modules_to_save=modules_to_save, bias="none", task_type="CAUSAL_LM", ) model = get_peft_model(model, config) model.print_trainable_parameters() # Be more transparent about the % of trainable params. print(model.get_nb_trainable_parameters()) print(model.num_parameters(only_trainable=...
"""cnn参数量统计, 使用方式cnn_paras_count(net)""" # Find total parameters and trainable parameters total_params = sum(p.numel() for p in net.parameters()) print(f'{total_params:,} total parameters.') total_trainable_params = sum(p.numel() for p in net.parameters() if p.requires_...
optim = Momentum(params=network.trainable_params(), learning_rate=learning_rate, momentum=0.9) model = Model(network, loss_fn=loss, optimizer=optim, metrics={'accuracy'}) print("---训练") model.train(Epoch,train_dataset,callbacks=[LossMonitor(10)]) 报错信息 Traceback (most recent ...
net_opt = nn.Momentum(network.trainable_params(), lr, momentum) # define the loss function net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') # define the model model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) steps_loss, steps_eval = ...
trainable params: 1,474,560 || all params: 560,689,152 || trainable%: 0.26299064191632515 PEFT 中 Prefix Tuning 相关的代码是基于清华开源的P-tuning-v2 进行的重构;同时,我们可以在chatglm-6b和chatglm2-6b中看到类似的代码。PEFT 中源码如下所示。 # 从上面的源码也可以看到 Prefix Tuning 与 P-...
opt = nn.Adam(params=net.trainable_params())model = Model(network=net, loss_fn=nn.MAELoss(), optimizer=opt, metrics={"mae"})model.train(50, train_dataset) 调试时发现,运行到model.train(epoch=50,train_dataset)时报错如下: Traceback (most recent call last): File "D:\VRLA1\first\...
optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9) model = Model(net, loss_fn=loss, optimizer=optim) model.train(epoch_size, dataset) 其中关于Model()的参数的定义,官方文档给出的参考如下所示: classmindspore.Model(network, loss_fn=None, optimizer=None, metrics...
Trainable params: 890,410 Non-trainable params: 0 ___ (1623, 635, 4) 从最后的结果上来看,model消耗的时间更少,而且正确率更高、loss更低。那么以后我优先建立model模型! 10000/10000 [===] - 2s 187us/step Test loss: 0.9320432889938355 Test accuracy: 0.682 10000...
Totalparams:93,656 Trainableparams:93,656 Non-trainableparams:0 计算: #conv1 model.add(Conv2D(filters=24,kernel_size=(4,4),input_shape=(27,27,3),activation='relu')) Layer (type)OutputShapeParam# === conv2d (Conv2D) (None,24,24,24)1176 1176=(4[kernel_size]×4[...
Non-trainable params: 0 ___ 根据打印结果,一共有5569个参数,这里面涉及到参数数量的计算方法,熟悉神经网络结构的前提下,我们可以飞快的计算出,10*128+128+128*32+32+32*1+1=5569。 其中,我们在隐层使用了sigmoid函数作为激活函数,第一层有128个神经元,第二层有32...