Input 0 is incompatible with layer model

I’m trying the default training (satellite unet) and train went good but when I’m trying to test the model I got the error ValueError: Input 0 is incompatible with layer model: expected shape=(None, 384, 384, 3), found shape=(None, 348, 3)

Here is my test code

model_file = 'model_satellite.h5'
input_shape = (384, 384, 3)

model = satellite_unet(
    input_shape
)

model.load_weights(model_file)
image = np.array(Image.open("test.jpg").resize((348, 348)))
print("shape: ", image.shape)
shape:  (348, 348, 3)
pr_mask = model.predict(image).round()

[karolzak]

image = np.array(Image.open("test.jpg").resize((384, 384)))
print("shape: ", image.shape)
shape: (384, 384, 3)
pr_mask = model.predict(image).round()

Ok, I see your problem. When providing inputs for prediction you need to serve images in batches - if you want to fit just a single image you need to reshape it from (384, 384, 3) to (1, 384, 384, 3)

[parcodepie]

Indeed it was the problem. Here is the new working code

image = np.array(Image.open("test.jpg").resize((384, 384)))
images_list = []
images_list.append(np.array(image))
x = np.asarray(images_list)
pr_mask = model.predict(x).round()

plt.imshow(
    pr_mask[0]
)
plt.show()

also I’m saving the image to the disk

data = Image.fromarray(np.reshape(pr_mask[0], (384, 384)) )
data = data.convert("L")
data.save('xxx.png')

the model needs more training but it’s a good start

Thanks @karolzak for your time