{"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_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom tqdm.notebook import tqdm\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\nfrom tensorflow.keras.layers import Conv2D, MaxPool2D, Input, GlobalAvgPool2D, Dense\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.optimizers import RMSprop\nfrom tensorflow import keras\n\n# from google.colab import drive\n\n# drive.mount('/content/drive')\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\ndata_path= '/kaggle/input/redesunicen2021/'\nmodel_path= '/kaggle/input/redesunicen2021/sample'\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\ntrain = pd.read_csv(data_path + 'train.csv')\ntest = pd.read_csv(data_path + 'test.csv')\ntrain.head()\ntest.head()\nds = np.load(data_path + 'processed.npz')\nx = ds['x_train']\ny = ds['y_train']\nx_test = ds['x_test']\nprint(x.shape)\ndel ds\n\nig, ax = plt.subplots()\ncax = ax.imshow(x[0,...], interpolation='nearest', cmap=cm.coolwarm, origin='lower', aspect='auto')\nax.set_title('MFCC')\n#Showing mfcc_data\nplt.show()\nprint(np.max(x))\nprint(np.min(x))\n\nx_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.2, random_state=42, stratify=y)\ndel x\ndel y\nx_train = np.expand_dims(x_train, axis=-1) / -80\nx_val = np.expand_dims(x_val, axis=-1) / -80\ny_train = np.asarray(y_train)\ny_val = np.asarray(y_val)\n\ni = Input((128, 500, 1))\nc = Conv2D(32, (5,5), activation='relu')(i)\nc = Conv2D(32, (5,5), activation='relu')(c)\nc= MaxPool2D()(c)\nc = Conv2D(64, (5,5), activation='relu')(c)\nc = Conv2D(64, (5,5), activation='relu')(c)\nc= MaxPool2D()(c)\nc = Conv2D(128, (5,5), activation='relu')(c)\nc = Conv2D(128, (5,5), activation='relu')(c)\nc = GlobalAvgPool2D()(c)\nc = Dense(8, activation='softmax')(c)\n\nmodel = Model(i, c)\nmodel.compile(loss='sparse_categorical_crossentropy', optimizer=RMSprop(learning_rate=1e-4), \n              metrics=['sparse_categorical_accuracy'])\nmodel.summary()\n\nif not os.path.exists(model_path + 'sample_model.h5'):\n    model.fit(x_train[:, :,500:1000, :], y_train, epochs=10, validation_data=(x_val[:, :,500:1000, :], y_val))\n    model.save(model_path + 'sample_model.h5')\n    \nmodel.fit(x_train[:, :,500:1000, :], y_train, epochs=10, validation_data=(x_val[:, :,500:1000, :], y_val))\n\ndel model\ndel i\ndel c\nmodel = keras.models.load_model(model_path + 'sample_model.h5')\nx_test = np.expand_dims(x_test, axis=-1) / -80\ny_pred = np.argmax(model.predict(x_test[:, :, 500:1000, :]), axis=-1)\n\nprediction = test['track_id'].to_frame()\nprediction['genre'] = y_pred\n#Descomentar para guardar las predicciones\n#prediction.to_csv(model_path + 'sample_predictions.csv', index=False)\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-11-09T14:00:55.59339Z","iopub.execute_input":"2021-11-09T14:00:55.593845Z","iopub.status.idle":"2021-11-09T14:05:15.122638Z","shell.execute_reply.started":"2021-11-09T14:00:55.593751Z","shell.execute_reply":"2021-11-09T14:05:15.121579Z"},"trusted":true},"execution_count":null,"outputs":[]}]}