notebooks/Indian_dance_classifer.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# Indian Dance classifier"
      ],
      "metadata": {
        "id": "vQ_WyJlcHTYb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Set up"
      ],
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      "source": [
        "import pandas as pd\n",
        "import os, shutil, pathlib\n",
        "from sklearn.model_selection import  train_test_split\n",
        "from tensorflow.keras.utils import image_dataset_from_directory\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading and organising data"
      ],
      "metadata": {
        "id": "qoN_3WRtHdnz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "catalog = pd.read_csv(\"/content/drive/MyDrive/dance/train.csv\")\n",
        "catalog.head()"
      ],
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          "data": {
            "text/plain": [
              "     Image         target\n",
              "0   96.jpg       manipuri\n",
              "1  163.jpg  bharatanatyam\n",
              "2  450.jpg         odissi\n",
              "3  219.jpg      kathakali\n",
              "4  455.jpg         odissi"
            ],
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              "      <th></th>\n",
              "      <th>Image</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>96.jpg</td>\n",
              "      <td>manipuri</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>163.jpg</td>\n",
              "      <td>bharatanatyam</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>450.jpg</td>\n",
              "      <td>odissi</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>219.jpg</td>\n",
              "      <td>kathakali</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>455.jpg</td>\n",
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      "cell_type": "code",
      "source": [
        "catalog.target.value_counts()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iayayrrjCZed",
        "outputId": "fe69e4fd-605b-49c9-c729-21a0c426e6fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mohiniyattam     50\n",
              "odissi           49\n",
              "bharatanatyam    47\n",
              "kathakali        47\n",
              "kuchipudi        46\n",
              "sattriya         45\n",
              "kathak           44\n",
              "manipuri         36\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "catalog.shape\n"
      ],
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          "data": {
            "text/plain": [
              "(364, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating train, validation and test sets"
      ],
      "metadata": {
        "id": "s4HPy-4NHnXh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = catalog.Image\n",
        "y = catalog.target"
      ],
      "metadata": {
        "id": "DEkdBvCfR_Wl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1, stratify = y)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size = 0.2, stratify = y_train)"
      ],
      "metadata": {
        "id": "RiPjuzVoSIdl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape, y_val.shape, y_test.shape"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "XpGcB-XQ-Pjr",
        "outputId": "e4157bf3-3e6b-4cb8-e740-9a0895f4c9fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((261,), (66,), (37,))"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "original_dir = pathlib.Path(\"/content/drive/MyDrive/dance/train\")\n",
        "new_base_dir = pathlib.Path(\"/content/drive/MyDrive/dance/data\")\n",
        "\n",
        "def make_subset(subset_name, X, y):\n",
        "  for category in y.unique():\n",
        "    indices = y[y == category].index\n",
        "    dir = new_base_dir / subset_name / category\n",
        "    os.makedirs(dir)\n",
        "    for i, fname in enumerate(X[indices]):\n",
        "      new_fname = f\"{category}.{i}.jpg\"\n",
        "      shutil.copyfile(src = original_dir / fname,\n",
        "                      dst = dir / new_fname)\n",
        "      \n",
        "make_subset(\"train\", X_train, y_train)\n",
        "make_subset(\"validation\", X_val, y_val)\n",
        "make_subset(\"test\", X_test, y_test)"
      ],
      "metadata": {
        "id": "b95B-w9rF8zu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_base_dir = pathlib.Path(\"/content/drive/MyDrive/dance/data\")\n",
        "\n",
        "train_dataset = image_dataset_from_directory(\n",
        "    new_base_dir / \"train\",\n",
        "    image_size = (180, 180),\n",
        "    batch_size = 16\n",
        ")\n",
        "\n",
        "validation_dataset = image_dataset_from_directory(\n",
        "    new_base_dir / \"validation\",\n",
        "    image_size = (180, 180),\n",
        "    batch_size = 16\n",
        ")\n",
        "\n",
        "test_dataset = image_dataset_from_directory(\n",
        "    new_base_dir / \"test\",\n",
        "    image_size = (180, 180),\n",
        "    batch_size = 16\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8FwVO4cKaakI",
        "outputId": "81849d17-9152-4422-d62f-71869d5bf18e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 261 files belonging to 8 classes.\n",
            "Found 66 files belonging to 8 classes.\n",
            "Found 37 files belonging to 8 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Initialising the pretrained model(VGG16)"
      ],
      "metadata": {
        "id": "gIT0RtVOH1D_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#instatiating the VGG 16 convolutional base\n",
        "conv_base = keras.applications.vgg16.VGG16(weights = \"imagenet\",\n",
        "                                          include_top = False,\n",
        "                                          input_shape = (180,180,3))\n",
        "\n",
        "conv_base.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R1PLRwIWVwUM",
        "outputId": "6283af8d-0b2d-4451-9463-43f6355ae8fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"vgg16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_35 (InputLayer)       [(None, 180, 180, 3)]     0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 180, 180, 64)      1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 180, 180, 64)      36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 90, 90, 64)        0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 90, 90, 128)       73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 90, 90, 128)       147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 45, 45, 128)       0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 45, 45, 256)       295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 45, 45, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 45, 45, 256)       590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 22, 22, 256)       0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 22, 22, 512)       1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 22, 22, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 22, 22, 512)       2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 11, 11, 512)       0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 11, 11, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 11, 11, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 11, 11, 512)       2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 5, 5, 512)         0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 14,714,688\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets's make the last two layers of VGG16 trainable for fine tuning"
      ],
      "metadata": {
        "id": "UWAl9gxgH-kv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "conv_base.trainable = True\n",
        "for layer in conv_base.layers[:-2]:\n",
        "  layer.trainable = False"
      ],
      "metadata": {
        "id": "Z1gOzu-NZxIJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Defining the model with keras functional API"
      ],
      "metadata": {
        "id": "79lOhFCgIaj0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_augmentation = keras.Sequential(\n",
        "    [\n",
        "        layers.RandomFlip(\"horizontal\"),\n",
        "        layers.RandomRotation(0.1),\n",
        "        layers.RandomZoom(0.2)\n",
        "    ]\n",
        ")\n",
        "\n",
        "inputs = keras.Input(shape = (180, 180, 3))\n",
        "x = data_augmentation(inputs)\n",
        "x = keras.applications.vgg16.preprocess_input(x)\n",
        "x = conv_base(x)\n",
        "x = layers.Dense(256, activation=\"relu\")(x)\n",
        "x = layers.Dense(128, activation=\"relu\")(x)\n",
        "x = layers.Flatten()(x)\n",
        "outputs = layers.Dense(8, activation=\"softmax\")(x)\n",
        "\n",
        "model = keras.Model(inputs, outputs)"
      ],
      "metadata": {
        "id": "0djAjFl5bLK6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y5KjsDsKlvBk",
        "outputId": "40632eda-8b38-490b-a8b9-9e8dafd07aa2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_28\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_36 (InputLayer)       [(None, 180, 180, 3)]     0         \n",
            "                                                                 \n",
            " sequential_28 (Sequential)  (None, 180, 180, 3)       0         \n",
            "                                                                 \n",
            " tf.__operators__.getitem_28  (None, 180, 180, 3)      0         \n",
            "  (SlicingOpLambda)                                              \n",
            "                                                                 \n",
            " tf.nn.bias_add_28 (TFOpLamb  (None, 180, 180, 3)      0         \n",
            " da)                                                             \n",
            "                                                                 \n",
            " vgg16 (Functional)          (None, 5, 5, 512)         14714688  \n",
            "                                                                 \n",
            " dense_73 (Dense)            (None, 5, 5, 256)         131328    \n",
            "                                                                 \n",
            " dense_74 (Dense)            (None, 5, 5, 128)         32896     \n",
            "                                                                 \n",
            " flatten_24 (Flatten)        (None, 3200)              0         \n",
            "                                                                 \n",
            " dense_75 (Dense)            (None, 8)                 25608     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14,904,520\n",
            "Trainable params: 189,832\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training"
      ],
      "metadata": {
        "id": "LZa8SHnYIf6l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=keras.optimizers.RMSprop(learning_rate=1e-3),\n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\n",
        "        filepath=\"dance.keras\",\n",
        "        save_best_only=True,\n",
        "        monitor=\"val_loss\")\n",
        "]\n",
        "\n",
        "history = model.fit(\n",
        "    train_dataset,\n",
        "    epochs=50,\n",
        "    validation_data=validation_dataset,\n",
        "    callbacks=callbacks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NJxZ1xCAZ5pt",
        "outputId": "47c985fb-d2db-4a5c-f268-e0890d1d8e61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "17/17 [==============================] - 5s 119ms/step - loss: 9.8137 - accuracy: 0.2184 - val_loss: 4.3834 - val_accuracy: 0.3333\n",
            "Epoch 2/50\n",
            "17/17 [==============================] - 2s 107ms/step - loss: 2.2451 - accuracy: 0.4789 - val_loss: 2.7352 - val_accuracy: 0.4091\n",
            "Epoch 3/50\n",
            "17/17 [==============================] - 2s 95ms/step - loss: 1.3580 - accuracy: 0.6054 - val_loss: 2.9711 - val_accuracy: 0.4545\n",
            "Epoch 4/50\n",
            "17/17 [==============================] - 2s 93ms/step - loss: 1.1054 - accuracy: 0.6705 - val_loss: 3.3315 - val_accuracy: 0.4697\n",
            "Epoch 5/50\n",
            "17/17 [==============================] - 2s 95ms/step - loss: 0.7960 - accuracy: 0.7739 - val_loss: 2.9473 - val_accuracy: 0.4545\n",
            "Epoch 6/50\n",
            "17/17 [==============================] - 2s 96ms/step - loss: 0.6481 - accuracy: 0.8046 - val_loss: 3.9395 - val_accuracy: 0.4545\n",
            "Epoch 7/50\n",
            "17/17 [==============================] - 2s 108ms/step - loss: 0.5335 - accuracy: 0.8238 - val_loss: 2.4257 - val_accuracy: 0.5909\n",
            "Epoch 8/50\n",
            "17/17 [==============================] - 2s 95ms/step - loss: 0.3714 - accuracy: 0.8621 - val_loss: 2.7674 - val_accuracy: 0.5455\n",
            "Epoch 9/50\n",
            "17/17 [==============================] - 2s 96ms/step - loss: 0.4277 - accuracy: 0.8736 - val_loss: 3.1928 - val_accuracy: 0.6061\n",
            "Epoch 10/50\n",
            "17/17 [==============================] - 2s 94ms/step - loss: 0.2454 - accuracy: 0.9234 - val_loss: 3.0702 - val_accuracy: 0.6364\n",
            "Epoch 11/50\n",
            "17/17 [==============================] - 2s 97ms/step - loss: 0.3740 - accuracy: 0.8927 - val_loss: 3.9289 - val_accuracy: 0.5303\n",
            "Epoch 12/50\n",
            "17/17 [==============================] - 2s 95ms/step - loss: 0.1835 - accuracy: 0.9349 - val_loss: 3.7532 - val_accuracy: 0.6364\n",
            "Epoch 13/50\n",
            "17/17 [==============================] - 2s 93ms/step - loss: 0.2451 - accuracy: 0.9310 - val_loss: 4.0107 - val_accuracy: 0.5455\n",
            "Epoch 14/50\n",
            "17/17 [==============================] - 2s 95ms/step - loss: 0.1545 - accuracy: 0.9502 - val_loss: 3.7938 - val_accuracy: 0.6212\n",
            "Epoch 15/50\n",
            "17/17 [==============================] - 2s 95ms/step - loss: 0.1097 - accuracy: 0.9617 - val_loss: 4.9292 - val_accuracy: 0.5909\n",
            "Epoch 16/50\n",
            "17/17 [==============================] - 2s 96ms/step - loss: 0.2325 - accuracy: 0.9425 - val_loss: 3.7030 - val_accuracy: 0.6061\n",
            "Epoch 17/50\n",
            "17/17 [==============================] - 2s 95ms/step - loss: 0.1526 - accuracy: 0.9387 - val_loss: 4.0276 - val_accuracy: 0.5606\n",
            "Epoch 18/50\n",
            "17/17 [==============================] - 2s 93ms/step - loss: 0.0877 - accuracy: 0.9579 - val_loss: 4.0638 - val_accuracy: 0.6061\n",
            "Epoch 19/50\n",
            "17/17 [==============================] - 2s 92ms/step - loss: 0.1258 - accuracy: 0.9655 - val_loss: 4.3004 - val_accuracy: 0.5303\n",
            "Epoch 20/50\n",
            "17/17 [==============================] - 2s 98ms/step - loss: 0.0843 - accuracy: 0.9847 - val_loss: 4.0210 - val_accuracy: 0.5606\n",
            "Epoch 21/50\n",
            "17/17 [==============================] - 2s 94ms/step - loss: 0.1956 - accuracy: 0.9502 - val_loss: 4.3036 - val_accuracy: 0.6364\n",
            "Epoch 22/50\n",
            "17/17 [==============================] - 2s 92ms/step - loss: 0.0807 - accuracy: 0.9693 - val_loss: 4.9890 - val_accuracy: 0.5303\n",
            "Epoch 23/50\n",
            "17/17 [==============================] - 2s 93ms/step - loss: 0.2092 - accuracy: 0.9387 - val_loss: 5.1162 - val_accuracy: 0.5455\n",
            "Epoch 24/50\n",
            "17/17 [==============================] - 2s 93ms/step - loss: 0.0599 - accuracy: 0.9732 - val_loss: 4.5475 - val_accuracy: 0.5758\n",
            "Epoch 25/50\n",
            "17/17 [==============================] - 2s 93ms/step - loss: 0.2203 - accuracy: 0.9425 - val_loss: 5.3968 - val_accuracy: 0.5606\n",
            "Epoch 26/50\n",
            "17/17 [==============================] - 2s 94ms/step - loss: 0.1341 - accuracy: 0.9770 - val_loss: 6.3528 - val_accuracy: 0.5000\n",
            "Epoch 27/50\n",
            "17/17 [==============================] - 2s 93ms/step - loss: 0.0951 - accuracy: 0.9617 - val_loss: 7.8083 - val_accuracy: 0.5455\n",
            "Epoch 28/50\n",
            "17/17 [==============================] - 2s 93ms/step - loss: 0.0938 - accuracy: 0.9693 - val_loss: 5.7891 - val_accuracy: 0.5606\n",
            "Epoch 29/50\n",
            "17/17 [==============================] - 2s 92ms/step - loss: 0.1348 - accuracy: 0.9617 - val_loss: 5.3809 - val_accuracy: 0.6515\n",
            "Epoch 30/50\n",
            "17/17 [==============================] - 2s 94ms/step - loss: 0.1874 - accuracy: 0.9502 - val_loss: 6.0165 - val_accuracy: 0.5909\n",
            "Epoch 31/50\n",
            "17/17 [==============================] - 2s 94ms/step - loss: 0.0729 - accuracy: 0.9732 - val_loss: 5.2241 - val_accuracy: 0.5758\n",
            "Epoch 32/50\n",
            "17/17 [==============================] - 2s 93ms/step - loss: 0.0910 - accuracy: 0.9808 - val_loss: 5.7217 - val_accuracy: 0.5606\n",
            "Epoch 33/50\n",
            "17/17 [==============================] - 2s 94ms/step - loss: 0.2123 - accuracy: 0.9464 - val_loss: 4.8334 - val_accuracy: 0.6061\n",
            "Epoch 34/50\n",
            "17/17 [==============================] - 2s 93ms/step - loss: 0.0603 - accuracy: 0.9885 - val_loss: 6.0222 - val_accuracy: 0.6212\n",
            "Epoch 35/50\n",
            "17/17 [==============================] - 2s 93ms/step - loss: 0.0428 - accuracy: 0.9808 - val_loss: 6.3823 - val_accuracy: 0.5758\n",
            "Epoch 36/50\n",
            "17/17 [==============================] - 2s 92ms/step - loss: 0.0619 - accuracy: 0.9770 - val_loss: 5.9423 - val_accuracy: 0.6515\n",
            "Epoch 37/50\n",
            "17/17 [==============================] - 2s 93ms/step - loss: 0.0620 - accuracy: 0.9770 - val_loss: 6.0685 - val_accuracy: 0.6061\n",
            "Epoch 38/50\n",
            "17/17 [==============================] - 2s 93ms/step - loss: 0.0555 - accuracy: 0.9885 - val_loss: 7.1240 - val_accuracy: 0.5455\n",
            "Epoch 39/50\n",
            "17/17 [==============================] - 2s 94ms/step - loss: 0.0104 - accuracy: 0.9962 - val_loss: 6.3217 - val_accuracy: 0.6364\n",
            "Epoch 40/50\n",
            "17/17 [==============================] - 2s 94ms/step - loss: 0.0897 - accuracy: 0.9770 - val_loss: 7.7627 - val_accuracy: 0.6061\n",
            "Epoch 41/50\n",
            "17/17 [==============================] - 2s 95ms/step - loss: 0.0600 - accuracy: 0.9808 - val_loss: 7.4649 - val_accuracy: 0.5758\n",
            "Epoch 42/50\n",
            "17/17 [==============================] - 2s 94ms/step - loss: 0.1157 - accuracy: 0.9693 - val_loss: 6.4832 - val_accuracy: 0.6061\n",
            "Epoch 43/50\n",
            "17/17 [==============================] - 2s 93ms/step - loss: 0.0938 - accuracy: 0.9808 - val_loss: 6.5912 - val_accuracy: 0.6061\n",
            "Epoch 44/50\n",
            "17/17 [==============================] - 2s 95ms/step - loss: 0.0227 - accuracy: 0.9962 - val_loss: 6.7776 - val_accuracy: 0.6061\n",
            "Epoch 45/50\n",
            "17/17 [==============================] - 2s 92ms/step - loss: 0.0675 - accuracy: 0.9923 - val_loss: 6.5223 - val_accuracy: 0.5909\n",
            "Epoch 46/50\n",
            "17/17 [==============================] - 2s 94ms/step - loss: 0.0205 - accuracy: 0.9923 - val_loss: 8.2756 - val_accuracy: 0.6061\n",
            "Epoch 47/50\n",
            "17/17 [==============================] - 2s 93ms/step - loss: 0.1856 - accuracy: 0.9617 - val_loss: 6.5450 - val_accuracy: 0.5758\n",
            "Epoch 48/50\n",
            "17/17 [==============================] - 2s 94ms/step - loss: 0.0263 - accuracy: 0.9885 - val_loss: 6.7647 - val_accuracy: 0.6212\n",
            "Epoch 49/50\n",
            "17/17 [==============================] - 2s 94ms/step - loss: 0.1103 - accuracy: 0.9770 - val_loss: 6.5702 - val_accuracy: 0.5758\n",
            "Epoch 50/50\n",
            "17/17 [==============================] - 2s 96ms/step - loss: 0.0473 - accuracy: 0.9847 - val_loss: 7.1947 - val_accuracy: 0.6364\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation"
      ],
      "metadata": {
        "id": "cIp4IOWhIjE5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\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",
        "epochs = range(1, len(acc) + 1)\n",
        "plt.plot(epochs, acc, \"bo\", label=\"Training accuracy\")\n",
        "plt.plot(epochs, val_acc, \"b\", label=\"Validation accuracy\")\n",
        "plt.title(\"Training and validation accuracy\")\n",
        "plt.legend()\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, \"bo\", label=\"Training loss\")\n",
        "plt.plot(epochs, val_loss, \"b\", label=\"Validation loss\")\n",
        "plt.title(\"Training and validation loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "9JKP4ShYb4Yd",
        "outputId": "d75a4030-3ef7-44fa-d5e6-658813eaf111"
      },
      "execution_count": null,
      "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"
          }
        },
        {
          "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 = keras.models.load_model(\"dance.keras\")\n",
        "test_loss, test_acc = model.evaluate(test_dataset)\n",
        "print(f\"Test accuracy: {test_acc:.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Ol8PAXNdjsP",
        "outputId": "1279fc0c-6b4d-4478-e3fa-ef73fd61b72c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 0s 46ms/step - loss: 1.5438 - accuracy: 0.6757\n",
            "Test accuracy: 0.676\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UY5ypd7Aixfl"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}