Thanks David!
Its weird.
I created a separate folder and ran this code
def get_data(path):
gen = image.ImageDataGenerator()
batches = gen.flow_from_directory(path, target_size=(540,270),
class_mode=None, shuffle=False, batch_size=2 )
result = np.concatenate([batches.next() for i in range(batches.nb_sample)])
print(result.shape)
return result
val_data = get_data(path+'imgflip')
plt.imshow(val_data[0]) Shows the negative but
plt.imshow(val_data[0]*255) shows the correct image. Even though if I printed val_data[0] itself it shows values above 1
array([[[ 133., 131., 134.],
[ 127., 126., 131.],
[ 123., 124., 129.],
[ 113., 117., 126.],
[ 108., 115., 125.],
[ 99., 107., 118.],
[ 91., 101., 111.],
[ 88., 101., 110.],
[ 82., 95., 104.],
[ 76., 89., 98.],
[ 68., 78., 87.],
[ 60., 69., 74.],
I am going to train my model again on this muiltiplied number because I am not sure if the negative values will impact the model.
Let me know if you have any thoughts