ResNet: ValueError: Input 0 is in compatible with layer model_7

Question

I have trained my ResNet101V2 model (keras) and have saved the model. On loading the model and trying to classify a new image, I keep getting the error: ValueError: Input 0 is incompatible with layer model_7: expected shape=(None, 255, 255, 3), found shape=(None, 255, 3)

Here's my code:

load_path = 'path to my model'
model = keras.models.load_model(load_path)

image_path = 'path to my image'
img_np = cv2.imread(image_path, cv2.IMREAD_COLOR)
resized_img_np = cv2.resize(img_np, (255, 255))
print(resized_img_np.shape) # <============= PRINTS (255, 255, 3)

prediction = model.predict(resized_img_np) # <========= ERROR

Answer 1

You need to add an extra dimension to match with batch size . Add a dimension using np.expand_dims to the resized image and pass to model for predictionion.

resized_img_np = np.expand_dims(resized_img_np,axis=0)
prediction = model.predict(resized_img_np)

Answer 2

As the model was trained on batches you have to add a batch value of 1 for a single sample, the error indicated that the size should be:

(None, 255, 255, 3)

Where the None shows the varying batchsize.

You can simply solve this by adding a "1" as the first dimension of your input image, showing that you are going to classify only one image.

Where the shape instead of (255, 255, 3) would be:

import numpy as np

resized_img_np = cv2.resize(np.array(img_np), (255, 255))
resized_img_np = np.expand_dims(resized_img_np, axis=0)