train_test_images.py

# Images
train_images = df_train.loc[:,'image_name']
train_labels = df_train.loc[:,'category']

test_images = df_test.loc[:,'image_name']
test_labels = df_test.loc[:,'category']

# Train images
x_train = []
for i in train_images:
    image = home_path+'/images/'+i
    img = cv2.imread(image)
    x_train.append(img)

# Train labels
y_train=keras.utils.to_categorical(train_labels)

# Test images
x_test = []
for i in test_images:
    image = home_path+'/images/'+i
    img = cv2.imread(image)
    x_test.append(img)

# Test labels
y_test=keras.utils.to_categorical(test_labels)

# Normalize images
x_train = np.array(x_train, dtype="float") / 255.0
x_test = np.array(x_test, dtype="float") / 255.0