61 lines
1.5 KiB
Python
61 lines
1.5 KiB
Python
import json
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import numpy as np
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from sklearn.model_selection import train_test_split
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import tensorflow as tf
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import tf.keras as keras
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from pathlib import Path
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# path to json file that stores MFCCs and genre labels for each processed segment
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DATA_PATH = str(Path.cwd()) + "mfcc_data.json"
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def load_data(data_path):
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with open(data_path, "r") as fp:
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data = json.load(fp)
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# convert lists to numpy arrays
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X = np.array(data["mfcc"])
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y = np.array(data["labels"])
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print("Data succesfully loaded!")
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return X, y
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if __name__ == "__main__":
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# load data
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X, y = load_data(DATA_PATH)
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# create train/test split
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
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# build network topology
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model = keras.Sequential([
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# input layer
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keras.layers.Flatten(input_shape=(X.shape[1], X.shape[2])),
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# 1st dense layer
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keras.layers.Dense(512, activation='relu'),
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# 2nd dense layer
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keras.layers.Dense(256, activation='relu'),
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# 3rd dense layer
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keras.layers.Dense(64, activation='relu'),
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# output layer
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keras.layers.Dense(10, activation='softmax')
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])
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# compile model
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optimiser = keras.optimizers.Adam(learning_rate=0.0001)
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model.compile(optimizer=optimiser,
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loss='sparse_categorical_crossentropy',
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metrics=['accuracy'])
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model.summary()
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# train model
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history = model.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size=32, epochs=50) |