From ddd2234689f4f158b4191a13a1ab34ef6d928fec Mon Sep 17 00:00:00 2001 From: guanjihuan Date: Wed, 12 Jun 2024 00:55:40 +0800 Subject: [PATCH] =?UTF-8?q?0.1.110-=E6=98=BE=E5=BC=8F=E4=BC=A0=E9=80=92?= =?UTF-8?q?=E5=8F=82=E6=95=B0=E8=87=B3=E5=B1=80=E5=9F=9F=E5=8F=98=E9=87=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- PyPI/setup.cfg | 2 +- PyPI/src/guan.egg-info/PKG-INFO | 2 +- PyPI/src/guan/machine_learning.py | 101 ++++++++++++++++-------------- 3 files changed, 56 insertions(+), 49 deletions(-) diff --git a/PyPI/setup.cfg b/PyPI/setup.cfg index f2483d3..544146a 100644 --- a/PyPI/setup.cfg +++ b/PyPI/setup.cfg @@ -1,7 +1,7 @@ [metadata] # replace with your username: name = guan -version = 0.1.109 +version = 0.1.110 author = guanjihuan author_email = guanjihuan@163.com description = An open source python package diff --git a/PyPI/src/guan.egg-info/PKG-INFO b/PyPI/src/guan.egg-info/PKG-INFO index aafa889..40d2792 100644 --- a/PyPI/src/guan.egg-info/PKG-INFO +++ b/PyPI/src/guan.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.1 Name: guan -Version: 0.1.109 +Version: 0.1.110 Summary: An open source python package Home-page: https://py.guanjihuan.com Author: guanjihuan diff --git a/PyPI/src/guan/machine_learning.py b/PyPI/src/guan/machine_learning.py index b70c380..13a3347 100644 --- a/PyPI/src/guan/machine_learning.py +++ b/PyPI/src/guan/machine_learning.py @@ -5,28 +5,29 @@ def fully_connected_neural_network_with_one_hidden_layer(input_size=1, hidden_si import torch global model_class_of_fully_connected_neural_network_with_one_hidden_layer class model_class_of_fully_connected_neural_network_with_one_hidden_layer(torch.nn.Module): - def __init__(self): + def __init__(self, input_size, hidden_size, output_size, activation): super().__init__() self.hidden_layer = torch.nn.Linear(input_size, hidden_size) self.output_layer = torch.nn.Linear(hidden_size, output_size) + self.activation = activation def forward(self, x): - if activation == 'relu': + if self.activation == 'relu': hidden_output = torch.nn.functional.relu(self.hidden_layer(x)) - elif activation == 'leaky_relu': + elif self.activation == 'leaky_relu': hidden_output = torch.nn.functional.leaky_relu(self.hidden_layer(x)) - elif activation == 'sigmoid': + elif self.activation == 'sigmoid': hidden_output = torch.nn.functional.sigmoid(self.hidden_layer(x)) - elif activation == 'tanh': + elif self.activation == 'tanh': hidden_output = torch.nn.functional.tanh(self.hidden_layer(x)) - elif activation == 'gelu': + elif self.activation == 'gelu': hidden_output = torch.nn.functional.gelu(self.hidden_layer(x)) - elif activation == 'silu': + elif self.activation == 'silu': hidden_output = torch.nn.functional.silu(self.hidden_layer(x)) else: hidden_output = self.hidden_layer(x) output = self.output_layer(hidden_output) return output - model = model_class_of_fully_connected_neural_network_with_one_hidden_layer() + model = model_class_of_fully_connected_neural_network_with_one_hidden_layer(input_size, hidden_size, output_size, activation) return model # 全连接神经网络模型（包含两个隐藏层）（模型的类定义成全局的） @@ -34,45 +35,47 @@ def fully_connected_neural_network_with_two_hidden_layers(input_size=1, hidden_s import torch global model_class_of_fully_connected_neural_network_with_two_hidden_layers class model_class_of_fully_connected_neural_network_with_two_hidden_layers(torch.nn.Module): - def __init__(self): + def __init__(self, input_size, hidden_size_1, hidden_size_2, output_size, activation_1, activation_2): super().__init__() self.hidden_layer_1 = torch.nn.Linear(input_size, hidden_size_1) self.hidden_layer_2 = torch.nn.Linear(hidden_size_1, hidden_size_2) self.output_layer = torch.nn.Linear(hidden_size_2, output_size) + self.activation_1 = activation_1 + self.activation_2 = activation_2 def forward(self, x): - if activation_1 == 'relu': + if self.activation_1 == 'relu': hidden_output_1 = torch.nn.functional.relu(self.hidden_layer_1(x)) - elif activation_1 == 'leaky_relu': + elif self.activation_1 == 'leaky_relu': hidden_output_1 = torch.nn.functional.leaky_relu(self.hidden_layer_1(x)) - elif activation_1 == 'sigmoid': + elif self.activation_1 == 'sigmoid': hidden_output_1 = torch.nn.functional.sigmoid(self.hidden_layer_1(x)) - elif activation_1 == 'tanh': + elif self.activation_1 == 'tanh': hidden_output_1 = torch.nn.functional.tanh(self.hidden_layer_1(x)) - elif activation_1 == 'gelu': + elif self.activation_1 == 'gelu': hidden_output_1 = torch.nn.functional.gelu(self.hidden_layer_1(x)) - elif activation_1 == 'silu': + elif self.activation_1 == 'silu': hidden_output_1 = torch.nn.functional.silu(self.hidden_layer_1(x)) else: hidden_output_1 = self.hidden_layer_1(x) - if activation_2 == 'relu': + if self.activation_2 == 'relu': hidden_output_2 = torch.nn.functional.relu(self.hidden_layer_2(hidden_output_1)) - elif activation_2 == 'leaky_relu': + elif self.activation_2 == 'leaky_relu': hidden_output_2 = torch.nn.functional.leaky_relu(self.hidden_layer_2(hidden_output_1)) - elif activation_2 == 'sigmoid': + elif self.activation_2 == 'sigmoid': hidden_output_2 = torch.nn.functional.sigmoid(self.hidden_layer_2(hidden_output_1)) - elif activation_2 == 'tanh': + elif self.activation_2 == 'tanh': hidden_output_2 = torch.nn.functional.tanh(self.hidden_layer_2(hidden_output_1)) - elif activation_2 == 'gelu': + elif self.activation_2 == 'gelu': hidden_output_2 = torch.nn.functional.gelu(self.hidden_layer_2(hidden_output_1)) - elif activation_2 == 'silu': + elif self.activation_2 == 'silu': hidden_output_2 = torch.nn.functional.silu(self.hidden_layer_2(hidden_output_1)) else: hidden_output_2 = self.hidden_layer_2(hidden_output_1) output = self.output_layer(hidden_output_2) return output - model = model_class_of_fully_connected_neural_network_with_two_hidden_layers() + model = model_class_of_fully_connected_neural_network_with_two_hidden_layers(input_size, hidden_size_1, hidden_size_2, output_size, activation_1, activation_2) return model # 全连接神经网络模型（包含三个隐藏层）（模型的类定义成全局的） @@ -80,61 +83,64 @@ def fully_connected_neural_network_with_three_hidden_layers(input_size=1, hidden import torch global model_class_of_fully_connected_neural_network_with_three_hidden_layers class model_class_of_fully_connected_neural_network_with_three_hidden_layers(torch.nn.Module): - def __init__(self): + def __init__(self, input_size, hidden_size_1, hidden_size_2, hidden_size_3, output_size, activation_1, activation_2, activation_3): super().__init__() self.hidden_layer_1 = torch.nn.Linear(input_size, hidden_size_1) self.hidden_layer_2 = torch.nn.Linear(hidden_size_1, hidden_size_2) self.hidden_layer_3 = torch.nn.Linear(hidden_size_2, hidden_size_3) self.output_layer = torch.nn.Linear(hidden_size_3, output_size) + self.activation_1 = activation_1 + self.activation_2 = activation_2 + self.activation_3 = activation_3 def forward(self, x): - if activation_1 == 'relu': + if self.activation_1 == 'relu': hidden_output_1 = torch.nn.functional.relu(self.hidden_layer_1(x)) - elif activation_1 == 'leaky_relu': + elif self.activation_1 == 'leaky_relu': hidden_output_1 = torch.nn.functional.leaky_relu(self.hidden_layer_1(x)) - elif activation_1 == 'sigmoid': + elif self.activation_1 == 'sigmoid': hidden_output_1 = torch.nn.functional.sigmoid(self.hidden_layer_1(x)) - elif activation_1 == 'tanh': + elif self.activation_1 == 'tanh': hidden_output_1 = torch.nn.functional.tanh(self.hidden_layer_1(x)) - elif activation_1 == 'gelu': + elif self.activation_1 == 'gelu': hidden_output_1 = torch.nn.functional.gelu(self.hidden_layer_1(x)) - elif activation_1 == 'silu': + elif self.activation_1 == 'silu': hidden_output_1 = torch.nn.functional.silu(self.hidden_layer_1(x)) else: hidden_output_1 = self.hidden_layer_1(x) - if activation_2 == 'relu': + if self.activation_2 == 'relu': hidden_output_2 = torch.nn.functional.relu(self.hidden_layer_2(hidden_output_1)) - elif activation_2 == 'leaky_relu': + elif self.activation_2 == 'leaky_relu': hidden_output_2 = torch.nn.functional.leaky_relu(self.hidden_layer_2(hidden_output_1)) - elif activation_2 == 'sigmoid': + elif self.activation_2 == 'sigmoid': hidden_output_2 = torch.nn.functional.sigmoid(self.hidden_layer_2(hidden_output_1)) - elif activation_2 == 'tanh': + elif self.activation_2 == 'tanh': hidden_output_2 = torch.nn.functional.tanh(self.hidden_layer_2(hidden_output_1)) - elif activation_2 == 'gelu': + elif self.activation_2 == 'gelu': hidden_output_2 = torch.nn.functional.gelu(self.hidden_layer_2(hidden_output_1)) - elif activation_2 == 'silu': + elif self.activation_2 == 'silu': hidden_output_2 = torch.nn.functional.silu(self.hidden_layer_2(hidden_output_1)) else: hidden_output_2 = self.hidden_layer_2(hidden_output_1) - if activation_3 == 'relu': + if self.activation_3 == 'relu': hidden_output_3 = torch.nn.functional.relu(self.hidden_layer_3(hidden_output_2)) - elif activation_3 == 'leaky_relu': + elif self.activation_3 == 'leaky_relu': hidden_output_3 = torch.nn.functional.leaky_relu(self.hidden_layer_3(hidden_output_2)) - elif activation_3 == 'sigmoid': + elif self.activation_3 == 'sigmoid': hidden_output_3 = torch.nn.functional.sigmoid(self.hidden_layer_3(hidden_output_2)) - elif activation_3 == 'tanh': + elif self.activation_3 == 'tanh': hidden_output_3 = torch.nn.functional.tanh(self.hidden_layer_3(hidden_output_2)) - elif activation_3 == 'gelu': + elif self.activation_3 == 'gelu': hidden_output_3 = torch.nn.functional.gelu(self.hidden_layer_3(hidden_output_2)) - elif activation_3 == 'silu': + elif self.activation_3 == 'silu': hidden_output_3 = torch.nn.functional.silu(self.hidden_layer_3(hidden_output_2)) else: hidden_output_3 = self.hidden_layer_3(hidden_output_2) output = self.output_layer(hidden_output_3) return output - model = model_class_of_fully_connected_neural_network_with_three_hidden_layers() + model = model_class_of_fully_connected_neural_network_with_three_hidden_layers(input_size, hidden_size_1, hidden_size_2, hidden_size_3, output_size, activation_1, activation_2, activation_3) return model # 卷积神经网络模型（包含两个卷积层和两个全连接层）（模型的类定义成全局的） @@ -142,7 +148,7 @@ def convolutional_neural_network_with_two_convolutional_layers_and_two_fully_con import torch global model_class_of_convolutional_neural_network_with_two_convolutional_layers_and_two_fully_connected_layers class model_class_of_convolutional_neural_network_with_two_convolutional_layers_and_two_fully_connected_layers(torch.nn.Module): - def __init__(self): + def __init__(self, in_channels, out_channels_1, out_channels_2, kernel_size_1, kernel_size_2, stride_1, stride_2, padding_1, padding_2, pooling, pooling_kernel_size, pooling_stride, input_size, hidden_size_1, hidden_size_2, output_size): super().__init__() self.convolutional_layer_1 = torch.nn.Conv2d(in_channels=in_channels, out_channels=out_channels_1, kernel_size=kernel_size_1, stride=stride_1, padding=padding_1) self.convolutional_layer_2 = torch.nn.Conv2d(in_channels=out_channels_1, out_channels=out_channels_2, kernel_size=kernel_size_2, stride=stride_2, padding=padding_2) @@ -150,8 +156,9 @@ def convolutional_neural_network_with_two_convolutional_layers_and_two_fully_con self.hidden_layer_1 = torch.nn.Linear(input_size, hidden_size_1) self.hidden_layer_2 = torch.nn.Linear(hidden_size_1, hidden_size_2) self.output_layer = torch.nn.Linear(hidden_size_2, output_size) + self.pooling = pooling def forward(self, x): - if pooling == 1: + if self.pooling == 1: channel_output_1 = torch.nn.functional.relu(self.pooling_layer(self.convolutional_layer_1(x))) channel_output_2 = torch.nn.functional.relu(self.pooling_layer(self.convolutional_layer_2(channel_output_1))) else: @@ -162,7 +169,7 @@ def convolutional_neural_network_with_two_convolutional_layers_and_two_fully_con hidden_output_2 = torch.nn.functional.relu(self.hidden_layer_2(hidden_output_1)) output = self.output_layer(hidden_output_2) return output - model = model_class_of_convolutional_neural_network_with_two_convolutional_layers_and_two_fully_connected_layers() + model = model_class_of_convolutional_neural_network_with_two_convolutional_layers_and_two_fully_connected_layers(in_channels, out_channels_1, out_channels_2, kernel_size_1, kernel_size_2, stride_1, stride_2, padding_1, padding_2, pooling, pooling_kernel_size, pooling_stride, input_size, hidden_size_1, hidden_size_2, output_size) return model # 从损失函数的变化情况中获取是否停止训练的信号 @@ -260,7 +267,7 @@ def save_model_with_torch_jit_script(model, filename='model_scripted_with_torch_ scripted_model = torch.jit.script(model) scripted_model.save(filename) -# 以字典的形式保存模型的所有信息到文件（保存时需要模型的类可访问，此外还要输入模型的实例化函数） +# 以字典的形式保存模型的所有信息到文件（保存时需要模型的类可访问，此外还要输入模型的实例化函数。需要注意的是：该方法要求类和实例化函数都是独立可直接运行的模块） def save_model_with_all_information(model, model_class, model_instantiation, note='', filename='./model_with_all_information.pth'): import torch import guan @@ -293,7 +300,7 @@ def load_model_with_torch_jit_script(filename='model_scripted_with_torch_jit.pth scripted_model = torch.jit.load(filename) return scripted_model -# 加载包含所有信息的模型（包含了模型的类和实例化函数等，返回的是模型对象） +# 加载包含所有信息的模型（包含了模型的类和实例化函数等，返回的是模型对象。需要注意的是：该方法要求类和实例化函数都是独立可直接运行的模块） def load_model_with_all_information(filename='./model_with_all_information.pth', note_print=0): import torch checkpoint = torch.load(filename)