TF/keras子类在急切的执行中工作得很好，如果没有它，会抛出大量无法追踪的错误？

class modrelu(Layer): def __init__(self, **kwargs): super(modrelu, self).__init__(**kwargs) def build(self, input_shape): print(input_shape) self.b = self.add_weight(name='brad', shape=(input_shape[0][1],), initializer='uniform', trainable=True) super(modrelu, self).build(input_shape) # Be sure to call this at the end def call(self, x): assert isinstance(x, list) ip_r, ip_i = x comp= tf.complex(ip_r, ip_i) ABS= tf.math.abs(comp) ANG= tf.math.angle(comp) ABS= tf.nn.relu( self.b + ABS) op_i= ABS * tf.sin(ANG) #K.dot ?? op_r= ABS * tf.cos(ANG) return [op_r, op_i] def compute_output_shape(self, input_shape): assert isinstance(input_shape, list) shape_a, shape_b = input_shape return [shape_a, shape_b] act= modrelu() a=tf.constant(np.array([[1,2], [4,4]]), dtype='float32') b=tf.constant(np.array([[3,4], [5, -1]]), dtype='float32') act([a,b])

1条回答

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1楼 · 发布于 2024-04-20 12:24:14

您将获得作为TensorShape实例的形状。如果执行不急，可以使用as_list()属性将其转换为Python列表：

if tf.executing_eagerly():
    shape = (input_shape[0][1], )
else:
    shape = (input_shape[0].as_list()[1], )

在您的代码中将是这样的：

import tensorflow as tf
from tensorflow.keras.layers import Layer
import numpy as np

class modrelu(Layer):
    def __init__(self, **kwargs):
        super(modrelu, self).__init__(**kwargs)

    def build(self, input_shape):
        if tf.executing_eagerly():
            shape = (input_shape[0][1])
        else:
            shape = (input_shape[0].as_list()[1], )
        self.b = self.add_weight(name='brad',
                                 shape=shape,
                                 initializer='uniform',
                                 trainable=True)
        super(modrelu, self).build(input_shape)  # Be sure to call this at the end

    def call(self, x):
        assert isinstance(x, list)
        ip_r, ip_i = x
        comp = tf.complex(ip_r, ip_i) 
        ABS = tf.math.abs(comp)
        ANG = tf.math.angle(comp)
        ABS = tf.nn.relu( self.b + ABS) 
        op_i =  ABS * tf.sin(ANG) #K.dot ??
        op_r = ABS * tf.cos(ANG)
        return [op_r, op_i]

    def compute_output_shape(self, input_shape):
        assert isinstance(input_shape, list)
        shape_a, shape_b = input_shape
        return [shape_a, shape_b]

act = modrelu()
a = tf.constant(np.array([[1,2], [4,4]]), dtype='float32')
b = tf.constant(np.array([[3,4], [5, -1]]), dtype='float32')
act([a,b])
# [<tf.Tensor 'modrelu_6/mul_1:0' shape=(2, 2) dtype=float32>,
#  <tf.Tensor 'modrelu_6/mul:0' shape=(2, 2) dtype=float32>]

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