神经网络 – 在反向传播期间,我可以(有选择地)反转Theano梯度吗?

我热衷于利用Lasagne / Theano框架中最近的论文“ Unsupervised Domain Adaptation by Backpropagation”中提出的架构.

关于这篇论文的一点是它有点不寻常的是它包含了一个“梯度反转层”,它在反向传播过程中反转了梯度:

enter image description here

(图像底部的箭头是反向旋转的反向传播).

在论文中,作者声称该方法“可以使用任何深度学习包实现”,实际上它们提供了version made in caffe.

但是,出于各种原因,我正在使用Lasagne / Theano框架.

在Lasagne / Theano中可以创建这样的梯度反转层吗?我还没有看到任何可以将自定义标量变换应用于这样的渐变的示例.如果是这样,我可以通过在烤宽面条中创建自定义图层来实现吗?

这是使用普通Theano的草图实现.这可以很容易地整合到烤宽面条中.

您需要创建一个自定义操作,该操作在前向传递中充当身份操作,但在反向传递中反转渐变.

以下是对如何实施这一建议的建议.它没有经过测试,我不是100%确定我已经正确理解了所有内容,但您可以根据需要进行验证和修复.

class ReverseGradient(theano.gof.Op):
    view_map = {0: [0]}

    __props__ = ('hp_lambda',)

    def __init__(self, hp_lambda):
        super(ReverseGradient, self).__init__()
        self.hp_lambda = hp_lambda

    def make_node(self, x):
        return theano.gof.graph.Apply(self, [x], [x.type.make_variable()])

    def perform(self, node, inputs, output_storage):
        xin, = inputs
        xout, = output_storage
        xout[0] = xin

    def grad(self, input, output_gradients):
        return [-self.hp_lambda * output_gradients[0]]

使用纸质符号和命名约定,这里是他们提出的完整通用模型的简单Theano实现.

import numpy
import theano
import theano.tensor as tt


def g_f(z, theta_f):
    for w_f, b_f in theta_f:
        z = tt.tanh(theano.dot(z, w_f) + b_f)
    return z


def g_y(z, theta_y):
    for w_y, b_y in theta_y[:-1]:
        z = tt.tanh(theano.dot(z, w_y) + b_y)
    w_y, b_y = theta_y[-1]
    z = tt.nnet.softmax(theano.dot(z, w_y) + b_y)
    return z


def g_d(z, theta_d):
    for w_d, b_d in theta_d[:-1]:
        z = tt.tanh(theano.dot(z, w_d) + b_d)
    w_d, b_d = theta_d[-1]
    z = tt.nnet.sigmoid(theano.dot(z, w_d) + b_d)
    return z


def l_y(z, y):
    return tt.nnet.categorical_crossentropy(z, y).mean()


def l_d(z, d):
    return tt.nnet.binary_crossentropy(z, d).mean()


def mlp_parameters(input_size, layer_sizes):
    parameters = []
    previous_size = input_size
    for layer_size in layer_sizes:
        parameters.append((theano.shared(numpy.random.randn(previous_size, layer_size).astype(theano.config.floatX)),
                           theano.shared(numpy.zeros(layer_size, dtype=theano.config.floatX))))
        previous_size = layer_size
    return parameters, previous_size


def compile(input_size, f_layer_sizes, y_layer_sizes, d_layer_sizes, hp_lambda, hp_mu):
    r = ReverseGradient(hp_lambda)

    theta_f, f_size = mlp_parameters(input_size, f_layer_sizes)
    theta_y, _ = mlp_parameters(f_size, y_layer_sizes)
    theta_d, _ = mlp_parameters(f_size, d_layer_sizes)

    xs = tt.matrix('xs')
    xs.tag.test_value = numpy.random.randn(9, input_size).astype(theano.config.floatX)
    xt = tt.matrix('xt')
    xt.tag.test_value = numpy.random.randn(10, input_size).astype(theano.config.floatX)
    ys = tt.ivector('ys')
    ys.tag.test_value = numpy.random.randint(y_layer_sizes[-1], size=9).astype(numpy.int32)

    fs = g_f(xs, theta_f)
    e = l_y(g_y(fs, theta_y), ys) + l_d(g_d(r(fs), theta_d), 0) + l_d(g_d(r(g_f(xt, theta_f)), theta_d), 1)

    updates = [(p, p - hp_mu * theano.grad(e, p)) for theta in theta_f + theta_y + theta_d for p in theta]
    train = theano.function([xs, xt, ys], outputs=e, updates=updates)

    return train


def main():
    theano.config.compute_test_value = 'raise'
    numpy.random.seed(1)
    compile(input_size=2, f_layer_sizes=[3, 4], y_layer_sizes=[7, 8], d_layer_sizes=[5, 6], hp_lambda=.5, hp_mu=.01)


main()

这是未经测试但以下可能允许此自定义操作用作烤宽面条层:

class ReverseGradientLayer(lasagne.layers.Layer):
    def __init__(self, incoming, hp_lambda, **kwargs):
        super(ReverseGradientLayer, self).__init__(incoming, **kwargs)
        self.op = ReverseGradient(hp_lambda)

    def get_output_for(self, input, **kwargs):
        return self.op(input)
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