谈一谈数组拼接tf.concat()和np.concatenate()的区别

concat

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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(

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谈一谈数组拼接谈一谈数组拼接tf.concat()和和np.concatenate()的区别的区别

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]])