convnet.py 文件源码

def __init__(self, rng, input, image_shape, filter_shape, poolsize=(2, 2)):
        # ???????????????????
        assert image_shape[1] == filter_shape[1]

        fan_in = np.prod(filter_shape[1:])
        fan_out = filter_shape[0] * np.prod(filter_shape[2:]) / np.prod(poolsize)

        W_bound = np.sqrt(6.0 / (fan_in + fan_out))
        self.W = theano.shared(
            np.asarray(rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
                       dtype=theano.config.floatX),  # @UndefinedVariable
            borrow=True)

        b_values = np.zeros((filter_shape[0],), dtype=theano.config.floatX)  # @UndefinedVariable
        self.b = theano.shared(value=b_values, borrow=T)

        # ??????????????????
        conv_out = conv.conv2d(
            input=input,
            filters=self.W,
            filter_shape=filter_shape,
            image_shape=image_shape)

        # Max-pooling????????????????????
        pooled_out = downsample.max_pool_2d(
            input=conv_out,
            ds=poolsize,
            ignore_border=True)

        # ????????
        self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))

        self.params = [self.W, self.b]