在tensorflow中写了这样一句:
y_out = tf.matmul(outputs, W)
出现以下报错:
Shape must be rank 2 but is rank 3 for ‘MatMul’ (op: ‘MatMul’) with input shapes: [16,336,400], [400,1].
import numpy as np
A = np.array([[[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 0, 1, 2]],
[[4, 3, 2, 1],
[8, 7, 6, 5],
[2, 1, 0, 9]]])
print(A)
print(A.shape)
print('---------------------------')
B = np.array([[1], [2], [3], [4]])
print(B)
print(B.shape)
print('---------------------------')
C = np.matmul(A, B)
print(C)
print(C.shape)
[[[1 2 3 4]
[5 6 7 8]
[9 0 1 2]]
[[4 3 2 1]
[8 7 6 5]
[2 1 0 9]]]
import numpy as np
import tensorflow as tf
sess = tf.Session()
A = np.array([[[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 0, 1, 2]],
[[4, 3, 2, 1],
[8, 7, 6, 5],
[2, 1, 0, 9]]])
B = np.array([[1], [2], [3], [4]])
A = tf.cast(tf.convert_to_tensor(A), tf.int32) # shape=[2, 3, 4]
B = tf.cast(tf.convert_to_tensor(B), tf.int32) # shape=[4, 1]
#-----------------------------------------修改部分(开始)----------------------------------------- #要想让A和B进行tf.matmul操作,第一个维数必须一致。因此要把B先tile后转成[2, 4, 1]维 B_ = tf.tile(B, [2, 1])# B的第一维复制2倍,第二维复制1倍 B = tf.reshape(B_, [2, 4, 1]) # 或 更通用的改法: #B_ = tf.tile(B, [tf.shape(A)[0], 1]) #B = tf.reshape(B_, [tf.shape(A)[0], tf.shape(B)[0], tf.shape(B)[1]]) #-----------------------------------------修改部分(结束)----------------------------------------- #此时就可以matmul了 C = tf.matmul(A, B) print(‘C:’,C.get_shape().as_list()) sess.run(C)
输出结果:
('C:', [2, 3, 1])
array([[[30],
[70],
[20]],
[[20],
[60],
[40]]], dtype=int32)
转自博客:http://blog.csdn.net/blythe0107/article/details/74171870