Added code for CNN music genre classifier.

Author: musikalkemist

16- How to implement a CNN for music genre classification/code/cnn_genre_classifier.py

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+import json
+import numpy as np
+from sklearn.model_selection import train_test_split
+import tensorflow.keras as keras
+import matplotlib.pyplot as plt
+
+DATA_PATH = "../13/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)
+
+    X = np.array(data["mfcc"])
+    y = np.array(data["labels"])
+    return X, y
+
+
+def plot_history(history):
+    """Plots accuracy/loss for training/validation set as a function of the epochs
+
+        :param history: Training history of model
+        :return:
+    """
+
+    fig, axs = plt.subplots(2)
+
+    # create accuracy sublpot
+    axs[0].plot(history.history["accuracy"], label="train accuracy")
+    axs[0].plot(history.history["val_accuracy"], label="test accuracy")
+    axs[0].set_ylabel("Accuracy")
+    axs[0].legend(loc="lower right")
+    axs[0].set_title("Accuracy eval")
+
+    # create error sublpot
+    axs[1].plot(history.history["loss"], label="train error")
+    axs[1].plot(history.history["val_loss"], label="test error")
+    axs[1].set_ylabel("Error")
+    axs[1].set_xlabel("Epoch")
+    axs[1].legend(loc="upper right")
+    axs[1].set_title("Error eval")
+
+    plt.show()
+
+
+def prepare_datasets(test_size, validation_size):
+    """Loads data and splits it into train, validation and test sets.
+
+    :param test_size (float): Value in [0, 1] indicating percentage of data set to allocate to test split
+    :param validation_size (float): Value in [0, 1] indicating percentage of train set to allocate to validation split
+
+    :return X_train (ndarray): Input training set
+    :return X_validation (ndarray): Input validation set
+    :return X_test (ndarray): Input test set
+    :return y_train (ndarray): Target training set
+    :return y_validation (ndarray): Target validation set
+    :return y_test (ndarray): Target test set
+    """
+
+    # load data
+    X, y = load_data(DATA_PATH)
+
+    # create train, validation and test split
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)
+    X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validation_size)
+
+    # add an axis to input sets
+    X_train = X_train[..., np.newaxis]
+    X_validation = X_validation[..., np.newaxis]
+    X_test = X_test[..., np.newaxis]
+
+    return X_train, X_validation, X_test, y_train, y_validation, y_test
+
+
+def build_model(input_shape):
+    """Generates CNN model
+
+    :param input_shape (tuple): Shape of input set
+    :return model: CNN model
+    """
+
+    # build network topology
+    model = keras.Sequential()
+
+    # 1st conv layer
+    model.add(keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
+    model.add(keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same'))
+    model.add(keras.layers.BatchNormalization())
+
+    # 2nd conv layer
+    model.add(keras.layers.Conv2D(32, (3, 3), activation='relu'))
+    model.add(keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same'))
+    model.add(keras.layers.BatchNormalization())
+
+    # 3rd conv layer
+    model.add(keras.layers.Conv2D(32, (2, 2), activation='relu'))
+    model.add(keras.layers.MaxPooling2D((2, 2), strides=(2, 2), padding='same'))
+    model.add(keras.layers.BatchNormalization())
+
+    # flatten output and feed it into dense layer
+    model.add(keras.layers.Flatten())
+    model.add(keras.layers.Dense(64, activation='relu'))
+    model.add(keras.layers.Dropout(0.3))
+
+    # output layer
+    model.add(keras.layers.Dense(10, activation='softmax'))
+
+    return model
+
+
+def predict(model, X, y):
+    """Predict a single sample using the trained model
+
+    :param model: Trained classifier
+    :param X: Input data
+    :param y (int): Target
+    """
+
+    # add a dimension to input data for sample - model.predict() expects a 4d array in this case
+    X = X[np.newaxis, ...] # array shape (1, 130, 13, 1)
+
+    # perform prediction
+    prediction = model.predict(X)
+
+    # get index with max value
+    predicted_index = np.argmax(prediction, axis=1)
+
+    print("Target: {}, Predicted label: {}".format(y, predicted_index))
+
+
+if __name__ == "__main__":
+
+    # get train, validation, test splits
+    X_train, X_validation, X_test, y_train, y_validation, y_test = prepare_datasets(0.25, 0.2)
+
+    # create network
+    input_shape = (X_train.shape[1], X_train.shape[2], 1)
+    model = build_model(input_shape)
+
+    # 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_validation, y_validation), batch_size=32, epochs=30)
+
+    # plot accuracy/error for training and validation
+    plot_history(history)
+
+    # evaluate model on test set
+    test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)
+    print('\nTest accuracy:', test_acc)
+
+    # pick a sample to predict from the test set
+    X_to_predict = X_test[100]
+    y_to_predict = y_test[100]
+
+    # predict sample
+    predict(model, X_to_predict, y_to_predict)
+
+