Output is always 1

[freedom9393]

I'm running the following code to test my custom images, and here is the cude:

import tensorflow as tf
from utils import Dataset, plotImages, plotWrongImages
from models import EfficientCapsNet
import cv2, numpy as np

img_path = "imgs/box_9.jpg"
img_np = cv2.bitwise_not(cv2.imread(img_path, 0)) # invert conversion

gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_visible_devices(gpus[0], 'GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)

# some parameters
model_name = 'MULTIMNIST'
custom_path = None # if you've trained a new model, insert here the full graph weights path

dataset = Dataset(model_name, config_path='config.json')

model_test = EfficientCapsNet(model_name, mode='test', verbose=True, custom_path=custom_path)

model_test.load_graph_weights() # load graph weights (bin folder)

model_test.evaluate(dataset.X_test, dataset.y_test) # if "smallnorb" use X_test_patch

#not working with MultiMNIST

shape = dataset.X_test[0].shape

img_np = cv2.resize(img_np, shape[:2], interpolation=cv2.INTER_AREA)
img_np = img_np[..., None]

y_pred = model_test.predict(np.expand_dims(img_np, axis=0))[0] # if "smallnorb" use X_test_patch

print(np.argmax(y_pred))
print(y_pred)

cv2.imshow('show', img_np)
cv2.waitKey(0)

And here is the result:

[[0.3153154  0.956559   0.2567287  0.2726928  0.18593171 0.6188526
  0.14006346 0.44058013 0.36337793 0.15720315]]

As it's shown, index 1 has highest value and it means the output is 1. But I tested 13 images and all has highest value in index 1 (output is always 1). What am I doing wrong?

[fsalv]

How are your test images generated? You are using the model trained on the MultiMNIST dataset, that is expecting two MNIST images superimposed and has been trained in order to dect both of them. If you have classical digit images, set

model_name = 'MNIST'