'''
An extremely hacky way of utilizing keras optimizers to optimize model inputs.
Can't be done using normal keras library (tf backend) since Tensors do not have the "assign" attr,
which is normally used for updating variables. Nevertheless, the grad associated with the Tensor is still calculated.

model : some keras model
loss : some defined loss
my_input : some numpy array that is used as input (i.e. model.predict(my_input))
num_iter : number of iterations

'''
def get_input_updates(optimizer,loss,model):
    def fake_assign(new_val,name=None):
        return new_val
    setattr(model.input,'assign',fake_assign)
    updates = optimizer.get_updates(loss=[loss],
                                     params=[model.input],
                                     )
    for idx,update in enumerate(updates):
        if update.op.inputs[0].name == model.input.name:
            updated_param = updates.pop(idx)
            break
    grad = updated_param - model.input
    return grad,updates

#Here is an example

optimizer = keras.optimizers.Adam(lr=0.9) #any keras optimizer
grad,optimizer_updates = get_input_updates(optimizer,loss,model.input)
opt_func = K.function([model.input],
                     [loss,grad],
                     updates=optimizer_updates,
                     name='input_optimizer')
for i in range(num_iter):
    this_loss,this_grad = opt_func([my_input])
    my_input +=this_grad