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parent
7531432845
commit
39ce5e32c2
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@ -98,7 +98,15 @@ class JianYingExport:
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"add_background_audio",
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"add_statement_video",
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"save",
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],
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], # 适用于淘宝闪购、存量抽手机
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"0002": [
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"add_subtitles_video",
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"add_background_video",
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"add_background_audio",
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"add_logo_video",
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"add_statement_video",
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"save",
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], # 适用于视频号
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}
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# 初始化工作配置
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@ -190,6 +198,13 @@ class JianYingExport:
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},
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],
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}, # 添加标识工作配置
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"add_logo_video": {
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"material_path": self.materials["logo_video_material_path"],
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"volume": [1.0], # 播放音量
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"clip_settings": [
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None,
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], # 图像调节设置
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}, # 添加标识视频工作配置
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"add_statement": {
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"text": self.materials["statement_text"],
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"style": [
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@ -358,7 +373,18 @@ class JianYingExport:
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else:
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self.materials["logo_material_path"] = []
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# 声明（声明）
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# 标识视频
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logo_video_folder_path = self.materials_folder_path / "标识视频"
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if logo_video_folder_path.exists() and logo_video_folder_path.is_dir():
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self.materials["logo_video_material_path"] = [
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file_path
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for file_path in logo_video_folder_path.rglob("*.mov")
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if file_path.is_file()
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]
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else:
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self.materials["logo_video_material_path"] = []
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# 声明文本
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statement_path = self.materials_folder_path / "声明.txt"
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if statement_path.exists() and statement_path.is_file():
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with open(statement_path, "r", encoding="utf-8") as file:
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@ -369,7 +395,7 @@ class JianYingExport:
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else:
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self.materials["statement_text"] = []
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# 声明（视频）
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# 声明视频
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statement_video_folder_path = self.materials_folder_path / "声明视频"
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if (
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statement_video_folder_path.exists()
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@ -500,12 +526,17 @@ class JianYingExport:
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print("-> 正在添加标识...", end="")
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draft.add_video_segment(**parameters)
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print("已完成")
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# 添加声明
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# 添加标识视频
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case "add_logo_video":
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print("-> 正在添加标识视频...", end="")
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draft.add_video_segment(**parameters)
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print("已完成")
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# 添加声明文本
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case "add_statement":
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print("-> 正在添加声明...", end="")
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draft.add_text_segment(**parameters)
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print("已完成")
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# 添加视频
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# 添加声明视频
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case "add_statement_video":
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print("-> 正在添加声明视频...", end="")
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draft.add_video_segment(**parameters)
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236
神经网络/main.py
236
神经网络/main.py
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@ -4,7 +4,7 @@
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"""
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# 导入模块
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from typing import List
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from typing import List, Literal
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import numpy
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@ -13,48 +13,206 @@ class NeuralNetwork:
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神经网络
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"""
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# 激活函数和其导数函数
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FUNCTIONS = {
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"relu": {
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"activate": lambda x: numpy.maximum(0, x),
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"derivative": lambda x: numpy.where(x > 0, 1, 0),
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},
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"linear": {
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"activate": lambda x: x,
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"derivative": lambda x: numpy.ones_like(x),
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}, # 适合回归任务的输出层
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"softmax": {
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"activate": lambda x: numpy.exp(x) / numpy.sum(numpy.exp(x), axis=1),
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"derivative": lambda x: x * (1 - x),
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}, # 适合分类任务的输出层
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}
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def __init__(
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self,
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hidden_layer_neurons: List[int] = [10],
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hidden_layer_function: str = "relu",
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output_layer_function: str = "softmax",
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neurons: List[int],
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hidden_activate: Literal["relu"] = "relu",
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output_activate: Literal["linear", "softmax"] = "linear",
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):
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"""
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初始化
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:param hidden_layer_neurons: 隐含层神经元数量
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:param hidden_layer_function: 隐含层函数
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:param output_layer_function: 输出层函数
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:param neurons: 神经元结构，例如[2, 10, 1]表示输入层为2个神经元、第一层隐含层为10个神经元、输出层为1个神经元
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:param hidden_activate: 隐含层激活函数，默认为relu
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:param output_activate: 输出层激活函数，默认为linear
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"""
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# 检查函数是否存在
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if not (
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hidden_layer_function in self.FUNCTIONS
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and output_layer_function in self.FUNCTIONS
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):
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raise RuntimeError("所输入的隐含层或输出层函数未定义")
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print("正在初始化神经网络...", end="")
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# 初始化神经元结构
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self.neurons = neurons
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# 初始化隐含层激活函数
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self.hidden_activate = hidden_activate
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# 初始化输出层激活函数
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self.output_activate = output_activate
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# 初始化神经网络结构
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self.neural_network = {}
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# 初始化神经网络所有层权重和偏置
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self._init_neural_network()
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print("已完成")
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def _init_neural_network(self):
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"""
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初始化神经网络所有层权重和偏置
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"""
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for idx in range(1, len(self.neurons)):
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# 若为隐含层则根据隐含层激活函数计算当前层权重的标准偏差，若为输出层则根据输出层激活函数计算当前层权重的标准偏差
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if idx != len(self.neurons) - 1:
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# 激活函数
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activate = self.hidden_activate
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match self.hidden_activate:
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case "relu":
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# 当前层权重的标准偏差
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standard_deviation = numpy.sqrt(
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2 / self.neurons[idx - 1]
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) # 使用He方差公式
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case _:
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raise RuntimeError(
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f"暂不支持该隐含层激活函数 {self.hidden_activate}"
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)
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else:
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# 激活函数
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activate = self.output_activate
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match self.output_activate:
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case "linear":
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# 当前层权重的标准偏差
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standard_deviation = numpy.sqrt(1 / self.neurons[idx - 1])
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case "softmax":
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# 当前层权重的标准偏差
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standard_deviation = numpy.sqrt(
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2 / (self.neurons[idx - 1] + self.neurons[idx])
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) # 使用Xavier方差公式
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case _:
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raise RuntimeError(
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f"暂不支持该输出层激活函数 {self.output_activate}"
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)
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self.neural_network[f"layer:{idx:03d}"] = {
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"weight": numpy.random.randn(self.neurons[idx - 1], self.neurons[idx])
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* standard_deviation, # 当前层权重
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"bias": numpy.zeros((1, self.neurons[idx])), # 当前层偏置
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"activate": activate, # 当前层激活函数
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"gamma": numpy.ones((1, self.neurons[idx])), # 当前层批标准化的缩放因子
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"beta": numpy.zeros((1, self.neurons[idx])), # 当前层批标准化的偏移因子
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}
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def _forward_propagate(self, x: numpy.ndarray) -> numpy.ndarray:
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"""
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前向传播
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:param x: 输入层输入
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:return: 输出层预测值
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"""
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activation = x # 将输入层输入作为第0层的激活值
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for layer_name, layer in self.neural_network.items():
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self.neural_network[layer_name].update(
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{
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"weighted_sum": (
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weighted_sum := numpy.dot(activation, layer["weight"])
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+ layer["bias"]
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), # 当前层加权和
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"batch_normalized_weighted_sum": (
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batch_normalized_weighted_sum := layer["gamma"]
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* (
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weighted_sum
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- numpy.mean(weighted_sum, axis=0, keepdims=True)
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)
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/ numpy.sqrt(
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numpy.var(
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weighted_sum, ddof=0, axis=0, keepdims=True
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) # 使用有偏方差公式
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+ 1e-8
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)
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+ layer["beta"]
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), # 当前层批标准化加权和
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"activation": (
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activation := self._activate(
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activate=layer["activate"],
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weighted_sum=batch_normalized_weighted_sum,
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)
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), # 当前层激活值
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}
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)
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y_predict = activation # 将第L-1层（最后一层）的激活值作为输出层预测值（L为神经网络层数）
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return y_predict
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def _activate(
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self,
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activate: Literal["relu", "linear", "softmax"],
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weighted_sum: numpy.ndarray,
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) -> numpy.ndarray:
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"""
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激活函数
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:param activate: 激活函数
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:param weighted_sum: 加权和
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:return: 激活值
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"""
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match activate:
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case "relu":
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return numpy.maximum(0, weighted_sum)
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case "linear":
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return weighted_sum
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case "softmax":
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# 加权和指数项
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e_weighted_sum = numpy.exp(
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weighted_sum - numpy.max(weighted_sum, axis=1, keepdims=True)
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)
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return e_weighted_sum / numpy.sum(e_weighted_sum, axis=1, keepdims=True)
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def _calculate_loss(
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self,
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y_true: numpy.ndarray,
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y_predict: numpy.ndarray,
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) -> numpy.floating:
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"""
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计算损失
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:param y_true: 输出层真实值
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:param y_predict: 输出层预测值
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:return: 损失值
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"""
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# 第L-1层（最后一层）的层名
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layer_name = list(self.neural_network.keys())[-1]
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# 根据第L-1层（最后一层）的激活函数计算损失
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match activate := self.neural_network[layer_name]["activate"]:
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case "linear":
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loss = 0.5 * numpy.mean(
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numpy.square(y_true - y_predict)
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) # 使用均方误差公式
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case "softmax":
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loss = -1 * numpy.mean(
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numpy.sum(y_true * numpy.log(y_predict + 1e-8), axis=1)
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) # 使用交叉熵损失公式
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case _:
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raise RuntimeError(f"暂不支持该输出层激活函数 {activate}")
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return loss
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def _backward_propagate(
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self,
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x: numpy.ndarray,
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y_true: numpy.ndarray,
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y_predict: numpy.ndarray,
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) -> None:
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"""
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后向传播
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:param x: 输入层输入
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:param y_true: 输出层真实值
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:param y_predict: 输出层预测值
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:return: 无
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"""
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# 所有层的层名
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layer_names = list(self.neural_network.keys())
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for idx, layer_name in enumerate(reversed(layer_names)):
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# 当前层激活函数、加权和、批标准化加权和和激活值
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activate, weighted_sum, batch_normalized_weighted_sum, activation = (
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self.neural_network[layer_name]["activate"],
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self.neural_network[layer_name]["weighted_sum"],
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self.neural_network[layer_name]["batch_normalized_weighted_sum"],
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self.neural_network[layer_name]["activation"],
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)
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# 输出层的误差项
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if idx == 0:
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match activate:
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case "linear" | "softmax":
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delta = y_predict - y_true # 损失函数对第L-1层（最后一层）激活值的梯度
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case _:
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raise RuntimeError(f"暂不支持该输出层激活函数 {activate}")
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# 隐含层的误差项
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else:
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delta = numpy.dot(delta, self.neural_network[layer_names[idx - 1]]["weight"].T)
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delta = 0
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# 初始化隐含层的激活函数和导数函数
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self.hidden_layer_activate, self.hidden_layer_derivative = (
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self.FUNCTIONS[hidden_layer_function]["activate"],
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self.FUNCTIONS[hidden_layer_function]["derivative"],
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)
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# 初始化输出层的激活函数和导数函数
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self.output_layer_activate, self.output_layer_derivative = (
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self.FUNCTIONS[output_layer_function]["activate"],
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self.FUNCTIONS[output_layer_function]["derivative"],
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)
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