Hi @Paolo_Pini You can try tf.keras.Model.predict as in name_of_your_model.predict(...) (be mindful of tensor shapes).
Examples:

Image classification  |  TensorFlow Core 2


Predict on new data
Finally, let’s use our model to classify an image that wasn’t included in the training or validation sets.
…
sunflower_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/592px-Red_sunflower.jpg"
sunflower_path = tf.keras.utils.get_file('Red_sunflower', origin=sunflower_url)

img = keras.preprocessing.image.load_img(
    sunflower_path, target_size=(img_height, img_width)
)
img_array = keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0) # Create a batch

predictions = model.predict(img_array)
...



Writing your own callbacks  |  TensorFlow Core


Now, define a simple custom callback that logs:

When fit/evaluate/predict starts & ends
When each epoch starts & ends
When each training batch starts & ends
When each evaluation (test) batch starts & ends
When each inference (prediction) batch starts & ends
…

class CustomCallback(keras.callbacks.Callback):
  ...
...
res = model.predict(x_test, batch_size=128, callbacks=[CustomCallback()])