AI-Song-Rater/pngToCutomModel.py at main · PeterM-13/AI-Song-Rater · GitHub

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import os
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing import image
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications.mobilenet import preprocess_input
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Flatten, Dense, Conv2D, MaxPooling2D
import seaborn as sns
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import BatchNormalization
from sklearn.metrics import ConfusionMatrixDisplay as cmd
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint
from datetime import datetime
from tensorflow.keras.regularizers import l2
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.applications.efficientnet import preprocess_input
from tensorflow.keras.callbacks import ReduceLROnPlateau


def load_images_from_path(path):
    images, labels = [], []

    for file in os.listdir(path):
        if file.lower().endswith('.png'):
            images.append(image.img_to_array(image.load_img(os.path.join(path, file),
                                                            target_size=(224, 224, 3))))
            labels.append((int(file[0])-1))

    return images, labels

def show_images(images):
    fig, axes = plt.subplots(1, 8, figsize=(20, 20),
                             subplot_kw={'xticks': [], 'yticks': []})

    for i, ax in enumerate(axes.flat):
        ax.imshow(images[i] / 255)


x, y = [], []

images, labels = load_images_from_path('Spectrograms')

x += images
y += labels


x_train, x_test, y_train, y_test = train_test_split(x, y, stratify=y, test_size=0.1,
                                                    random_state=0)

y_train = np.array(y_train)
y_test = np.array(y_test)


timestamp = datetime.now().strftime("%m-%d-%y_%H:%M:%S")
checkpoint = ModelCheckpoint(
    filepath=f'Models/Sonai_{timestamp}.h5',
    monitor='val_accuracy',           # or 'val_loss'
    save_best_only=True,
    mode='max',                       # use 'min' for val_loss
    verbose=1
)

x_train_norm = preprocess_input(np.array(x_train))
x_test_norm = preprocess_input(np.array(x_test))


model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)))
model.add(MaxPooling2D(2, 2))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(2, 2))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(2, 2))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D(2, 2))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D(2, 2))
model.add(Flatten())
model.add(Dense(512, activation='relu', kernel_regularizer=l2(0.001)))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(128, activation='relu', kernel_regularizer=l2(0.001)))
model.add(BatchNormalization())
model.add(Dense(5, activation='softmax'))

model.compile(optimizer=Adam(learning_rate=0.0005),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Callbacks
early_stop = EarlyStopping(monitor='val_accuracy', patience=15, restore_best_weights=True)
lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, verbose=1)

hist = model.fit(x_train_norm, y_train, validation_data=(x_test_norm, y_test),
                 batch_size=22, epochs=40, callbacks=[checkpoint, early_stop, lr_reducer])

sns.set()

acc = hist.history['accuracy']
val_acc = hist.history['val_accuracy']
epochs = range(1, len(acc) + 1)

plt.plot(epochs, acc, '-', label='Training Accuracy')
plt.plot(epochs, val_acc, ':', label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')

# Save the plot with a timestamp
plot_filename = f"Models/Plot_{timestamp}.png"
plt.savefig(plot_filename)