Tensorflow：TypeError：“TensorShape”对象不可调用

Question

我有一个简单的占位符：

input_x = tf.placeholder(name='tensor_a',shape=[2,3,4],dtype=tf.int32)

我想获取形状索引并在变量中使用它作为参数，例如：

var_b = tf.get_variable('name_a',shape=[input_x.get_shape()[0],2],dtype=tf.float32,initializer=tf.random_uniform_initializer())

和

var_c = tf.get_variable('name_b',shape=[input_x.get_shape()[1],2],dtype=tf.float32,initializer=tf.random_uniform_initializer())

完整的程序：

import tensorflow as tf
import numpy as np

tf.reset_default_graph()

input_x = tf.placeholder(name='tensor_a',shape=[2,3,4],dtype=tf.int32)

var_b = tf.get_variable('name_a',shape=[input_x.get_shape()[0],2],dtype=tf.float32,initializer=tf.random_uniform_initializer())
var_c = tf.get_variable('name_b',shape=[input_x.get_shape()[1],2],dtype=tf.float32,initializer=tf.random_uniform_initializer())

sum_c=tf.add(var_b,var_c)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print(sess.run(sum_c,feed_dict={input_x:np.random.randint(0,10,[2,3,4])}))

但我收到此错误：

TypeError: 'TensorShape' object is not callable

我怎样才能做到这一点？

编辑：如果我试图重塑它，我会收到另一个错误：

import tensorflow as tf
import numpy as np

tf.reset_default_graph()

input_x = tf.placeholder(name='tensor_a',shape=[None,None],dtype=tf.int32)
print(input_x.get_shape())

var_b = tf.get_variable('name_a',shape=[4,4],dtype=tf.float32,initializer=tf.random_uniform_initializer())
var_c = tf.get_variable('name_b',shape=[4,4],dtype=tf.float32,initializer=tf.random_uniform_initializer())
vac_d= tf.get_variable('name_d',shape=[var_c.shape[0],60],dtype=tf.float32,initializer=tf.random_uniform_initializer())
reshape_ = tf.reshape(var_c,[input_x.shape[0],input_x.shape[1],-1])
print(reshape_)
sum_c=tf.add(var_b,var_c)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print(sess.run(sum_c,feed_dict={input_x:np.random.randint(0,10,[2,2])}))

错误：

TypeError: Failed to convert object of type <class 'list'> to Tensor. Contents: [Dimension(None), Dimension(None), -1]. Consider casting elements to a supported type.

Answer 1

改变

var_b = tf.get_variable('name_a',shape=[input_x.shape()[0],2],dtype=tf.float32,initializer=tf.random_uniform_initializer())
var_c = tf.get_variable('name_b',shape=[input_x.shape()[1],2],dtype=tf.float32,initializer=tf.random_uniform_initializer())

到

var_b = tf.get_variable('name_a',shape=[input_x.shape[0],2],dtype=tf.float32,initializer=tf.random_uniform_initializer())
var_c = tf.get_variable('name_b',shape=[input_x.shape[1],2],dtype=tf.float32,initializer=tf.random_uniform_initializer())