Hello, Happy new year! I wish you a happy, productive, and fruitful new year. I just have a Keras question and through you might have an idea on it, especially that I have been trying to solve the issue for a while now but couldn't yet solve the issue. The following code always predicts the same class. I tried balancing the training dataset and used different data (i.e. melanoma vs. nevus, cats vs. dogs), but always having only "1" class predicted. from keras import layers

from keras import models from keras import optimizers from keras.preprocessing.image import ImageDataGenerator from sklearn.metrics import roc_curve from sklearn.metrics import auc import cv2 import numpy as np import os

train_directory = '/train' validation_directory = '/valid' test_directory = '/test' results_directory = '/results' correct_classification = 0 number_of_test_images = 0 labels = [] prediction_probabilities = []

model = models.Sequential()

model.add(layers.Conv2D(32,(3,3),activation='relu',input_shape=(512,512,3))) model.add(layers.MaxPooling2D(2,2)) model.add(layers.Conv2D(64,(3,3),activation='relu')) model.add(layers.MaxPooling2D(2,2)) model.add(layers.Conv2D(128,(3,3),activation='relu')) model.add(layers.MaxPooling2D(2,2)) model.add(layers.Conv2D(256,(3,3),activation='relu')) model.add(layers.MaxPooling2D(2,2)) model.add(layers.Conv2D(512,(3,3),activation='relu')) model.add(layers.MaxPooling2D(2,2))

model.add(layers.Flatten()) model.add(layers.Dense(1024,activation='relu')) model.add(layers.Dense(1,activation='sigmoid'))

model.compile(loss='binary_crossentropy',optimizer='rmsprop',metrics=['acc'])

train_data = ImageDataGenerator(rescale=1.0/255) validation_data = ImageDataGenerator(rescale=1.0/255)

train_generator = train_data.flow_from_directory(train_directory,target_size=(512,512),batch_size=20,class_mode='binary') validation_generator = validation_data.flow_from_directory(validation_directory,target_size=(512,512),batch_size=20,class_mode='binary')

history = model.fit_generator(train_generator, steps_per_epoch=10, epochs=10, validation_data=validation_generator, validation_steps=5)

model.save('my_model.h5')

for root, dirs, files in os.walk(test_directory): for file in files: img = cv2.imread(root + '/' + file) img = cv2.resize(img,(512,512),interpolation=cv2.INTER_AREA) img = np.expand_dims(img, axis=0) img = img/255.0 if os.path.basename(root) == 'nevus': label = 1 elif os.path.basename(root) == 'melanoma': label = 0 labels.append(label) img_class = model.predict_classes(img) img_class_probability = model.predict(img) prediction_probability = img_class_probability[0] prediction_probabilities.append(prediction_probability) prediction = img_class[0] if prediction == label: correct_classification = correct_classification + 1

Any ideas on how the issue could be solved? Thanks a lot, and appreciate you kind support.