Hi all! Thanks for this awesome course and the great people who take the time to answer questions on the threads.

I have trained a model with two classes and multi class images. I am pretty happy with the training result. However, now when I try to predict an image using predict_array based on a saved model, I get constant response that looks like this: [[0. 1.]]. I get the same response on different images and even on a completely black image.

Here’s the code I’ve used for training. Please note that I have not used any augmentation as I am using 100 000 images.

```
f_model = resnet101
n = len(list(open(CSVPATH)))-1
val_idxs = get_cv_idxs(n)
def get_data(sz):
tfms = tfms_from_model(f_model, sz)
return ImageClassifierData.from_csv(PATH, 'train', CSVPATH, tfms=tfms,
val_idxs=val_idxs)
sz = 128
data = get_data(sz)
# Since we are training earlier layers in the model, precompute needs to be False
learn = ConvLearner.pretrained(f_model, data, precompute=False)
lr = 0.0045
lrs = np.array([lr/9,lr/3,lr])
learn.unfreeze()
learn.fit(lrs, 3, cycle_len=1, cycle_mult=2)
learn.save(f'{MODELPATH}{sz}')
```

The last Epoch looks like this:

`6 0.069832 0.090509 0.967358`

After saving the model, this is the code I use to predict a single image:

```
predict = ConvLearner.pretrained(f_model, data, precompute=False)
predict.load(f'{MODELPATH}{sz}')
predict.model.eval()
trn_tfms, val_tfms = tfms_from_model(f_model, sz)
fileName = PREDICTPATH+"im1.jpg"
im = np.array(Image.open(fileName).resize((sz, sz)))
im_transformed = val_tfms(im)
plt.imshow(im)
pr = predict.predict_array(im_transformed[None])
print(pr)
```

Any help is appreciated. Thanks!