定义一个模型
TensorLayerX 提供了两种定义模型的方式。 序列式模型可以让您顺畅的搭建一个模型，而动态模型则可以让您全面掌控模型前向传播过程。
序列式模型
from tensorlayerx.nn import SequentialLayer
from tensorlayerx.nn import Linear
import tensorlayerx as tlx
def get_model():
layer_list = []
layer_list.append(Linear(out_features=800, act=tlx.ReLU, in_features=784, name='Linear1'))
layer_list.append(Linear(out_features=800, act=tlx.ReLU, in_features=800, name='Linear2'))
layer_list.append(Linear(out_features=10, act=tlx.ReLU, in_features=800, name='Linear3'))
MLP = SequentialLayer(layer_list)
return MLP
动态模型
这种情况下，您需要手动将上一层的输出形状提供给新的层。
import tensorlayerx as tlx
from tensorlayerx.nn import Module
from tensorlayerx.nn import Dropout, Linear
class CustomModel(Module):
def __init__(self):
super(CustomModel, self).__init__()
self.dropout1 = Dropout(p=0.2)
self.linear1 = Linear(out_features=800, act=tlx.ReLU, in_features=784)
self.dropout2 = Dropout(p=0.2)
self.linear2 = Linear(out_features=800, act=tlx.ReLU, in_features=800)
self.dropout3 = Dropout(p=0.2)
self.linear3 = Linear(out_features=10, act=None, in_features=800)
def forward(self, x, foo=False):
z = self.dropout1(x)
z = self.linear1(z)
z = self.dropout2(z)
z = self.linear2(z)
z = self.dropout3(z)
out = self.linear3(z)
if foo:
out = tlx.softmax(out)
return out
MLP = CustomModel()
MLP.set_eval()
outputs = MLP(data, foo=True) # controls the forward here
outputs = MLP(data, foo=False)
动态模型不需要提供前一层的输出形状
这种情况下，您不需要手动将上一层的输出形状提供给新的层。
import tensorlayerx as tlx
from tensorlayerx.nn import Module
from tensorlayerx.nn import Dropout, Linear
class CustomModel(Module):
def __init__(self):
super(CustomModel, self).__init__()
self.dropout1 = Dropout(p=0.2)
self.linear1 = Linear(out_features=800, act=tlx.ReLU)
self.dropout2 = Dropout(p=0.2)
self.linear2 = Linear(out_features=800, act=tlx.ReLU)
self.dropout3 = Dropout(p=0.2)
self.linear3 = Linear(out_features=10, act=None)
def forward(self, x, foo=False):
z = self.dropout1(x)
z = self.linear1(z)
z = self.dropout2(z)
z = self.linear2(z)
z = self.dropout3(z)
out = self.linear3(z)
if foo:
out = tlx.softmax(out)
return out
MLP = CustomModel()
MLP.init_build(tlx.nn.Input(shape=(1, 784))) # init_build must be called to initialize the weights.
MLP.set_eval()
outputs = MLP(data, foo=True) # controls the forward here
outputs = MLP(data, foo=False)
切换训练/测试模式
# method 1: 在前向传播前切换
MLP.set_train() # enable dropout, batch norm moving avg ...
output = MLP(train_data)
... # training code here
Model.set_eval() # disable dropout, batch norm moving avg ...
output = MLP(test_data)
... # testing code here
# method 2: 使用封装的训练模块
model = tlx.model.Model(network=MLP, loss_fn=tlx.losses.softmax_cross_entropy_with_logits, optimizer=optimizer)
model.train(n_epoch=n_epoch, train_dataset=train_ds)
权重的复用
对于动态模型，只需要在前向传播函数中多次调用layer对象即可
import tensorlayerx as tlx
from tensorlayerx.nn import Module, Linear, Concat
class MyModel(Module):
def __init__(self):
super(MyModel, self).__init__()
self.linear_shared = Linear(out_features=800, act=tlx.ReLU, in_features=784)
self.linear1 = Linear(out_features=10, act=tlx.ReLU, in_features=800)
self.linear2 = Linear(out_features=10, act=tlx.ReLU, in_features=800)
self.cat = Concat()
def forward(self, x):
x1 = self.linear_shared(x) # call dense_shared twice
x2 = self.linear_shared(x)
x1 = self.linear1(x1)
x2 = self.linear2(x2)
out = self.cat([x1, x2])
return out
model = MyModel()
模型打印相关函数
print(MLP) # 只需要简单的调用print函数
# Model(
# (_inputlayer): Input(shape=[None, 784], name='_inputlayer')
# (dropout): Dropout(p=0.8, name='dropout')
# (linear): Linear(out_features=800, relu, in_features='784', name='linear')
# (dropout_1): Dropout(p=0.8, name='dropout_1')
# (linear_1): Linear(out_features=800, relu, in_features='800', name='linear_1')
# (dropout_2): Dropout(p=0.8, name='dropout_2')
# (linear_2): Linear(out_features=10, None, in_features='800', name='linear_2')
# )
访问指定权重
We can get the specific weights by indexing or naming.
# indexing
all_weights = MLP.all_weights
some_weights = MLP.all_weights[1:3]
保存和恢复模型
我们提供了两种模型保存和恢复的方式
仅保存权重参数
MLP.save_weights('./model_weights.npz') # by default, file will be in hdf5 format
MLP.load_weights('./model_weights.npz')
保存模型权重
# 在使用封装的训练模块时，可用如下方式保存和加载模型。
model = tlx.model.Model(network=MLP, loss_fn=tlx.losses.softmax_cross_entropy_with_logits, optimizer=optimizer)
model.train(n_epoch=n_epoch, train_dataset=train_ds)
model.save_weights('./model.npz', format='npz_dict')
model.load_weights('./model.npz', format='npz_dict')
