prediction change for an image in a batch depending of the size of the batch.
@nomadeCodeur,
I tried to execute the mentioned code with the sample images and observed that the code was executed without any issue/error & the output is also intended. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/456ca94783a93daf9bd4be3012d76fb1/untitled1747.ipynb) and also try to execute the code with keras3.0 version. Thank you! @tilakrayal Thank you for your response. If I understand well, the output is as intended because it is really near to zeros (about 10**-8) . It is possible that is a model is not well trained, it deviate a lot from zeros ? I am trying to understand the possible causes of this phenomenon. In my case the deviation from zero was about 0.1 which is significant.

 @nomadeCodeur,
Could you please try to execute the code by passing a single image in multiple iterations as the input. Also **pred_batch** uses the entire batch, while **pred_batch2** uses only the first element. Depending on the model architecture, processing the entire batch might involve internal calculations that affect the final output for each element. This could lead to a slight difference even for the first element compared to using it alone. Thank you! This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. I tried to test the following. It gives very slight difference. I think there are internal calculations that are not significant for a well trained model. Thank you. 
print("start")

first_image = x_batch[0:1]
two_images = tf.concat([first_image, first_image], axis=0)
three_images = tf.concat([first_image, first_image, first_image], axis=0)

print("1 => "); print(model.predict(first_image, verbose=0))
print("2 => "); print(model.predict(two_images, verbose=0))
print("3 => "); print(model.predict(three_images, verbose=0))

The output is: 
start
1 => 
2024-03-22 15:44:55.612396: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8101
2024-03-22 15:44:56.980805: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.
[[0.39561632 0.52117676 0.08320692]]
2 => 
[[0.39560094 0.52118075 0.08321837]
 [0.39560094 0.52118075 0.08321837]]
3 => 
[[0.3956172  0.52115935 0.08322345]
 [0.3956172  0.52115935 0.08322345]
 [0.3956172  0.52115935 0.08322345]] Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63282">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63282">No</a>

### Issue type

Performance

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

2.10.1

### Custom code

Yes

### OS platform and distribution

Windows 11

### Mobile device

_No response_

### Python version

3.8.12

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

cuda 11.2 cuDnn 8.1

### GPU model and memory

NVIDIA GeForce RTX 3070 Ti

### Current behavior?

After training my model I save it using model.save(path_saving_model).

When I reload my model with model = keras.models.load_model(path_saved_model)

I run some predictions 
for x_batch, y_GT in batch_generator_i:


    print("start");
    print("1 => "); print(model.predict(x_batch, verbose=0));
    print("2 => "); print(model.predict(x_batch[0:1], verbose=0));
    print("end");

Inside one iteration I get the results in the console
start
1 => 
2024-03-08 10:27:03.928564: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8101
2024-03-08 10:27:05.143783: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.
[[8.15018594e-01 6.95814118e-02 1.15400046e-01]
 [9.66099262e-01 3.29955555e-02 9.05242225e-04]
 [5.14399529e-01 4.07320522e-02 4.44868475e-01]
 [4.52679815e-04 9.99545634e-01 1.65109623e-06]]
2 => 
[[0.7397079  0.12610717 0.13418493]]
end 

I thought that my second prediction should be [[8.15018594e-01 6.95814118e-02 1.15400046e-01]] instead of [[0.7397079  0.12610717 0.13418493]]
Why it is not the case?
What are the possible reasons for this ? 
I am running this in debug mode. I see that x_batch is an EagerTensor.

The model.summary() gives :
Model: "model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input (InputLayer)          [(None, None, None, 3)]   0         
                                                                 
 resizing_layer (Resizing)   (None, 128, 128, 3)       0         
                                                                 
 tf.__operators__.getitem (S  (None, 128, 128, 3)      0         
 licingOpLambda)                                                 
                                                                 
 tf.nn.bias_add (TFOpLambda)  (None, 128, 128, 3)      0         
                                                                 
 resnet50 (Functional)       (None, 2048)              23587712  
                                                                 
 output_softmax (Dense)      (None, 3)                 6147      
                                                                 
=================================================================
Total params: 23,593,859
Trainable params: 23,540,739
Non-trainable params: 53,120
_________________________________________________________________




### Standalone code to reproduce the issue

```shell
Above I use my own code base. Below is how I can reproduce. Just have a cat.jpg and elephant.jpg next to your python file.

import numpy as np
import keras
from keras.applications import ResNet50 
from keras.preprocessing import image

preprocess_input = keras.applications.resnet.preprocess_input
decode_predictions = keras.applications.resnet.decode_predictions

# Load pre-trained ResNet50 model without top (fully connected layers)
model = ResNet50(weights='imagenet', include_top=True)

# Download an example image for testing
img_path = 'elephant.jpg'  # You can change this path to the path of any image you want to use
img2_path = 'cat.jpg'

# Load and preprocess the image
img = keras.utils.load_img(img_path, target_size=(224, 224))
img_array = keras.utils.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array = preprocess_input(img_array)


# Load and preprocess the image
img2 = keras.utils.load_img(img2_path, target_size=(224, 224))
img2_array = keras.utils.img_to_array(img)
img2_array = np.expand_dims(img_array, axis=0)
img2_array = preprocess_input(img_array)

batch = np.concatenate((img_array, img2_array), axis=0)

# Make predictions
pred_batch = model.predict(batch)

pred_batch2 = model.predict(batch[0:1])

difference = pred_batch[0]-pred_batch2[0]

print(difference) ## In my understanding there should only have zeros in this vector.
```


### Relevant log output

_No response_