layers.py 文件源码

def spatial_expected_softmax(x, temp=1):
    assert len(x.get_shape()) == 4
    vals = []
    for dim in [0, 1]:
        dim_val = x.get_shape()[dim + 1].value
        lin = tf.linspace(-1.0, 1.0, dim_val)
        lin = tf.expand_dims(lin, 1 - dim)
        lin = tf.expand_dims(lin, 0)
        lin = tf.expand_dims(lin, 3)
        m = tf.reduce_max(x, [1, 2], keep_dims=True)
        e = tf.exp((x - m) / temp) + 1e-5
        val = tf.reduce_sum(e * lin, [1, 2]) / (tf.reduce_sum(e, [1, 2]))
        vals.append(tf.expand_dims(val, 2))

    return tf.reshape(tf.concat(2, vals), [-1, x.get_shape()[-1].value * 2])