CS231N课程作业Assignment1--Softmax
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Assignment1–Softmax
作业要求见这里.
主要需要完成 KNN，SVM，Softmax分类器，还有一个两层的神经网络分类器的实现。
数据集CIFAR-10.
Softmax原理
将线性分类得到的得分值转化为概率值，进行多分类，在SVM中的输出是得分值，Softmax的输出是概率。
Softmax 函数：其输入值是一个向量，向量中元素为任意实数的评分值，输出一个向量，其中每个元素值在0到1之间，且所有元素之和为1。
构建Softmax分类器
程序整体框架如下：包括classifiers和datasets文件夹，softmax.py、data_utils.py、linear_softmax.py和linear_classifier.py
softmax.py
from linear_classifier import Softmax
import time
import numpy as np #导入numpy的库函数
from datasets.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
from classifiers.linear_softmax import *
import math
cifar10_dir = 'E:/cifar-10-batches-py'
X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)
print('Training data shape: ',X_train.shape)
print('Training labels shape: ',y_train.shape)
print('Test data shape: ',X_test.shape)
print('Test labels shape: ',y_test.shape)
classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
num_classes = len(classes)
samples_per_class = 7 #每个类别采样个数
for y,cls in enumerate(classes): #(0,plane),y返回元素位置，cls返回元素本身
idxs = np.flatnonzero(y_train==y) #找出标签中y类的位置
idxs = np.random.choice(idxs,samples_per_class,replace=False) #从中随机算出7个样本
for i,idx in enumerate(idxs): #对所选样本的位置和样本所对应的图片在训练集中的位置进行循环
plt_idx = i * num_classes + y + 1 #在子图中所占位置的计算
plt.subplot(samples_per_class,num_classes,plt_idx) #说明要画的子图的编号
plt.imshow(X_train[idx].astype('uint8')) #画图
plt.axis('off')
if i == 0:
plt.title(cls) #写上类别名
plt.show()
num_training = 49000 # 训练集 num_dev会从其中抽取一定数量的图片用于训练，减少训练时间
num_validation = 1000 # 验证集 在不同的学习率和正则参数下使用该验证集获取最高的正确率，最终找到最好的学习率和正则参数
num_test = 1000 # 测试集 在获取到最好的学习率和正则参数之后，测试最终的正确率
num_dev = 500 # 随机训练集 用于实现随机化梯度下降的
mask = list(range(num_training, num_training + num_validation)) # 从训练数据x_train和y_train中获取验证集数据
X_val = X_train[mask]
y_val = y_train[mask]
mask = list(range(num_training)) # 从训练数据x_train和y_train中获取全体训练集数据
X_train = X_train[mask]
y_train = y_train[mask]
mask = list(range(num_test)) # 从训练数据x_test和y_test中获取全体测试集数据
X_test = X_test[mask]
y_test = y_test[mask]
mask = np.random.choice(num_training, num_dev, replace=False) # 从num_training中随机选取随机训练集数据
X_dev = X_train[mask]
y_dev = y_train[mask]
# 将x_train，x_val，x_test，x_dev这些n*32*32*3的图片集，转化成n*3072的矩阵；将每张图片拉伸成一维的矩阵，方便后面进行数据处理
X_train = np.reshape(X_train, (X_train.shape[0], -1))
X_val = np.reshape(X_val, (X_val.shape[0], -1))
X_test = np.reshape(X_test, (X_test.shape[0], -1))
X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))
# 将x_train，x_val，x_test，x_dev这些图片集进行去均值处理 ；统一量纲，和归一化操作类似，只是没有再除以方差而已
mean_image = np.mean(X_train, axis = 0)
X_train -= mean_image
X_val -= mean_image
X_test -= mean_image
X_dev -= mean_image
X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])
X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])
X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])
X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])
print('Train data shape: ', X_train.shape)
print('Train labels shape: ', y_train.shape)
print('Validation data shape: ', X_val.shape)
print('Validation labels shape: ', y_val.shape)
print('Test data shape: ', X_test.shape)
print('Test labels shape: ', y_test.shape)
print('dev data shape: ', X_dev.shape)
print('dev labels shape: ', y_dev.shape)
W = np.random.randn(3073, 10) * 0.0001
loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)
print('loss: %f' % loss)
print('sanity check: %f' % (-np.log(0.1)))
tic = time.time()
loss_naive, grad_naive = softmax_loss_naive(W, X_dev, y_dev, 0.000005)
toc = time.time()
print('naive loss: %e computed in %fs' % (loss_naive, toc - tic))
tic = time.time()
loss_vectorized, grad_vectorized = softmax_loss_vectorized(W, X_dev, y_dev, 0.000005)
toc = time.time()
print('vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))
grad_difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')
print('Loss difference: %f' % np.abs(loss_naive - loss_vectorized))
print('Gradient difference: %f' % grad_difference)
#调参
#两个参数，学习率；正则化强度
results =
best_val = -1
best_softmax = None
learning_rates = [1e-7, 3e-7, 5e-7, 9e-7]
regularization_strengths = [2.5e4, 1e4, 3e4, 2e4]
for lr in learning_rates: # 循环执行代码；对不同的学习率以及正则化强度进行测试
for reg in regularization_strengths:
softmax = Softmax() # learning_rate学习率；reg正则化强度；num_iters步长值；batch_size每一步使用的样本数量；verbose若为真则打印过程
loss_hist = softmax.train(X_train, y_train, learning_rate=lr, reg=reg,num_iters=1500, verbose=True)
y_train_pred = softmax.predict(X_train)
y_val_pred = softmax.predict(X_val)
y_train_acc = np.mean(y_train_pred==y_train) # np.mean():求取均值
y_val_acc = np.mean(y_val_pred==y_val)
results[(lr,reg)] = [y_train_acc, y_val_acc]
if y_val_acc > best_val: # 判断优略
best_val = y_val_acc
best_softmax = softmax # 保存当前模型
for lr, reg in sorted(results):
train_accuracy, val_accuracy = results[(lr, reg)] # 存储数据
print('lr %e reg %e train accuracy: %f val accuracy: %f' % (lr, reg, train_accuracy, val_accuracy))
print('best validation accuracy achieved during cross-validation: %f' % best_val)
x_scatter = [math.log10(x[0]) for x in results]
y_scatter = [math.log10(x[1]) for x in results]
marker_size = 100
colors = [results[x][0] for x in results]
plt.subplot(1, 2, 1)
plt.scatter(x_scatter, y_scatter, marker_size, c=colors)
plt.colorbar()
plt.xlabel('log learning rate')
plt.ylabel('log regularization strength')
plt.title('CIFAR-10 training accuracy')
colors = [results[x][1] for x in results] # default size of markers is 20
plt.subplot(1, 2, 2)
plt.scatter(x_scatter, y_scatter, marker_size, c=colors)
plt.colorbar()
plt.xlabel('log learning rate')
plt.ylabel('log regularization strength')
plt.title('CIFAR-10 validation accuracy')
plt.show()
y_test_pred = best_softmax.predict(X_test)
test_accuracy = np.mean(y_test == y_test_pred)
print('softmax on raw pixels final test set accuracy: %f' % (test_accuracy, ))
#得到最优W时，W的可视化结果数据 W的图像可以看出权重的高低
w = best_softmax.W[:-1,:] # strip out the bias
w = w.reshape(32, 32, 3, 10)
w_min, w_max = np.min(w), np.max(w)
classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] # 类别划分 列表
for i in range(10):
plt.subplot(2, 5, i + 1)
# Rescale the weights to be between 0 and 255
wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)
plt.imshow(wimg.astype('uint8'))
plt.axis('off')
plt.title(classes[i])
plt.show() # W最终学习成的图片
data_utils.py
from __future__ import print_function
from builtins import range
from six.moves import cPickle as pickle
import numpy as np
import os
from imageio import imread
import platform
def load_pickle(f):
version = platform.python_version_tuple()
if version[0] == '2':
return pickle.load(f)
elif version[0] == '3':
return pickle.load(f, encoding='latin1')
raise ValueError("invalid python version: ".format(version))
def load_CIFAR_batch(filename):
""" load single batch of cifar """
with open(filename, 'rb') as f:
datadict = load_pickle(f)
X = datadict['data']
Y = datadict['labels']
X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype("float")
Y = np.array(Y)
return X, Y
def load_CIFAR10(ROOT):
""" load all of cifar """
xs = []
ys = []
for b in range(1,6):
f = os.path.join(ROOT, 'data_batch_%d' % (b, ))
X, Y = load_CIFAR_batch(f)
xs.append(X)
ys.append(Y)
Xtr = np.concatenate(xs)
Ytr = np.concatenate(ys)
del X, Y
Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))
return Xtr, Ytr, Xte, Yte
def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000,
subtract_mean=True):
"""
Load the CIFAR-10 dataset from disk and perform preprocessing to prepare
it for classifiers. These are the same steps as we used for the SVM, but
condensed to a single function.
"""
# Load the raw CIFAR-10 data
cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'
X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)
# Subsample the data
mask = list(range(num_training, num_training + num_validation))
X_val = X_train[mask]
y_val = y_train[mask]
mask = list(range(num_training))
X_train = X_train[mask]
y_train = y_train[mask]
mask = list(range(num_test))
X_test = X_test[mask]
y_test = y_test[mask]
# Normalize the data: subtract the mean image
if subtract_mean:
mean_image = np.mean(X_train, axis=0)
X_train -= mean_image
X_val -= mean_image
X_test -= mean_image
# Transpose so that channels come first
X_train = X_train.transpose(0, 3, 1, 2).copy()
X_val = X_val.transpose(0, 3, 1, 2).copy()
X_test = X_test.transpose(0, 3, 1, 2).copy()
# Package data into a dictionary
return
'X_train': X_train, 'y_train': y_train,
'X_val': X_val, 'y_val': y_val,
'X_test': X_test, 'y_test': y_test,
def load_tiny_imagenet(path, dtype=np.float32, subtract_mean=True):
"""
Load TinyImageNet. Each of TinyImageNet-100-A, TinyImageNet-100-B, and
TinyImageNet-200 have the same directory structure, so this can be used
to load any of them.
Inputs:
- path: String giving path to the directory to load.
- dtype: numpy datatype used to load the data.
- subtract_mean: Whether to subtract the mean training image.
Returns: A dictionary with the following entries:
- class_names: A list where class_names[i] is a list of strings giving the
WordNet names for class i in the loaded dataset.
- X_train: (N_tr, 3, 64, 64) array of training images
- y_train: (N_tr,) array of training labels
- X_val: (N_val, 3, 64, 64) array of validation images
- y_val: (N_val,) array of validation labels
- X_test: (N_test, 3, 64, 64) array of testing images.
- y_test: (N_test,) array of test labels; if test labels are not available
(such as in student code) then y_test will be None.
- mean_image: (3, 64, 64) array giving mean training image
"""
# First load wnids
with open(os.path.join(path, 'wnids.txt'), 'r') as f:
wnids = [x.strip() for x in f]
# Map wnids to integer labels
wnid_to_label = wnid: i for i, wnid in enumerate(wnids)
# Use words.txt to get names for each class
with open(os.path.join(path, 'words.txt'), 'r') as f:
wnid_to_words = dict(line.split('\\t') for line in f)
for wnid, words in wnid_to_words.items():
wnid_to_words[wnid] = [w.strip() for w in words.split(',')]
class_names = [wnid_to_words[wnid] for wnid in wnids]
# Next load training data.
X_train = []
y_train = []
for i, wnid in enumerate(wnids):
if (i + 1) % 20 == 0:
print('loading training data for synset %d / %d'
% (i + 1, len(wnids)))
# To figure out the filenames we need to open the boxes file
boxes_file = os.path.join(path, 'train', wnid, '%s_boxes.txt&以上是关于CS231N课程作业Assignment1--Softmax的主要内容，如果未能解决你的问题，请参考以下文章
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