确实，我想在训练的不同阶段更改学习率。类似于这样：

for i in range(iter_num):    learn_rate = i*alpha    do_training(learn_rate,...)

显然，每次迭代重新编译一个新函数会太慢。所以我想知道在Theano中是否有更好的方法来实现这一点？谢谢！

回答：

你可以将学习率设为符号变量，并像这样传递给训练函数：

import numpyimport theanoimport theano.tensor as ttdef compile(input_size, hidden_size, output_size):    W_h = theano.shared(numpy.random.standard_normal(size=(input_size, hidden_size)).astype(theano.config.floatX))    b_h = theano.shared(numpy.zeros((hidden_size,), dtype=theano.config.floatX))    W_y = theano.shared(numpy.random.standard_normal(size=(hidden_size, output_size)).astype(theano.config.floatX))    b_y = theano.shared(numpy.zeros((output_size,), dtype=theano.config.floatX))    x = tt.matrix('x')    z = tt.ivector('z')    learning_rate = tt.scalar()    h = tt.tanh(theano.dot(x, W_h) + b_h)    y = tt.nnet.softmax(theano.dot(h, W_y) + b_y)    cost = tt.nnet.categorical_crossentropy(y, z).mean()    updates = [(p, p - learning_rate * tt.grad(cost, p)) for p in (W_h, b_h, W_y, b_y)]    return theano.function([x, z, learning_rate], outputs=cost, updates=updates)def main():    input_size = 5    hidden_size = 4    output_size = 3    train = compile(input_size, hidden_size, output_size)    print train([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]], [1, 2], 0.1)main()

请注意，现在训练函数有三个参数；第三个是学习率。