How to calculate outputs of different trained network all at once, i.e without using for loop for each network IN python?

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Simply, Imagine I have 100 trained Neural Networks, And a Test image( or Samples, tensor, vector etc.) I want to calculate output of all my trained nets on test sample, One simple way is to use for loop, and calculate network responses one by one. Like so :

def show_reconstructions(model,X_test):
    reconstructions = model.predict(X_test)
    return reconstructions

for i in range(100):
    Rec                 = show_reconstructions(Nets[i],test_samples)
    Diff                = Rec - test_samples
    NormDiff.append     = np.linalg.norm(Diff)

I assign all my net object in a list named Nets, and finally I need the NormDiff variable which is a vector of norm of difference between test samples and net output (something like error, make sense) whose size is 100. My question is, simply: How Can I Remove The "for" Loop and calculate Whole of Network Outputs to Test Input, All At Once?? In order to save time and compute the calculations in shorter time(real time application is assumed). This is obvious that output of one network is completely independent of the other net outputs, so this task seems to be done in a parallel fashion.(?) But I am a new python coder and don't know how can do that. I try to do that with numba library, but it don't accept network object as input argument as list or dict.

keras tensorflow task-parallel-library real-time python
1 Answers

I have 2 suggestions :

  1. Parallelize the for loop to speed up the process How do I parallelize a simple Python loop?
  2. Create a giant model that is composed of the 100 models and you can predict only once.
def f(models):
    inputs = keras.Input(shape=(150, 150, 3))
    x = models[0](inputs, training=False)
    for model in models[1:]:
        res = model(inputs, training=False)
        x = tf.concat([x, res ])
    return tf.keras.Model(inputs=inputs, outputs=x)

model = f(models)
result = model.predict(input_)
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