evaluator.py

def eval_loss_and_grads(x):
    x = x.reshape((1,3,img_nrows,img_ncols))
    outs = f_outputs([x])
    loss_value = outs[0]
    if(len(outs[1:])==1):
        grad_values = outs[1].flatten().astype('float64')
    else:
        grad_values = np.array(outs[1:]).flatten().astype('float64')
    return loss_value,grad_values

class Evaluator(object):
    def __init__(self):
        self.loss_value = None
        self.grad_values = None

    def loss(self,x):
        assert self.loss_value is None
        loss_value, grad_values = eval_loss_and_grads(x)
        self.loss_value = loss_value
        self.grad_values = grad_values
        return self.loss_value

    def grads(self,x):
        assert self.loss_value is not None
        grad_values = np.copy(self.grad_values)
        self.loss_value = None
        self.grad_values = None
        return grad_values