from keras.preprocessing.image import ImageDataGenerator datagen = ImageDataGenerator( rescale=1.0/255.0, validation_split=0.2 ) train_gen = datagen.flow_from_dataframe( dataframe= df, directory=None, x_col="image_path", y_col=training_df.columns[1:], subset="training", batch_size=32, shuffle=True, class_mode="raw", target_size=(224, 224), ) valid_gen = datagen.flow_from_dataframe( dataframe= df, directory=None, x_col="image_path", y_col=training_df.columns[1:], subset="validation", batch_size=32, shuffle=True, class_mode="raw", target_size=(224, 224), )

from livelossplot import PlotLossesKeras plotlosses = PlotLossesKeras() history = model.fit( train_gen, validation_data=valid_gen, epochs=10, callbacks=[plotlosses], verbose=0 )

from sklearn.model_selection import train_test_split from keras.preprocessing.image import load_img, img_to_array import numpy as np def load_image(image_path, target_size=(224,224)): img = load_img(image_path, target_size=target_size) img = img_to_array(img) img = img / 255.0 # Normalize to [0,1] return img subset_df = df.sample(n=10, random_state=42) train_df, val_df = train_test_split(subset_df, test_size=0.2, random_state=42) X_train = np.array([load_image(path) for path in train_df['image_path']]) Y_train = train_df[train_df.columns[1:]].values # Assumes first column is image_path, rest are labels X_val = np.array([load_image(path) for path in val_df['image_path']]) Y_val = val_df[val_df.columns[1:]].values

from livelossplot import PlotLossesKeras plotlosses = PlotLossesKeras() history = model.fit( X_train, Y_train, validation_data=(X_val, Y_val), epochs=10, callbacks=[plotlosses], verbose=0, )