I'm trying to predict a new image using trained model. My accuracy is 95%. But the predict_classes always return the first label [0] whatever I input.
I guess one of the reason is I use featurewise_center=True
and samplewise_center=True
in ImageDataGenerator
. I think I should do the same thing on my input image. But I can't find what did these function do to the image.
Any suggestion will be appreciated.
ImageDataGenerator
code:
train_datagen = ImageDataGenerator(
samplewise_center=True,
rescale=1. / 255,
shear_range=30,
zoom_range=30,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2)
test_datagen = ImageDataGenerator(
samplewise_center=True,
rescale=1. / 255,
shear_range=30,
zoom_range=30,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True)
Prediction code (I use 100*100*3 image to train the model):
model = load_model('CNN_model.h5')
img = cv2.imread('train/defect/6.png')
img = cv2.resize(img,(100,100))
img = np.reshape(img,[1,100,100,3])
img = img/255.
classes = model.predict_classes(img)
print (classes)
updated 11/14:
I change my code to predict image like below. But the model still predict the same class even if I feed the image which I have used to train my model(and got 95% accuarcy). Is there anything I missed?
model = load_model('CNN_model.h5')
img = cv2.imread('train/defect/6.png')
img = cv2.resize(img,(100,100))
img = np.reshape(img,[1,100,100,3])
img = np.array(img, dtype=np.float64)
img = train_datagen.standardize(img)
classes = model.predict_classes(img)
print(classes)
You need to use the standardize()
method of ImageDataGenerator
instance. From Keras documentation:
standardize
standardize(x)
Applies the normalization configuration to a batch of inputs.
Arguments
- x: Batch of inputs to be normalized.
Returns
The inputs, normalized.
So it would be like this:
img = cv2.imread('train/defect/6.png')
img = cv2.resize(img,(100,100))
img = np.reshape(img,[1,100,100,3])
img = train_datagen.standardize(img)
classes = model.predict_classes(img)
Note that it would apply the rescaling as well so there is no need to do it yourself (i.e. remove img = img/255.
).
Further, keep in mind that since you have set featurewise_ceneter=True
you need to use fit()
method of generator before using it for training:
train_datagen.fit(training_data)
# then use fit_generator method
model.fit_generator(train_datagen, ...)