batch_acc = 0
val_acc = 0
val_loss = 0
# train
total = 0
train_acc = 0
train_loss = 0
# imgs,labs=data.next_batch(batch_size)
for i in range(60000 // batch_size):
imgs, labs = data.next_batch(batch_size)
imgs = imgs
#训练
sf_out = net.forward(imgs)
net.backward(sf_out - labs)
net.Gradient(alpha=learning_rate, weight_decay=0.01)
#统计
for j in range(batch_size):
if np.argmax(sf_out[j]) == np.argmax(labs[j]):
train_acc += 1
total += 1
mod = 100
if i % mod == 0:
print("epoch=%d batchs=%d train_acc: %.4f " %
(epoch, i, train_acc / total))
train_acc = 0
total = 0
