{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"import librosa\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport os\nfrom PIL import Image\nimport pathlib\nimport csv\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder, StandardScaler\nimport keras\nfrom keras import layers\nfrom keras import layers\nimport keras\nfrom keras.models import Sequential\nimport warnings\nwarnings.filterwarnings('ignore')","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"data = pd.read_csv('../input/birdcalldataset/dataset.csv')\ndata.head()\n# Удаление ненужных столбцов\ndata = data.drop(['filename'],axis=1)\n# Создание меток\ngenre_list = data.iloc[:, -1]\nencoder = LabelEncoder()\ny = encoder.fit_transform(genre_list)\n# Масштабирование столбцов признаков\nscaler = StandardScaler()\nX = scaler.fit_transform(np.array(data.iloc[:, :-1], dtype = float))\n# Разделение данных на обучающий и тестовый набор\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"num_class = len(data.label.unique())\n\nmodel = Sequential()\nmodel.add(layers.Dense(256, activation='relu', input_shape=(X_train.shape[1],)))\nmodel.add(layers.Dense(128, activation='relu'))\nmodel.add(layers.Dense(64, activation='relu'))\nmodel.add(layers.Dense(num_class, activation='softmax'))\nmodel.compile(optimizer='adam',\n              loss='sparse_categorical_crossentropy',\n              metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"classifier = model.fit(X_train,\n                    y_train,\n                    epochs=100,\n                    batch_size=128)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}