《深度学习》,又名“花书”。该书由三位大佬 Ian Goodfellow、Yoshua Bengio 和 Aaron Courville 撰写,是深度学习领域奠基性的经典教材,被誉为深度学习的“圣经”。
原书内容非常充实,接近 800 页。这本书内容很深很全面,但起点稍微高了一些,对数学理论基础知识要求的比较多。因此,读完之后,及时进行高度概括和经验总结是十分有帮助的。石头君最近在 GitHub 上发现一个关于花书各章摘要的项目,内容非常精炼,除了笔记的同时,部分章节还配备代码,值得推荐,我们一起来看一下。
该项目的名称是:Deep-Learning-Book-Chapter-Summaries,作者是 Aman Dalmia 和 Ameya Godbole 两位小哥。项目地址为:
https://github.com/dalmia/Deep-Learning-Book-Chapter-Summaries
主要内容
这份花书核心笔记主要涉及的章节包括:
- ch02 线性代数
-
ch03 概率与信息理论
-
ch04 数值优化
-
ch07 深度学习正则化
-
ch08 深度模型中的优化
-
ch09 卷积网络
-
ch11 实践方法论
-
ch13 线性因子模型
笔记的形式是 .ipynb,便于在 Jupyter Notebook 上打开和观看。例如,我们来看一下第二章线性代数的笔记。
可见,Jupyter 笔记不仅包含了知识点的总结,也有相关代码。再来看第九章的卷积网络部分,配备了一些完整的图片处理代码。
import numpy as np
from scipy import signal
from scipy import misc
import matplotlib.pyplot as plt
# %matplotlib inline
img = misc.ascent()
kernel = np.random.randn(5,5)
# kernel = np.array([[0,-10,0,10,0],[-10,-30,0,30,10],[0,-10,0,10,0]])
img = img.astype(np.float32)/255
orig_in = img
offsetx = offsety = 20
shift_in = np.zeros(orig_in.shape)
shift_in[offsetx:,offsety:] = img[:-offsetx,:-offsety]
rot_in = misc.imrotate(img, 90)
scale_in = misc.imresize(orig_in, 1.5)
output1 = signal.convolve2d(orig_in, kernel, mode='same')
output2 = signal.convolve2d(shift_in, kernel, mode='same')
output3 = signal.convolve2d(rot_in, kernel, mode='same')
output4 = signal.convolve2d(scale_in, kernel, mode='same')
fig, axes = plt.subplots(2, 4, figsize=(14, 7))
ax_orig = axes[0,0]
ax_shift = axes[0,1]
ax_rot = axes[0,2]
ax_scale = axes[0,3]
diff_orig = axes[1,0]
diff_shift = axes[1,1]
diff_rot = axes[1,2]
diff_scale = axes[1,3]
ax_orig.imshow(output1, cmap='gray')
ax_orig.set_title('Original')
ax_shift.imshow(output2, cmap='gray')
ax_shift.set_title('Shifted')
ax_rot.imshow(output3, cmap='gray')
ax_rot.set_title('Rotated')
ax_scale.imshow(output4, cmap='gray')
ax_scale.set_title('Scaled')
def shift(arr, offset):
output = np.zeros(arr.shape)
output[offset:, offset:] = arr[:-offset,:-offset]
return output
def rotate(arr, angle):
return misc.imrotate(arr, angle)
def resize(arr, scale):
return misc.imresize(arr, scale)
diff_orig.hist(np.ravel(output1),bins=100)
diff_orig.set_title('Output histogram')
diff_shift.hist(np.ravel(np.abs(output2-shift(output1, 20))),bins=100)
diff_shift.set_title('Shift histogram difference')
diff_rot.hist(np.ravel(np.abs(output3-rotate(output1, 10))),bins=100)
diff_rot.set_title('Rotate histogram difference')
diff_scale.hist(np.ravel(np.abs(output4-resize(output1, 1.5))),bins=100)
diff_scale.set_title('Scale histogram difference')
ax_orig.set_xticks([])
ax_shift.set_xticks([])
ax_rot.set_xticks([])
ax_scale.set_xticks([])
ax_orig.set_yticks([])
ax_shift.set_yticks([])
ax_rot.set_yticks([])
ax_scale.set_yticks([])
plt.tight_layout()
# plt.show()
plt.savefig('images/conv_equivariance.png')
对于池化层的代码示例:
import numpy as np
np.random.seed(101)
from scipy import signal
from scipy import misc
import matplotlib.pyplot as plt
%matplotlib inline
img = misc.ascent()
img = img.astype(np.float32)/255
# The image is more interesting here
orig_in = img[-200:,-300:-100]
offsetx = offsety = 15
shift_in = img[-200-offsetx:-offsetx,-300-offsety:-100-offsety]
kernel1 = np.random.randn(5,5)
kernel2 = np.random.randn(5,5)
kernel3 = np.random.randn(5,5)
def sigmoid(arr):
# Lazy implementation of sigmoid activation
return 1./(1 + np.exp(-arr))
def maxpool(arr, poolsize, stride):
# Lazy looping implementation of maxpool
output_shape = np.floor((np.array(arr.shape)-poolsize)/stride)+1
output_shape = output_shape.astype(np.int32)
output = np.zeros(output_shape)
for x in range(output_shape[0]):
for y in range(output_shape[1]):
output[x,y] = np.max(arr[x*stride:x*stride+poolsize,y*stride:y*stride+poolsize])
return output
output1_1 = signal.convolve2d(orig_in, kernel1, mode='valid')
pool1_1 = maxpool(output1_1, 2, 2)
actv1_1 = sigmoid(pool1_1)
output1_2 = signal.convolve2d(actv1_1, kernel2, mode='valid')
pool1_2 = maxpool(output1_2, 2, 2)
actv1_2 = sigmoid(pool1_2)
output1_3 = signal.convolve2d(actv1_2, kernel3, mode='valid')
pool1_3 = maxpool(output1_3, 2, 2)
output2_1 = signal.convolve2d(shift_in, kernel1, mode='valid')
pool2_1 = maxpool(output2_1, 2, 2)
actv2_1 = sigmoid(pool2_1)
output2_2 = signal.convolve2d(actv2_1, kernel2, mode='valid')
pool2_2 = maxpool(output2_2, 2, 2)
actv2_2 = sigmoid(pool2_2)
output2_3 = signal.convolve2d(actv2_2, kernel3, mode='valid')
pool2_3 = maxpool(output2_3, 2, 2)
fig, axes = plt.subplots(4, 3, figsize=(10, 10))
k1, k2, k3 = axes[0,:]
p1_1, p1_2, p1_3 = axes[1,:]
p2_1, p2_2, p2_3 = axes[2,:]
h1, h2, h3 = axes[3,:]
k1.imshow(kernel1, cmap='gray')
k1.set_title('kernel1')
k2.imshow(kernel2, cmap='gray')
k2.set_title('kernel2')
k3.imshow(kernel3, cmap='gray')
k3.set_title('kernel3')
k1.set_xticks([])
k2.set_xticks([])
k3.set_xticks([])
k1.set_yticks([])
k2.set_yticks([])
k3.set_yticks([])
p1_1.imshow(pool1_1, cmap='gray')
p1_1.set_title('pool1_1')
p1_2.imshow(pool1_2, cmap='gray')
p1_2.set_title('pool1_2')
p1_3.imshow(pool1_3, cmap='gray')
p1_3.set_title('pool1_3')
p1_1.set_xticks([])
p1_2.set_xticks([])
p1_3.set_xticks([])
p1_1.set_yticks([])
p1_2.set_yticks([])
p1_3.set_yticks([])
p2_1.imshow(pool2_1, cmap='gray')
p2_1.set_title('pool2_1')
p2_2.imshow(pool2_2, cmap='gray')
p2_2.set_title('pool2_2')
p2_3.imshow(pool2_3, cmap='gray')
p2_3.set_title('pool2_3')
p2_1.set_xticks([])
p2_2.set_xticks([])
p2_3.set_xticks([])
p2_1.set_yticks([])
p2_2.set_yticks([])
p2_3.set_yticks([])
h1.hist(np.ravel(np.abs(pool1_1-pool2_1)),bins=100)
h1.set_title('Pool 1 diff')
h2.hist(np.ravel(np.abs(pool1_2-pool2_2)),bins=100)
h2.set_title('Pool 2 diff')
h3.hist(np.ravel(np.abs(pool1_3-pool2_3)),bins=100)
h3.set_title('Pool 3 diff')
plt.tight_layout()
# plt.show()
plt.savefig('images/pool_invariance.png')
博客笔记
该项目的作者还在自己的个人网站上发布了花书的精炼笔记,地址为:
https://medium.com/inveterate-learner/tagged/deep-learning
附加资源
除了这份花书重点章节摘要之外,石头君还推荐一份来自 Microsoft 计算机软件工程师 Jeff Macaluso 总结的关于花书的经验法则!
在线版阅读地址:
https://jeffmacaluso.github.io/post/DeepLearningRulesOfThumb/
离线地址:
链接:https://pan.baidu.com/s/1eLlJy3xB6Hs0w_Q7bO536g
提取码:7q1d
希望这份资源对你有所帮助~
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未经允许不得转载:红色石头的个人博客 » 深度学习圣经“花书”各章摘要与笔记整理