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Merge pull request #150 from yrahal/main

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Fix minor typos in Chapter 6 notebooks
This commit is contained in:
udlbook
2024-02-12 13:11:23 -05:00
committed by GitHub
parent f2a3fab832 435971e3e2
commit 025b677457
5 changed files with 22 additions and 24 deletions
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9
Notebooks/Chap06/6_1_Line_Search.ipynb
View File

@@ -113,7 +113,7 @@
" b = 0.33\n",
" c = 0.66\n",
" d = 1.0\n",
" n_iter =0;\n",
" n_iter = 0\n",
"\n",
" # While we haven't found the minimum closely enough\n",
" while np.abs(b-c) > thresh and n_iter < max_iter:\n",
@@ -131,8 +131,7 @@
"\n",
" print('Iter %d, a=%3.3f, b=%3.3f, c=%3.3f, d=%3.3f'%(n_iter, a,b,c,d))\n",
"\n",
" # Rule #1 If the HEIGHT at point A is less the HEIGHT at points B, C, and D then halve values of B, C, and D\n",
" # i.e. bring them closer to the original point\n",
" # Rule #1 If the HEIGHT at point A is less than the HEIGHT at points B, C, and D then halve values of B, C, and D\n",
" # i.e. bring them closer to the original point\n",
" # TODO REPLACE THE BLOCK OF CODE BELOW WITH THIS RULE\n",
" if (0):\n",
@@ -140,7 +139,7 @@
"\n",
"\n",
" # Rule #2 If the HEIGHT at point b is less than the HEIGHT at point c then\n",
" # then point d becomes point c, and\n",
" # point d becomes point c, and\n",
" # point b becomes 1/3 between a and new d\n",
" # point c becomes 2/3 between a and new d\n",
" # TODO REPLACE THE BLOCK OF CODE BELOW WITH THIS RULE\n",
@@ -148,7 +147,7 @@
" continue;\n",
"\n",
" # Rule #3 If the HEIGHT at point c is less than the HEIGHT at point b then\n",
" # then point a becomes point b, and\n",
" # point a becomes point b, and\n",
" # point b becomes 1/3 between new a and d\n",
" # point c becomes 2/3 between new a and d\n",
" # TODO REPLACE THE BLOCK OF CODE BELOW WITH THIS RULE\n",

8
Notebooks/Chap06/6_2_Gradient_Descent.ipynb
View File

@@ -117,7 +117,7 @@
"id": "QU5mdGvpTtEG"
},
"source": [
"Now lets create compute the sum of squares loss for the training data"
"Now let's compute the sum of squares loss for the training data"
]
},
{
@@ -317,7 +317,7 @@
" b = 0.33 * max_dist\n",
" c = 0.66 * max_dist\n",
" d = 1.0 * max_dist\n",
" n_iter =0;\n",
" n_iter = 0\n",
"\n",
" # While we haven't found the minimum closely enough\n",
" while np.abs(b-c) > thresh and n_iter < max_iter:\n",
@@ -341,7 +341,7 @@
" continue;\n",
"\n",
" # Rule #2 If point b is less than point c then\n",
" # then point d becomes point c, and\n",
" # point d becomes point c, and\n",
" # point b becomes 1/3 between a and new d\n",
" # point c becomes 2/3 between a and new d\n",
" if lossb < lossc:\n",
@@ -351,7 +351,7 @@
" continue\n",
"\n",
" # Rule #2 If point c is less than point b then\n",
" # then point a becomes point b, and\n",
" # point a becomes point b, and\n",
" # point b becomes 1/3 between new a and d\n",
" # point c becomes 2/3 between new a and d\n",
" a = b\n",

12
Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb
View File

@@ -53,7 +53,7 @@
},
"outputs": [],
"source": [
"# Let's create our training data 30 pairs {x_i, y_i}\n",
"# Let's create our training data of 30 pairs {x_i, y_i}\n",
"# We'll try to fit the Gabor model to these data\n",
"data = np.array([[-1.920e+00,-1.422e+01,1.490e+00,-1.940e+00,-2.389e+00,-5.090e+00,\n",
" -8.861e+00,3.578e+00,-6.010e+00,-6.995e+00,3.634e+00,8.743e-01,\n",
@@ -128,7 +128,7 @@
"id": "QU5mdGvpTtEG"
},
"source": [
"Now lets create compute the sum of squares loss for the training data"
"Now let's compute the sum of squares loss for the training data"
]
},
{
@@ -198,7 +198,7 @@
" b = np.floor(my_colormap_vals_dec - r * 256 *256 - g * 256)\n",
" my_colormap = ListedColormap(np.vstack((r,g,b)).transpose()/255.0)\n",
"\n",
" # Make grid of intercept/slope values to plot\n",
" # Make grid of offset/frequency values to plot\n",
" offsets_mesh, freqs_mesh = np.meshgrid(np.arange(-10,10.0,0.1), np.arange(2.5,22.5,0.1))\n",
" loss_mesh = np.zeros_like(freqs_mesh)\n",
" # Compute loss for every set of parameters\n",
@@ -343,7 +343,7 @@
" b = 0.33 * max_dist\n",
" c = 0.66 * max_dist\n",
" d = 1.0 * max_dist\n",
" n_iter =0;\n",
" n_iter = 0\n",
"\n",
" # While we haven't found the minimum closely enough\n",
" while np.abs(b-c) > thresh and n_iter < max_iter:\n",
@@ -367,7 +367,7 @@
" continue;\n",
"\n",
" # Rule #2 If point b is less than point c then\n",
" # then point d becomes point c, and\n",
" # point d becomes point c, and\n",
" # point b becomes 1/3 between a and new d\n",
" # point c becomes 2/3 between a and new d\n",
" if lossb < lossc:\n",
@@ -377,7 +377,7 @@
" continue\n",
"\n",
" # Rule #2 If point c is less than point b then\n",
" # then point a becomes point b, and\n",
" # point a becomes point b, and\n",
" # point b becomes 1/3 between new a and d\n",
" # point c becomes 2/3 between new a and d\n",
" a = b\n",

7
Notebooks/Chap06/6_4_Momentum.ipynb
View File

@@ -61,7 +61,7 @@
{
"cell_type": "code",
"source": [
"# Let's create our training data 30 pairs {x_i, y_i}\n",
"# Let's create our training data of 30 pairs {x_i, y_i}\n",
"# We'll try to fit the Gabor model to these data\n",
"data = np.array([[-1.920e+00,-1.422e+01,1.490e+00,-1.940e+00,-2.389e+00,-5.090e+00,\n",
" -8.861e+00,3.578e+00,-6.010e+00,-6.995e+00,3.634e+00,8.743e-01,\n",
@@ -137,7 +137,7 @@
{
"cell_type": "markdown",
"source": [
"Now lets compute the sum of squares loss for the training data and plot the loss function"
"Now let's compute the sum of squares loss for the training data and plot the loss function"
],
"metadata": {
"id": "QU5mdGvpTtEG"
@@ -160,7 +160,7 @@
" b = np.floor(my_colormap_vals_dec - r * 256 *256 - g * 256)\n",
" my_colormap = ListedColormap(np.vstack((r,g,b)).transpose()/255.0)\n",
"\n",
" # Make grid of intercept/slope values to plot\n",
" # Make grid of offset/frequency values to plot\n",
" offsets_mesh, freqs_mesh = np.meshgrid(np.arange(-10,10.0,0.1), np.arange(2.5,22.5,0.1))\n",
" loss_mesh = np.zeros_like(freqs_mesh)\n",
" # Compute loss for every set of parameters\n",
@@ -365,7 +365,6 @@
"\n",
" # Update the parameters\n",
" phi_all[:,c_step+1:c_step+2] = phi_all[:,c_step:c_step+1] - alpha * momentum\n",
" # Measure loss and draw model every 8th step\n",
"\n",
"loss = compute_loss(data[0,:], data[1,:], model, phi_all[:,c_step+1:c_step+2])\n",
"draw_model(data,model,phi_all[:,c_step+1], \"Iteration %d, loss = %f\"%(c_step+1,loss))\n",

4
Notebooks/Chap06/6_5_Adam.ipynb
View File

@@ -110,7 +110,7 @@
" ax.plot(opt_path[0,:], opt_path[1,:],'-', color='#a0d9d3ff')\n",
" ax.plot(opt_path[0,:], opt_path[1,:],'.', color='#a0d9d3ff',markersize=10)\n",
" ax.set_xlabel(\"$\\phi_{0}$\")\n",
" ax.set_ylabel(\"$\\phi_1}$\")\n",
" ax.set_ylabel(\"$\\phi_{1}$\")\n",
" plt.show()"
],
"metadata": {
@@ -169,7 +169,7 @@
{
"cell_type": "markdown",
"source": [
"Because the function changes much faster in $\\phi_1$ than in $\\phi_0$, there is no great step size to choose. If we set the step size so that it makes sensible progress in the $\\phi_1$, then it takes many iterations to converge. If we set the step size tso that we make sensible progress in the $\\phi_{0}$ direction, then the path oscillates in the $\\phi_1$ direction. \n",
"Because the function changes much faster in $\\phi_1$ than in $\\phi_0$, there is no great step size to choose. If we set the step size so that it makes sensible progress in the $\\phi_1$ direction, then it takes many iterations to converge. If we set the step size so that we make sensible progress in the $\\phi_{0}$ direction, then the path oscillates in the $\\phi_1$ direction. \n",
"\n",
"This motivates Adam. At the core of Adam is the idea that we should just determine which way is downhill along each axis (i.e. left/right for $\\phi_0$ or up/down for $\\phi_1$) and move a fixed distance in that direction."
],