资讯详情

PyTorch学习基础知识二

PyTorch学习基础知识二

文章目录

    • PyTorch学习基础知识二
      • 一、简介
      • 二、 神经网络概述
      • 三、浅层神经网络概述
      • 四、神经网络的例子
      • 五、总结

一、简介

这是PyTorch学习基础知识的第二篇主要讲解神经网络的相关知识。

二、 神经网络概述

""" 神经网络知识 Sigmoid函数(参见具体形式Sigmoid函数.jpg) 损失函数(Cross Entropy Loss) (交叉熵损失函数(Cross Entropy Loss)) (见具体形式:交叉熵损失函数0.jpg,叉熵损失函数1.jpg 以及 交叉熵损失函数2.jpg) 梯度下降(Gradient Descent) (梯度下降原理) 计算图 神经网络模型主要分为两个步骤: 1、正向传播(Forward Propagation); 2、反向传播(Back Propagation)。 (正传播是计算损失函数的过程,反传播是计算参数梯度的过程。 (正传播是计算损失函数的过程,反传播是计算参数梯度的过程。 使用计算图可以求导参数 (逻辑回归、损失函数、梯度下降和计算图) """             

三、浅层神经网络概述

""" NN 浅层神经网络NN(Neutral Network) Input Layer Hidden Layer Output Layer (见单个神经元结构.jpg) 线性部分 非线性部分(非线性部分主要是激活函数,激活函数主要是Sigmoid函数) 1.激活函数(正向传播) Sigmoid函数(Activation Function 激活函数中的一种) 除此之外,激活函数也可以是: tanh函数(参见tanh函数.jpg) RelU函数(参见RelU函数.jpg) Leaky_ReLU函数(参见Leaky_ReLU函数.jpg)(可能有一个表达式不清楚,请参见LeakyRelU表达式.jpg) 其中,λ 一般为可变参数 λ∈(0,1),例如 λ=0.01 2.反向传播(神经网络反向传输) 损失函数(见损失函数).jpg) 整体流程见正方向传输(NN).jpg """  """ """ 

四、神经网络的例子

目前,我还没有完全理解这个案例,但我正在努力学习,这里供您参考:

""" 自己写一个神经网络模型 (目前不太懂...) 后续需要进一步看这个东西,然后学会自己搭建。 """   import numpy as np import matplotlib.pyplot as plt  # %matplotlib inline  r = np.random.randn(200)*0.8 x1 = np.linspace(-3, 1, 200) x2 = np.linspace(-1, 3, 200) y1 = x1*x1   2*x1 - 2   r y2 = -x2*x2   2*x2   2   r  X = np.hstack(([x1, y1], [x2, y2]))                     # 输入样本 X,维度:2 x 400 Y = np.hstack(np.zeros((1, 200)), np.ones((1, 200)))) # 输出标签 Y plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral) # plt.show() m = X.shape[1] # 样本个数 n_x = X.shape[0] # 输入层神经元个数 n_h = 3 # 隐藏层神经元个数 n_y = Y.shape[0] # 输出层神经元个数 W1 = np.random.randn(n_h,n_x)*0.01 b1 = np.zeros((n_h,1)) W2 = np.random.randn(n_y,n_h)*0.01 b2 = np.zeros((n_y,1)) assert (W1.shape == (n_h, n_x)) assert (b1.shape == (n_h, 1)) assert (W2.shape == (n_y, n_h)) assert (b2.shape == (n_y, 1)) parameters = { 
         "W1": W1, "b1": b1, "W2": W2, "b2": b2} def sigmoid(x): """ Compute the sigmoid of x """ s = 1/(1+np.exp(-x)) return s def forward_propagation(X, parameters): W1 = parameters["W1"] b1 = parameters["b1"] W2 = parameters["W2"] b2 = parameters["b2"] Z1 = np.dot(W1,X) + b1 A1 = np.tanh(Z1) Z2 = np.dot(W2,A1) + b2 A2 = sigmoid(Z2) assert(A2.shape == (1, X.shape[1])) cache = { 
         "Z1": Z1, "A1": A1, "Z2": Z2, "A2": A2} return A2, cache def compute_cost(A2, Y, parameters): m = Y.shape[1] # number of example # 计算交叉熵损失函数 logprobs = np.multiply(np.log(A2),Y)+np.multiply(np.log(1-A2),(1-Y)) cost = - 1/m * np.sum(logprobs) cost = np.squeeze(cost) return cost def backward_propagation(parameters, cache, X, Y): m = X.shape[1] W1 = parameters["W1"] W2 = parameters["W2"] A1 = cache["A1"] A2 = cache["A2"] # 反向求导 dZ2 = A2 - Y dW2 = 1/m*np.dot(dZ2,A1.T) db2 = 1/m*np.sum(dZ2,axis=1,keepdims=True) dZ1 = np.dot(W2.T,dZ2)*(1 - np.power(A1, 2)) dW1 = 1/m*np.dot(dZ1,X.T) db1 = 1/m*np.sum(dZ1,axis=1,keepdims=True) # 存储各个梯度值 grads = { 
         "dW1": dW1, "db1": db1, "dW2": dW2, "db2": db2} return grads def update_parameters(parameters, grads, learning_rate = 0.1): W1 = parameters["W1"] b1 = parameters["b1"] W2 = parameters["W2"] b2 = parameters["b2"] dW1 = grads["dW1"] db1 = grads["db1"] dW2 = grads["dW2"] db2 = grads["db2"] W1 = W1 - learning_rate*dW1 b1 = b1 - learning_rate*db1 W2 = W2 - learning_rate*dW2 b2 = b2 - learning_rate*db2 parameters = { 
         "W1": W1, "b1": b1, "W2": W2, "b2": b2} return parameters def nn_model(X, Y, n_h = 3, num_iterations = 10000, print_cost=False): m = X.shape[1] # 样本个数 n_x = X.shape[0] # 输入层神经元个数 n_y = Y.shape[0] # 输出层神经元个数 W1 = np.random.randn(n_h,n_x)*0.01 b1 = np.zeros((n_h,1)) W2 = np.random.randn(n_y,n_h)*0.01 b2 = np.zeros((n_y,1)) parameters = { 
         "W1": W1, "b1": b1, "W2": W2, "b2": b2} # 迭代训练 J = [] # 存储损失函数 for i in range(0, num_iterations): A2, cache = forward_propagation(X, parameters) # 正向传播 cost = compute_cost(A2, Y, parameters) # 计算损失函数 grads = backward_propagation(parameters, cache, X, Y) # 反向传播 parameters = update_parameters(parameters, grads) # 更新权重 J.append(cost) # 每隔 1000 次训练,打印 cost if print_cost and i % 1000 == 0: print ("Cost after iteration %i: %f" %(i, cost)) return parameters parameters = nn_model(X, Y, n_h = 3, num_iterations = 10000, print_cost=True) def predict(parameters, X): A2, cache = forward_propagation(X, parameters) predictions = A2 > 0.5 return predictions y_pred = predict(parameters,X) accuracy = np.mean(y_pred == Y) print(accuracy) def plot_decision_boundary(model, X, y): x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1 y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1 h = 0.01 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) Z = model(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral) plt.ylabel('x2') plt.xlabel('x1') plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral) plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y) plt.title("Decision Boundary, hidden layers = 5") plt.show() """ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### 构造原始数据" ] }, { "cell_type": "code", "execution_count": 466, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "\n", "r = np.random.randn(200)*0.8\n", "x1 = np.linspace(-3, 1, 200)\n", "x2 = np.linspace(-1, 3, 200)\n", "y1 = x1*x1 + 2*x1 - 2 + r\n", "y2 = -x2*x2 + 2*x2 + 2 + r\n", "\n", "X = np.hstack(([x1, y1],[x2, y2])) # 输入样本 X,维度:2 x 400\n", "Y = np.hstack((np.zeros((1,200)),np.ones((1,200)))) # 输出标签 Y" ] }, { "cell_type": "code", "execution_count": 467, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMD+3gmYqYMjMAEXFVlBM1qE4

标签: bzw04p26抑制二极管电子台秤传感器yzobk6电阻器bk6系列电阻器bzw04p17抑制二极管bzw04p64抑制二极管

锐单商城拥有海量元器件数据手册IC替代型号,打造 电子元器件IC百科大全!

锐单商城 - 一站式电子元器件采购平台