废话不多说啦，直接看代码吧！
tf.concat
t1 = [[1, 2, 3], [4, 5, 6]] t2 = [[7, 8, 9], [10, 11, 12]] tf.concat(0, [t1, t2]) ==> [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]] tf.concat(1, [t1, t2]) ==> [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]] # tensor t3 with shape [2, 3] # tensor t4 with shape [2, 3] tf.shape(tf.concat(0, [t3, t4])) ==> [4, 3] tf.shape(tf.concat(1, [t3, t4])) ==> [2, 6]
numpy.concatenate
a = np.array([[1, 2], [3, 4]]) b = np.array([[5, 6]]) np.concatenate((a, b), axis=0) array([[1, 2], [3, 4], [5, 6]]) np.concatenate((a, b.T), axis=1) array([[1, 2, 5], [3, 4, 6]])
以上这篇谈一谈数组拼接tf.concat()和np.concatenate()的区别就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。