def main():
data_path = args.data_path
train_df = pd.read_csv(os.path.join(data_path, 'train.csv'))
test_df = pd.read_csv(os.path.join(data_path, 'test.csv'))
train_df['type'] = 1
test_df['type'] = 0
df = pd.concat([train_df, test_df], ignore_index=True)
df, field_dims = preprocessing(df)
train_df = df[df['type'] == 1]
test_df = df[df['type'] == 0]
x_train = train_df[[col for col in train_df.columns if col not in ['Survived', 'type']]].values
y_train = train_df[['Survived']].values.ravel()
x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.2, random_state=1024)
x_test = test_df.drop(['Survived', 'type'], axis=1).values
print("x_train.shape =", x_train.shape)
print("x_val.shape =", x_val.shape)
print("x_test.shape =", x_test.shape)
print("y_train.shape =", y_train.shape)
print("y_val.shape =", y_val.shape)
X_train_tensor = torch.tensor(x_train).long()
y_train_tensor = torch.tensor(y_train).float()
X_val_tensor = torch.tensor(x_val).long()
y_val_tensor = torch.tensor(y_val).float()
X_test_tensor = torch.tensor(x_test).long()
dl_train = DataLoader(TensorDataset(X_train_tensor, y_train_tensor), shuffle=True, batch_size=8)
dl_val = DataLoader(TensorDataset(X_val_tensor, y_val_tensor), shuffle=True, batch_size=8)
dl_test = DataLoader(TensorDataset(X_test_tensor))
device = torch.device(args.device)
# fm = FactorizationMachineModel(field_dims=field_dims, embed_dim=16)
fm = DeepFMModel(field_dims=field_dims, embed_dim=16)
summary(fm, input_shape=(15,), input_dtype=torch.long)
criterion = nn.BCELoss()
optimizer = torch.optim.Adam(params=fm.parameters(), lr=0.001, weight_decay=1e-6)
writer = SummaryWriter('../data/tensorboard/')
for e in range(args.epochs):
train(fm, dl_train, device, criterion, optimizer)
train_auc_score = test(fm, dl_train, device)
val_auc_score = test(fm, dl_val, device)
writer.add_scalars('AUC', {'Train': train_auc_score,
'Val': val_auc_score}, e)
print(f'\nepoch {e}\t train auc: {round(train_auc_score, 4)}\tvalidation auc: {round(val_auc_score, 4)}')
writer.add_graph(fm, input_to_model=torch.rand(1, 15).long())
writer.flush()
writer.close()
torch.tensor(x_valid).float(),
torch.tensor(y_valid).float()),
shuffle=False,
batch_size=batch_size)
# 测试数据管道
for features, labels in dl_train:
print(features, labels)
break
for features, labels in dl_valid:
print(features, labels)
break
net = Model(n_feature=n_feature, n_hidden=n_hidden, n_output=n_output)
print(net)
summary(net, input_shape=(n_feature, ))
# 训练模型
# 1、定义损失函数、优化函数、评估指标
from sklearn.metrics import accuracy_score
loss_func = nn.BCELoss()
optimizer = torch.optim.Adam(params=net.parameters(), lr=lr)
metric_func = lambda y_pred, y_true: accuracy_score(
y_true.data.numpy(),
y_pred.data.numpy() > 0.5)
metric_name = "accuracy"
# 2、训练模型
dfhistory = pd.DataFrame(columns=[
"epoch", "loss", metric_name, "val_loss", "val_" + metric_name
net.add_module("linear1", nn.Linear(15, 20))
net.add_module("relu1", nn.ReLU())
net.add_module("linear2", nn.Linear(20, 15))
net.add_module("relu2", nn.ReLU())
net.add_module("linear3", nn.Linear(15, 1))
net.add_module("sigmoid", nn.Sigmoid())
return net
net = create_net()
print(net)
#%% 两种方法summary数据
# 1
from torchkeras import summary
summary(net, input_shape=(15,))
# 2
# from torchsummary import summary
# summary(net, input_size=(15,))
#%% 训练模型
from sklearn.metrics import accuracy_score
loss_func = nn.BCELoss()
optimizer = torch.optim.Adam(params=net.parameters(), lr=0.01)
metric_func = lambda y_pred, y_true: accuracy_score(y_true.data.numpy(), y_pred.data.numpy() > 0.5)
metric_name = "accuracy"
#%% 脚本循环
import datetime
# @File : test_net.py
import torch
from torch import nn
from torchkeras import summary
from thop import profile
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
y = self.relu(x)
return y
if __name__ == '__main__':
model = Net()
print(model)
print(summary(model, input_shape=(3, 20, 20)))
print('number of params:', sum(param.numel() for param in model.parameters()))
inputs = torch.randn(8, 3, 20, 20)
flops, params = profile(model, (inputs,))
print('flops:', flops, 'params:', params)
traindata=True,
shuffle=True)
val_dloader = load_data(data_base_dir + 'cnews.val.txt',
traindata=False,
shuffle=False)
print('*' * 27, '%d 个 step:' % len(train_dloader)) # 1000 个step/batch
sample_batch = next(iter(train_dloader))
print('*' * 27, 'sample_batch:', len(sample_batch), sample_batch[0].size(),
sample_batch[0].dtype, sample_batch[1].size(),
sample_batch[1].dtype) # 4 [b, doc_maxlen] int64
model = DPCNN(num_classes, net_depth=net_depth, max_seqlen=doc_maxlen)
torchkeras.summary(model,
input_shape=(doc_maxlen, ),
input_dtype=torch.int64)
model = model.to(device)
if ngpu > 1:
model = torch.nn.DataParallel(model, device_ids=list(
range(ngpu))) # 设置并行执行 device_ids=[0,1,2,3]
model.eval()
sample_out = model(sample_batch[0])
print('*' * 10, 'sample_out:', sample_out.shape) # [b, 10]
optimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=1e-4)
scheduler_1r = torch.optim.lr_scheduler.LambdaLR(
optimizer, lr_lambda=lambda epoch: 0.1 if epoch > EPOCHS * 0.8 else 1)
train_model(
x = self.dropout(x)
x = self.adaptive_pool(x)
x = self.flatten(x)
x = self.linear1(x)
x = self.relu(x)
x = self.linear2(x)
y = self.sigmoid(x)
return y
net = Net()
print(net)
import torchkeras
print(torchkeras.summary(net, input_shape=(3, 28, 28)))
import pandas as pd
from sklearn.metrics import roc_auc_score
model = net
model.optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
model.loss_func = nn.BCELoss()
model.metric_func = lambda y_pred, y_true: roc_auc_score(
y_true.data.numpy(), y_pred.data.numpy())
model_metric_name = "auc"
def train_step(model, features, labels):
labels_tensor = trans_labels(labels, model.out_n)
model.train()
# 使用nn.ModuleList作为模型容器：
# 注意：下面中的ModuleList不能使用Python中的列表代替：
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.layers = nn.ModuleList([
nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Dropout2d(p=0.1),
nn.AdaptiveMaxPool2d((1, 1)),
nn.Flatten(),
nn.Linear(64, 32),
nn.ReLU(),
nn.Linear(32, 1),
nn.Sigmoid()
])
def forward(self, x):
for layer in self.layers:
# print(layer) # layer是layers中的各行
x = layer(x)
return x
net = Net()
print(net)
summary(net, input_shape=(3, 32, 32))
x = self.adaptive_pool(x)
x = self.flatten(x)
x = self.linear1(x)
x = self.relu(x)
x = self.linear2(x)
y = self.sigmoid(x)
return y
net = Net()
# print(net)
#%%
import torchkeras
torchkeras.summary(net, input_shape=(3, 32, 32))
#%% 训练模型，函数形式循环
import pandas as pd
from sklearn.metrics import roc_auc_score
model = net
model.optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
model.loss_func = torch.nn.BCELoss()
model.metric_func = lambda y_pred, y_true: roc_auc_score(
y_true.data.numpy(), y_pred.data.numpy())
model.metric_name = "auc"
def train_step(model, features, labels):
"""
def __init__(self):
super(Resnet18,self).__init__()
self.resnet18 = resnet18
self.linear1 = nn.Linear(1000,128)
self.relu = nn.ReLU()
self.linear2 = nn.Linear(128,1)
self.sigmoid = nn.Sigmoid()
def forward(self,x):
x = self.resnet18(x)
x = self.linear1(x)
x = self.relu(x)
x = self.linear2(x)
y = self.sigmoid(x)
return y
'''
net = Resnet18()
summary(net,input_shape= (3,30,30))
print(net)
while 1:
pass
'''
class LetNet(nn.Module):
def __init__(self):
super(LetNet,self).__init__()
self.conv1 = nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3)
self.pool1 = nn.MaxPool2d(kernel_size = 2,stride = 2)
self.conv2 = nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5)
self.pool2 = nn.MaxPool2d(kernel_size = 2,stride = 2)
self.dropout = nn.Dropout2d(p = 0.1)