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重磅！深度学习圣经“花书”核心笔记、代码发布

花匠小妙招
2024-11-24 17:51

《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')