layer.py 文件源码

def tf_apply(self, x, update):
        if self.name == 'elu':
            x = tf.nn.elu(features=x)

        elif self.name == 'none':
            x = tf.identity(input=x)

        elif self.name == 'relu':
            x = tf.nn.relu(features=x)
            if 'relu' in self.summary_labels:
                non_zero = tf.cast(x=tf.count_nonzero(input_tensor=x), dtype=tf.float32)
                size = tf.cast(x=tf.reduce_prod(input_tensor=tf.shape(input=x)), dtype=tf.float32)
                summary = tf.summary.scalar(name='relu', tensor=(non_zero / size))
                self.summaries.append(summary)

        elif self.name == 'selu':
            # https://arxiv.org/pdf/1706.02515.pdf
            alpha = 1.6732632423543772848170429916717
            scale = 1.0507009873554804934193349852946
            negative = alpha * tf.nn.elu(features=x)
            x = scale * tf.where(condition=(x >= 0.0), x=x, y=negative)

        elif self.name == 'sigmoid':
            x = tf.sigmoid(x=x)

        elif self.name == 'softmax':
            x = tf.nn.softmax(logits=x)

        elif self.name == 'softplus':
            x = tf.nn.softplus(features=x)

        elif self.name == 'tanh':
            x = tf.nn.tanh(x=x)

        else:
            raise TensorForceError('Invalid non-linearity: {}'.format(self.name))

        return x