概述
在使用keras中的keras.backend.batch_dot和tf.matmul实现功能其实是一样的智能矩阵乘法，比如A,B,C,D,E,F,G,H，I,J,K,L都是二维矩阵，中间点表示矩阵乘法，AG 表示矩阵A 和G 矩阵乘法（A 的列维度等于G 行维度），WX=Z
import keras.backend as K import tensorflow as tf import numpy as np w = K.variable(np.random.randint(10,size=(10,12,4,5))) k = K.variable(np.random.randint(10,size=(10,12,5,8))) z = K.batch_dot(w,k) print(z.shape) #(10, 12, 4, 8)
import keras.backend as K import tensorflow as tf import numpy as np w = tf.Variable(np.random.randint(10,size=(10,12,4,5)),dtype=tf.float32) k = tf.Variable(np.random.randint(10,size=(10,12,5,8)),dtype=tf.float32) z = tf.matmul(w,k) print(z.shape) #(10, 12, 4, 8)
示例
from keras import backend as K a = K.ones((3,4,5,2)) b = K.ones((2,5,3,7)) c = K.dot(a, b) print(c.shape)
会输出：
ValueError: Dimensions must be equal, but are 2 and 3 for ‘MatMul' (op: ‘MatMul') with input shapes: [60,2], [3,70].
from keras import backend as K a = K.ones((3,4)) b = K.ones((4,5)) c = K.dot(a, b) print(c.shape)#(3,5)
或者
import tensorflow as tf a = tf.ones((3,4)) b = tf.ones((4,5)) c = tf.matmul(a, b) print(c.shape)#(3,5)
如果增加维度：
from keras import backend as K a = K.ones((2,3,4)) b = K.ones((7,4,5)) c = K.dot(a, b) print(c.shape)#(2, 3, 7, 5)
这个矩阵乘法会沿着两个矩阵最后两个维度进行乘法，不是element-wise矩阵乘法
from keras import backend as K a = K.ones((1, 2, 3 , 4)) b = K.ones((8, 7, 4, 5)) c = K.dot(a, b) print(c.shape)#(1, 2, 3, 8, 7, 5)
keras的dot方法是Theano中的复制
from keras import backend as K a = K.ones((1, 2, 4)) b = K.ones((8, 7, 4, 5)) c = K.dot(a, b) print(c.shape)# (1, 2, 8, 7, 5).
from keras import backend as K a = K.ones((9, 8, 7, 4, 2)) b = K.ones((9, 8, 7, 2, 5)) c = K.batch_dot(a, b) print(c.shape) #(9, 8, 7, 4, 5)
或者
import tensorflow as tf a = tf.ones((9, 8, 7, 4, 2)) b = tf.ones((9, 8, 7, 2, 5)) c = tf.matmul(a, b) print(c.shape) #(9, 8, 7, 4, 5)
以上这篇浅谈keras中的batch_dot,dot方法和TensorFlow的matmul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。