cnn_genre_classifier_keras.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import logging
import os
from pathlib import Path

import keras
from keras.models import Sequential
from keras.layers import Dense, Conv2D, Flatten, Dropout

from GenerateSpectrogramData import (
    GenerateSpectrogramData,
)

from keras import backend as K

K.set_image_dim_ordering('tf')

logging.getLogger("tensorflow").setLevel(logging.ERROR)

genre_features = GenerateSpectrogramData()

if (
        os.path.isfile(genre_features.train_X_preprocessed_data)
        and os.path.isfile(genre_features.train_Y_preprocessed_data)
        and os.path.isfile(genre_features.dev_X_preprocessed_data)
        and os.path.isfile(genre_features.dev_Y_preprocessed_data)
        and os.path.isfile(genre_features.test_X_preprocessed_data)
        and os.path.isfile(genre_features.test_Y_preprocessed_data)
):
    print("Preprocessed files exist, deserializing npy files")
    genre_features.load_deserialize_data()
else:
    print("Preprocessing raw audio files")
    genre_features.load_preprocess_data()

print("Training X shape: " + str(genre_features.train_X.shape))
print("Training Y shape: " + str(genre_features.train_Y.shape))
print("Dev X shape: " + str(genre_features.dev_X.shape))
print("Dev Y shape: " + str(genre_features.dev_Y.shape))
print("Test X shape: " + str(genre_features.test_X.shape))
print("Test Y shape: " + str(genre_features.test_Y.shape))

shaped_train_X = genre_features.train_X.reshape(genre_features.train_X.shape[0], 1, genre_features.train_X.shape[1],
                                                genre_features.train_X.shape[2])
shaped_dev_X = genre_features.dev_X.reshape(genre_features.dev_X.shape[0], 1, genre_features.dev_X.shape[1],
                                            genre_features.dev_X.shape[2])
shaped_test_X = genre_features.test_X.reshape(genre_features.test_X.shape[0], 1, genre_features.test_X.shape[1],
                                              genre_features.test_X.shape[2])

input_shape = (shaped_train_X.shape[1], shaped_train_X.shape[2], shaped_train_X.shape[3])
print("Build CNN model ...")
model = Sequential()

model.add(Conv2D(32, kernel_size=(4, 4), activation="relu", padding='same', input_shape=input_shape))
model.add(Conv2D(64, kernel_size=(5, 5), activation="relu", padding='same'))
model.add(Conv2D(32, kernel_size=(4, 4), activation="relu", padding='same'))
model.add(Conv2D(18, kernel_size=(2, 2), activation="relu", padding='same'))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(len(genre_features.genre_list), activation="softmax"))

print("Compiling ...")
opt = keras.optimizers.SGD()
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
model.summary()

print("Training ...")
batch_size = 25  # num of training examples per minibatch
num_epochs = 500
model.fit(
    shaped_train_X,
    genre_features.train_Y,
    batch_size=batch_size,
    epochs=num_epochs,
)

print("\nValidating ...")
score, accuracy = model.evaluate(
    shaped_dev_X, genre_features.dev_Y, batch_size=batch_size, verbose=1
)
print("Dev loss:  ", score)
print("Dev accuracy:  ", accuracy)

print("\nTesting ...")
score, accuracy = model.evaluate(
    shaped_test_X, genre_features.test_Y, batch_size=batch_size, verbose=1
)
print("Test loss:  ", score)
print("Test accuracy:  ", accuracy)

# Creates a HDF5 file
model_filename = "cnn_genre_classifier_lstm_25_500_sgd_80w.h5"
print("\nSaving model: " + model_filename)
model.save(model_filename)
# Creates a json file
print("creating .json file....")
model_json = model.to_json()
f = Path("cnn_genre_classifier_lstm_25_500_sgd_80w.json")
f.write_text(model_json)