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genre_classifier.py
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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
DATASET_PATH = 'data.json'
def load_data(dataset_path):
with open(dataset_path,'r') as fp:
data = json.load(fp)
# convert lists into numpy arrays
inputs = np.array(data['mfcc'])
targets = np.array(data['labels'])
return inputs, targets
def plot_history(history):
fig, axs = plt.subplots(2)
# create the accuracy subplot
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 the accuracy subplot
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("Epochs")
axs[1].legend(loc='upper right')
axs[1].set_title("Error eval")
plt.show()
if __name__ == '__main__':
# load data
inputs, targets = load_data(DATASET_PATH)
# split the data into train and test sets
inputs_train, inputs_test, targets_train, targets_test = train_test_split(inputs, targets, test_size=0.3)
# build the network architecture
model = keras.Sequential([
# input layer
keras.layers.Flatten(input_shape=(inputs.shape[1], inputs.shape[2])),
# first hidden layer
keras.layers.Dense(512, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
keras.layers.Dropout(0.3),
# second hidden layer
keras.layers.Dense(256, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
keras.layers.Dropout(0.3),
# third hidden layer
keras.layers.Dense(128, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
keras.layers.Dropout(0.3),
# output layer
keras.layers.Dense(10, activation='softmax')
])
# compile network
optimizer = keras.optimizers.Adam(learning_rate=0.0001)
model.compile(optimizer=optimizer,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.summary()
# train network
history = model.fit(inputs_train, targets_train,
validation_data=(inputs_test, targets_test),
epochs=100,
batch_size=32)
# plot accuracy and error over the epochs
plot_history(history)