layers.py 文件源码

def get_output_for(self, input, **kwargs):
        return spatial_expected_softmax(input)#, self.temp)
        # max_ = tf.reduce_max(input, reduction_indices=[1, 2], keep_dims=True)
        # exp = tf.exp(input - max_) + 1e-5

        # vals = []
        #
        # for dim in [0, 1]:
        #     dim_val = input.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(input, [1, 2], keep_dims=True)
        #     e = tf.exp(input - m) + 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, input.get_shape()[-1].value * 2])

        # import ipdb; ipdb.set_trace()

        # input.get_shape()
        # exp / tf.reduce_sum(exp, reduction_indices=[1, 2], keep_dims=True)
        # import ipdb;
        # ipdb.set_trace()
        # spatial softmax?

        # for dim in range(2):
        #     val = obs.get_shape()[dim + 1].value
        #     lin = tf.linspace(-1.0, 1.0, val)
        #     lin = tf.expand_dims(lin, 1 - dim)
        #     lin = tf.expand_dims(lin, 0)
        #     lin = tf.expand_dims(lin, 3)
        #     m = tf.reduce_max(e, [1, 2], keep_dims=True)
        #     e = tf.exp(e - m) + 1e-3
        #     val = tf.reduce_sum(e * lin, [1, 2]) / (tf.reduce_sum(e, [1, 2]))