
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        "df = pd.read_csv(titanic_file_path)"
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      "execution_count": 3,
      "outputs": []
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              "   survived     sex   age  n_siblings_spouses  parch     fare  class     deck  \\\n",
              "0         0    male  22.0                   1      0   7.2500  Third  unknown   \n",
              "1         1  female  38.0                   1      0  71.2833  First        C   \n",
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              "3         1  female  35.0                   1      0  53.1000  First        C   \n",
              "4         0    male  28.0                   0      0   8.4583  Third  unknown   \n",
              "\n",
              "   embark_town alone  \n",
              "0  Southampton     n  \n",
              "1    Cherbourg     n  \n",
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              "     target     sex   age  n_siblings_spouses  parch      fare   class  \\\n",
              "445       0    male  57.0                   0      0   12.3500  Second   \n",
              "230       1  female  31.0                   0      2  164.8667   First   \n",
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              "361       0  female  37.0                   0      0    9.5875   Third   \n",
              "..      ...     ...   ...                 ...    ...       ...     ...   \n",
              "572       0    male  35.0                   0      0   10.5000  Second   \n",
              "54        0    male  26.0                   2      0    8.6625   Third   \n",
              "19        0    male  28.0                   0      0    7.2250   Third   \n",
              "609       1  female  27.0                   1      0   13.8583  Second   \n",
              "178       0    male  22.0                   0      0    7.1250   Third   \n",
              "\n",
              "        deck  embark_town alone  \n",
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              "289  unknown  Southampton     y  \n",
              "622  unknown  Southampton     y  \n",
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              "..       ...          ...   ...  \n",
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              "54   unknown  Southampton     n  \n",
              "19   unknown    Cherbourg     y  \n",
              "609  unknown    Cherbourg     n  \n",
              "178  unknown  Southampton     y  \n",
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              "[501 rows x 10 columns]"
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8d4146c6-b577-4583-bc1d-156eb4342b6f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8d4146c6-b577-4583-bc1d-156eb4342b6f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def df_to_dataset(dataframe, shuffle=True, batch_size=32):\n",
        "  df = dataframe.copy()\n",
        "  labels = df.pop('target')\n",
        "  df = {key: value.values[:,tf.newaxis] for key, value in dataframe.items()}\n",
        "  ds = tf.data.Dataset.from_tensor_slices((dict(df), labels))\n",
        "  if shuffle:\n",
        "    ds = ds.shuffle(buffer_size=len(dataframe))\n",
        "  ds = ds.batch(batch_size)\n",
        "  ds = ds.prefetch(batch_size)\n",
        "  return ds"
      ],
      "metadata": {
        "id": "tXBvXTObmHul"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 10\n",
        "train_ds = df_to_dataset(train, batch_size=batch_size)\n",
        "val_ds = df_to_dataset(val, batch_size=batch_size)\n",
        "test_ds = df_to_dataset(test, batch_size=batch_size)"
      ],
      "metadata": {
        "id": "fKqfXjvDsP1d"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Preprocessing the dataset"
      ],
      "metadata": {
        "id": "UpWegJf6GiMx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "References:\n",
        "1. Classifying Structured data using keras preprocessing layers - https://www.tensorflow.org/tutorials/structured_data/preprocessing_layers\n",
        "2. keras preprocessing layers - https://www.tensorflow.org/guide/keras/preprocessing_layers\n",
        "\n",
        "The titanic dataset is preprocessed using the following keras preprocessing layers:\n",
        "- `tf.keras.layers.Normalization`\n",
        "- `tf.keras.layers.CategoryEncoding`\n",
        "- `tf.keras.layers.StringLookup`\n",
        "- `tf.keras.layers.IntegerLookup`\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "sBOtOwmlGrha"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_normalization_layer(name, dataset):\n",
        "  # Create a Normalization layer for the feature.\n",
        "  normalizer = tf.keras.layers.Normalization(axis=None)\n",
        "\n",
        "  # Prepare a Dataset that only yields the feature.\n",
        "  feature_ds = dataset.map(lambda x, y: x[name])\n",
        "\n",
        "  # Learn the statistics of the data.\n",
        "  normalizer.adapt(feature_ds)\n",
        "\n",
        "  return normalizer\n"
      ],
      "metadata": {
        "id": "VKapMftwG7Ad"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_category_encoding_layer(name, dataset, dtype, max_tokens=None):\n",
        "  # Create a layer that turns strings into integer indices.\n",
        "  if dtype == 'string':\n",
        "    index = tf.keras.layers.StringLookup(max_tokens=max_tokens)\n",
        "  # Otherwise, create a layer that turns integer values into integer indices.\n",
        "  else:\n",
        "    index = tf.keras.layers.IntegerLookup(max_tokens=max_tokens)\n",
        "\n",
        "  # Prepare a `tf.data.Dataset` that only yields the feature.\n",
        "  feature_ds = dataset.map(lambda x, y: x[name])\n",
        "\n",
        "  # Learn the set of possible values and assign them a fixed integer index.\n",
        "  index.adapt(feature_ds)\n",
        "\n",
        "  # Encode the integer indices.\n",
        "  encoder = tf.keras.layers.CategoryEncoding(num_tokens=index.vocabulary_size())\n",
        "\n",
        "  # Apply multi-hot encoding to the indices. The lambda function captures the\n",
        "  # layer, so you can use them, or include them in the Keras Functional model later.\n",
        "  return lambda feature: encoder(index(feature))\n"
      ],
      "metadata": {
        "id": "9F_fL-pKlvG4"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numerical_cols = [\"age\",\"fare\"]\n",
        "numerical_categorical_cols = [\"n_siblings_spouses\",\"parch\"]\n",
        "categorical_cols = [\"sex\",\"class\",\"deck\",\"embark_town\",\"alone\"]\n",
        "\n",
        "all_inputs = []\n",
        "encoded_features = []\n",
        "\n",
        "# Numerical features.\n",
        "for header in numerical_cols:\n",
        "  numeric_col = tf.keras.Input(shape=(1,), name=header)\n",
        "  normalization_layer = get_normalization_layer(header, train_ds) # Normalization\n",
        "  encoded_numeric_col = normalization_layer(numeric_col)\n",
        "  all_inputs.append(numeric_col)\n",
        "  encoded_features.append(encoded_numeric_col)\n",
        "\n",
        "# Numerical Categorial features\n",
        "for header in numerical_categorical_cols:\n",
        "  categorical_col = tf.keras.Input(shape=(1,), name=header, dtype='int64')\n",
        "  encoding_layer = get_category_encoding_layer(name=header,dataset=train_ds,dtype='int64') # encoding\n",
        "  encoded_categorical_col = encoding_layer(categorical_col)\n",
        "  all_inputs.append(categorical_col)\n",
        "  encoded_features.append(encoded_categorical_col)\n",
        "\n",
        "# Other categorical Features\n",
        "for header in categorical_cols:\n",
        "  categorical_col = tf.keras.Input(shape=(1,), name=header, dtype='string')\n",
        "  encoding_layer = get_category_encoding_layer(name=header,dataset=train_ds,dtype='string',max_tokens=5) # encoding\n",
        "  encoded_categorical_col = encoding_layer(categorical_col)\n",
        "  all_inputs.append(categorical_col)\n",
        "  encoded_features.append(encoded_categorical_col)\n"
      ],
      "metadata": {
        "id": "rtqf0Y3FoRri"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating the model"
      ],
      "metadata": {
        "id": "XIDkFIZgwb9J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = tf.keras.layers.concatenate(encoded_features)\n",
        "x = tf.keras.layers.Dense(32, activation=\"relu\")(x)\n",
        "x = tf.keras.layers.Dense(8, activation=\"relu\")(x)\n",
        "x = tf.keras.layers.Dense(4, activation=\"relu\")(x)\n",
        "x = tf.keras.layers.Dense(2, activation=\"relu\")(x)\n",
        "outputs = tf.keras.layers.Dense(1, activation=\"sigmoid\")(x)\n",
        "model = tf.keras.Model(all_inputs,outputs)\n",
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss=tf.keras.losses.BinaryCrossentropy(from_logits=False),\n",
        "    metrics=[\"accuracy\"]\n",
        ")"
      ],
      "metadata": {
        "id": "lp7cBZAnvaBp"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "DLR-XRSTxBaz",
        "outputId": "9c9620d4-2ba2-4289-f04c-ceecfa612794"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
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          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training the model"
      ],
      "metadata": {
        "id": "Y6tFBRkl4dKj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(train_ds,validation_data=val_ds,epochs=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NC7gkfVW4fuW",
        "outputId": "b45c3de9-bd36-4a86-c3f8-0ed1894b86b0"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:559: UserWarning: Input dict contained keys ['target'] which did not match any model input. They will be ignored by the model.\n",
            "  inputs = self._flatten_to_reference_inputs(inputs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "51/51 [==============================] - 2s 11ms/step - loss: 0.7937 - accuracy: 0.4511 - val_loss: 0.6897 - val_accuracy: 0.5397\n",
            "Epoch 2/50\n",
            "51/51 [==============================] - 0s 4ms/step - loss: 0.6718 - accuracy: 0.6447 - val_loss: 0.6590 - val_accuracy: 0.6508\n",
            "Epoch 3/50\n",
            "51/51 [==============================] - 0s 3ms/step - loss: 0.6352 - accuracy: 0.7285 - val_loss: 0.6238 - val_accuracy: 0.6825\n",
            "Epoch 4/50\n",
            "51/51 [==============================] - 0s 4ms/step - loss: 0.5985 - accuracy: 0.7685 - val_loss: 0.5821 - val_accuracy: 0.7619\n",
            "Epoch 5/50\n",
            "51/51 [==============================] - 0s 4ms/step - loss: 0.5624 - accuracy: 0.8004 - val_loss: 0.5475 - val_accuracy: 0.8095\n",
            "Epoch 6/50\n",
            "51/51 [==============================] - 0s 8ms/step - loss: 0.5345 - accuracy: 0.8204 - val_loss: 0.5201 - val_accuracy: 0.8254\n",
            "Epoch 7/50\n",
            "51/51 [==============================] - 0s 8ms/step - loss: 0.5158 - accuracy: 0.8224 - val_loss: 0.4981 - val_accuracy: 0.8413\n",
            "Epoch 8/50\n",
            "51/51 [==============================] - 0s 9ms/step - loss: 0.4899 - accuracy: 0.8423 - val_loss: 0.4811 - val_accuracy: 0.8254\n",
            "Epoch 9/50\n",
            "51/51 [==============================] - 0s 8ms/step - loss: 0.4784 - accuracy: 0.8323 - val_loss: 0.4657 - val_accuracy: 0.8413\n",
            "Epoch 10/50\n",
            "51/51 [==============================] - 0s 8ms/step - loss: 0.4586 - accuracy: 0.8523 - val_loss: 0.4502 - val_accuracy: 0.8413\n",
            "Epoch 11/50\n",
            "51/51 [==============================] - 0s 8ms/step - loss: 0.4430 - accuracy: 0.8503 - val_loss: 0.4377 - val_accuracy: 0.8413\n",
            "Epoch 12/50\n",
            "51/51 [==============================] - 0s 6ms/step - loss: 0.4284 - accuracy: 0.8463 - val_loss: 0.4318 - val_accuracy: 0.8413\n",
            "Epoch 13/50\n",
            "51/51 [==============================] - 0s 8ms/step - loss: 0.4196 - accuracy: 0.8543 - val_loss: 0.4204 - val_accuracy: 0.8571\n",
            "Epoch 14/50\n",
            "51/51 [==============================] - 0s 9ms/step - loss: 0.4090 - accuracy: 0.8503 - val_loss: 0.4147 - val_accuracy: 0.8413\n",
            "Epoch 15/50\n",
            "51/51 [==============================] - 0s 8ms/step - loss: 0.3979 - accuracy: 0.8543 - val_loss: 0.4112 - val_accuracy: 0.8413\n",
            "Epoch 16/50\n",
            "51/51 [==============================] - 0s 6ms/step - loss: 0.3904 - accuracy: 0.8563 - val_loss: 0.3959 - val_accuracy: 0.8571\n",
            "Epoch 17/50\n",
            "51/51 [==============================] - 1s 10ms/step - loss: 0.3849 - accuracy: 0.8583 - val_loss: 0.3916 - val_accuracy: 0.8571\n",
            "Epoch 18/50\n",
            "51/51 [==============================] - 0s 8ms/step - loss: 0.3745 - accuracy: 0.8623 - val_loss: 0.3959 - val_accuracy: 0.8571\n",
            "Epoch 19/50\n",
            "51/51 [==============================] - 0s 10ms/step - loss: 0.3745 - accuracy: 0.8643 - val_loss: 0.4005 - val_accuracy: 0.8571\n",
            "Epoch 20/50\n",
            "51/51 [==============================] - 0s 8ms/step - loss: 0.3643 - accuracy: 0.8703 - val_loss: 0.3962 - val_accuracy: 0.8571\n",
            "Epoch 21/50\n",
            "51/51 [==============================] - 0s 9ms/step - loss: 0.3595 - accuracy: 0.8723 - val_loss: 0.3932 - val_accuracy: 0.8413\n",
            "Epoch 22/50\n",
            "51/51 [==============================] - 0s 6ms/step - loss: 0.3551 - accuracy: 0.8723 - val_loss: 0.3898 - val_accuracy: 0.8571\n",
            "Epoch 23/50\n",
            "51/51 [==============================] - 0s 7ms/step - loss: 0.3542 - accuracy: 0.8723 - val_loss: 0.3954 - val_accuracy: 0.8571\n",
            "Epoch 24/50\n",
            "51/51 [==============================] - 1s 15ms/step - loss: 0.3493 - accuracy: 0.8683 - val_loss: 0.3882 - val_accuracy: 0.8571\n",
            "Epoch 25/50\n",
            "51/51 [==============================] - 1s 10ms/step - loss: 0.3483 - accuracy: 0.8723 - val_loss: 0.3905 - val_accuracy: 0.8571\n",
            "Epoch 26/50\n",
            "51/51 [==============================] - 1s 10ms/step - loss: 0.3429 - accuracy: 0.8723 - val_loss: 0.3927 - val_accuracy: 0.8413\n",
            "Epoch 27/50\n",
            "51/51 [==============================] - 1s 11ms/step - loss: 0.3407 - accuracy: 0.8683 - val_loss: 0.4065 - val_accuracy: 0.8413\n",
            "Epoch 28/50\n",
            "51/51 [==============================] - 1s 11ms/step - loss: 0.3370 - accuracy: 0.8782 - val_loss: 0.3950 - val_accuracy: 0.8571\n",
            "Epoch 29/50\n",
            "51/51 [==============================] - 1s 11ms/step - loss: 0.3386 - accuracy: 0.8743 - val_loss: 0.3900 - val_accuracy: 0.8413\n",
            "Epoch 30/50\n",
            "51/51 [==============================] - 0s 8ms/step - loss: 0.3332 - accuracy: 0.8762 - val_loss: 0.3932 - val_accuracy: 0.8413\n",
            "Epoch 31/50\n",
            "51/51 [==============================] - 0s 8ms/step - loss: 0.3315 - accuracy: 0.8842 - val_loss: 0.3875 - val_accuracy: 0.8413\n",
            "Epoch 32/50\n",
            "51/51 [==============================] - 1s 13ms/step - loss: 0.3263 - accuracy: 0.8782 - val_loss: 0.3924 - val_accuracy: 0.8254\n",
            "Epoch 33/50\n",
            "51/51 [==============================] - 0s 8ms/step - loss: 0.3317 - accuracy: 0.8762 - val_loss: 0.4036 - val_accuracy: 0.8413\n",
            "Epoch 34/50\n",
            "51/51 [==============================] - 1s 9ms/step - loss: 0.3235 - accuracy: 0.8762 - val_loss: 0.3995 - val_accuracy: 0.8413\n",
            "Epoch 35/50\n",
            "51/51 [==============================] - 1s 11ms/step - loss: 0.3199 - accuracy: 0.8842 - val_loss: 0.4150 - val_accuracy: 0.8413\n",
            "Epoch 36/50\n",
            "51/51 [==============================] - 0s 9ms/step - loss: 0.3348 - accuracy: 0.8703 - val_loss: 0.4000 - val_accuracy: 0.8413\n",
            "Epoch 37/50\n",
            "51/51 [==============================] - 1s 10ms/step - loss: 0.3218 - accuracy: 0.8782 - val_loss: 0.4039 - val_accuracy: 0.8413\n",
            "Epoch 38/50\n",
            "51/51 [==============================] - 1s 11ms/step - loss: 0.3199 - accuracy: 0.8802 - val_loss: 0.3961 - val_accuracy: 0.8413\n",
            "Epoch 39/50\n",
            "51/51 [==============================] - 1s 10ms/step - loss: 0.3189 - accuracy: 0.8802 - val_loss: 0.3981 - val_accuracy: 0.8413\n",
            "Epoch 40/50\n",
            "51/51 [==============================] - 0s 9ms/step - loss: 0.3204 - accuracy: 0.8822 - val_loss: 0.4024 - val_accuracy: 0.8254\n",
            "Epoch 41/50\n",
            "51/51 [==============================] - 0s 8ms/step - loss: 0.3175 - accuracy: 0.8842 - val_loss: 0.4059 - val_accuracy: 0.8413\n",
            "Epoch 42/50\n",
            "51/51 [==============================] - 0s 8ms/step - loss: 0.3162 - accuracy: 0.8842 - val_loss: 0.4178 - val_accuracy: 0.8413\n",
            "Epoch 43/50\n",
            "51/51 [==============================] - 0s 7ms/step - loss: 0.3199 - accuracy: 0.8802 - val_loss: 0.4000 - val_accuracy: 0.8413\n",
            "Epoch 44/50\n",
            "51/51 [==============================] - 0s 4ms/step - loss: 0.3137 - accuracy: 0.8802 - val_loss: 0.4009 - val_accuracy: 0.8413\n",
            "Epoch 45/50\n",
            "51/51 [==============================] - 0s 4ms/step - loss: 0.3172 - accuracy: 0.8723 - val_loss: 0.4056 - val_accuracy: 0.8413\n",
            "Epoch 46/50\n",
            "51/51 [==============================] - 0s 4ms/step - loss: 0.3110 - accuracy: 0.8822 - val_loss: 0.4015 - val_accuracy: 0.8413\n",
            "Epoch 47/50\n",
            "51/51 [==============================] - 0s 4ms/step - loss: 0.3108 - accuracy: 0.8862 - val_loss: 0.4096 - val_accuracy: 0.8413\n",
            "Epoch 48/50\n",
            "51/51 [==============================] - 0s 4ms/step - loss: 0.3121 - accuracy: 0.8842 - val_loss: 0.4233 - val_accuracy: 0.8413\n",
            "Epoch 49/50\n",
            "51/51 [==============================] - 0s 4ms/step - loss: 0.3333 - accuracy: 0.8782 - val_loss: 0.4364 - val_accuracy: 0.8254\n",
            "Epoch 50/50\n",
            "51/51 [==============================] - 0s 4ms/step - loss: 0.3159 - accuracy: 0.8802 - val_loss: 0.4143 - val_accuracy: 0.8413\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Ploting learing curves"
      ],
      "metadata": {
        "id": "p55M0Sj540pW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = history.history"
      ],
      "metadata": {
        "id": "y47Yge886Jbm"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "plt.subplot(121)\n",
        "plt.title(\"Accuracy\")\n",
        "plt.plot(history[\"accuracy\"],label=\"train acc\")\n",
        "plt.plot(history[\"val_accuracy\"],label=\"val acc\")\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.title(\"Loss\")\n",
        "plt.plot(history[\"loss\"],label=\"train loss\")\n",
        "plt.plot(history[\"val_loss\"],label=\"val loss\")\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "4izsZIX66FDt",
        "outputId": "b11f4027-ee86-42a1-f701-251904677783"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Testing the model on test set"
      ],
      "metadata": {
        "id": "o5K6Yrel7FG4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss ,accuracy = model.evaluate(test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZa0DugZ7IUu",
        "outputId": "167de9e0-f6a5-4b8b-8cdc-f759d9b2f417"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 3ms/step - loss: 0.6833 - accuracy: 0.7143\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"test loss :\",loss)\n",
        "print(\"test accuracy :\",accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2uQ0kUZV7KIJ",
        "outputId": "7358ba52-d9da-46a1-a0cf-48006bb3e437"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test loss : 0.68325275182724\n",
            "test accuracy : 0.7142857313156128\n"
          ]
        }
      ]
    }
  ]
}