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0.1.105

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guanjihuan 2024-06-04 21:37:50 +08:00
parent 11950302de
commit e79efc284f
3 changed files with 36 additions and 8 deletions
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2
PyPI/setup.cfg
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@ -1,7 +1,7 @@
[metadata]
# replace with your username:
name = guan
version = 0.1.104
version = 0.1.105
author = guanjihuan
author_email = guanjihuan@163.com
description = An open source python package

2
PyPI/src/guan.egg-info/PKG-INFO
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@ -1,6 +1,6 @@
Metadata-Version: 2.1
Name: guan
Version: 0.1.104
Version: 0.1.105
Summary: An open source python package
Home-page: https://py.guanjihuan.com
Author: guanjihuan

40
PyPI/src/guan/machine_learning.py
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@ -165,8 +165,21 @@ def convolutional_neural_network_with_two_convolutional_layers_and_two_fully_con
model = model_class_of_convolutional_neural_network_with_two_convolutional_layers_and_two_fully_connected_layers()
return model
# 从损失函数的变化情况中获取是否停止训练的信号
def get_break_signal_from_loss_array(loss_array, patience=100, min_delta=0.001):
break_signal = 0
counter = 0
num = len(loss_array)
for i0 in range(num):
if i0 != 0:
if abs(loss_array[i0]-loss_array[i0-1])<min_delta:
counter += 1
if counter >= patience:
break_signal = 1
return break_signal
# 使用优化器训练模型
def train_model(model, x_data, y_data, optimizer='Adam', learning_rate=0.001, criterion='MSELoss', num_epochs=1000, print_show=1):
def train_model(model, x_data, y_data, optimizer='Adam', learning_rate=0.001, criterion='MSELoss', num_epochs=1000, print_show=1, early_stop=0, patience=100, min_delta=0.001):
import torch
if optimizer == 'Adam':
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
@ -177,21 +190,26 @@ def train_model(model, x_data, y_data, optimizer='Adam', learning_rate=0.001, cr
criterion = torch.nn.MSELoss()
elif criterion == 'CrossEntropyLoss':
criterion = torch.nn.CrossEntropyLoss()
losses = []
loss_array = []
for epoch in range(num_epochs):
output = model.forward(x_data)
loss = criterion(output, y_data)
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.append(loss.item())
loss_array.append(loss.item())
if print_show == 1:
if (epoch + 1) % 100 == 0:
print(epoch, loss.item())
return model, losses
if early_stop == 1:
import guan
break_signal = guan.get_break_signal_from_loss_array(loss_array, patience=patience, min_delta=min_delta)
if break_signal:
break
return model, loss_array
# 使用优化器批量训练模型
def batch_train_model(model, train_loader, optimizer='Adam', learning_rate=0.001, criterion='MSELoss', num_epochs=1000, print_show=1):
def batch_train_model(model, train_loader, optimizer='Adam', learning_rate=0.001, criterion='MSELoss', num_epochs=1000, print_show=1, more_loss_data=0, early_stop=0, patience=100, min_delta=0.001):
import torch
if optimizer == 'Adam':
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
@ -202,6 +220,7 @@ def batch_train_model(model, train_loader, optimizer='Adam', learning_rate=0.001
criterion = torch.nn.MSELoss()
elif criterion == 'CrossEntropyLoss':
criterion = torch.nn.CrossEntropyLoss()
loss_array = []
losses = []
for epoch in range(num_epochs):
for batch_x, batch_y in train_loader:
@ -211,10 +230,19 @@ def batch_train_model(model, train_loader, optimizer='Adam', learning_rate=0.001
loss.backward()
optimizer.step()
losses.append(loss.item())
loss_array.append(loss.item())
if print_show == 1:
if (epoch + 1) % 100 == 0:
print(epoch, loss.item())
return model, losses
if early_stop == 1:
import guan
break_signal = guan.get_break_signal_from_loss_array(loss_array, patience=patience, min_delta=min_delta)
if break_signal:
break
if more_loss_data == 0:
return model, loss_array
else:
return model, losses
# 保存模型参数到文件
def save_model_parameters(model, filename='./model_parameters.pth'):