genre_classifier_basic_nn.py

# A note on altering code: The guidelines for this project mention 
# altering an existing codebase. However, for this project, I did 
# not adapt an existing codebase. I started out with a blank slate
# idea to classify music genres with a neural network. I made heavy 
# use of TensorFlow tutorials documentation and for ideas on the 
# structure and parameters of MLPs and CNNs for music genre 
# classification I have integrated code from Velardo’s YouTube 
# tutorial series (Velardo 2020).

# In-Code Citations will be inverted. I will cite where code was 
# taken from Velardo's tutorial series.

import json
import numpy as np
from sklearn.model_selection import train_test_split
import tensorflow.keras as keras

# path to json file that stores MFCCs and genre labels for each processed segment
DATA_PATH = "data_10.json"

def load_data(data_path):
    """Loads training dataset from json file.

        :param data_path (str): Path to json file containing data
        :return X (ndarray): Inputs
        :return y (ndarray): Targets
    """

    with open(data_path, "r") as fp:
        data = json.load(fp)

    # convert lists to numpy arrays
    X = np.array(data["mfcc"])
    y = np.array(data["labels"])

    print("Data succesfully loaded!")

    return  X, y




if __name__ == "__main__":

    # load data
    X, y = load_data(DATA_PATH)

    # create train/test split
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

    # build network topology
    model = keras.Sequential([

        # input layer
        keras.layers.Flatten(input_shape=(X.shape[1], X.shape[2])),

        # 1st dense layer
        keras.layers.Dense(512, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
        keras.layers.Dropout(0.3),

        # 2nd dense layer
        keras.layers.Dense(256, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
        keras.layers.Dropout(0.3),

        # 3rd dense layer
        keras.layers.Dense(64, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
        keras.layers.Dropout(0.3),

        # output layer
        keras.layers.Dense(10, activation='softmax')
    ])

    # compile model
    optimiser = keras.optimizers.Adam(learning_rate=0.0001)
    model.compile(optimizer=optimiser,
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])

    model.summary()

    # train model
    history = model.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size=32, epochs=50)