You could go further; if you can assure that iand(c_loc(array),7) == 0 for all arrays, you can put them all in one loop, and set !dir$ vector nontemporal !dir$ vector aligned do ...for that loop. In such a case, you could expect to gain performance by zer...
通过打平后的vector乘以权重矩阵W_fc1,再加上bias b_fc1,最后应用ReLU激活函数后就能实现: # hidden layers h_conv2_flat = tf.reshape(h_conv2, [-1, 25 * 25 * 20]) h_fc1 = tf.nn.relu(tf.matmul(h_conv2_flat, W_fc1) + b_fc1) # tf.matmul(a, b) 将矩阵a乘于矩阵b 2.3.2 ...
You could go further; if you can assure that iand(c_loc(array),7) == 0 for all arrays, you can put them all in one loop, and set !dir$ vector nontemporal !dir$ vector aligned do ...for that loop. In such a case, you could expect to gain performance by ze...
You could go further; if you can assure that iand(c_loc(array),7) == 0 for all arrays, you can put them all in one loop, and set !dir$ vector nontemporal !dir$ vector aligned do ...for that loop. In such a case, you could expect to gain perform...