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udlbook
2023-12-10 17:06:17 +00:00
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commit 40fc192198
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8
Notebooks/Chap18/18_1_Diffusion_Encoder.ipynb
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@@ -4,7 +4,7 @@
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyMpC8kgLnXx0XQBtwNAQ4jJ",
"authorship_tag": "ABX9TyNHifufahHPhghaXpcrZ7c5",
"include_colab_link": true
},
"kernelspec": {
@@ -233,7 +233,7 @@
{
"cell_type": "markdown",
"source": [
"Notice that the samples have a tendency to move toward the center. Now let's look at the histogram of the samples at each stage"
"Notice that the samples have a tendencey to move toward the center. Now let's look at the histogram of the samples at each stage"
],
"metadata": {
"id": "SGTYGGevAktz"
@@ -248,7 +248,7 @@
" plt.hist(z_t , bins=np.arange(-3,3, 0.1), density = True)\n",
" ax.set_xlim([-3,3])\n",
" ax.set_ylim([0,1.0])\n",
" ax.set_title('title')\n",
" ax.set_title(title)\n",
" plt.show()"
],
"metadata": {
@@ -418,7 +418,7 @@
" # 1. For each x (value in x_plot_vals):\n",
" # 2. Compute the mean and variance of the diffusion kernel at time t\n",
" # 3. Compute pdf of this Gaussian at every x_plot_val\n",
" # 4. Weight Gaussian by probability at position x and by 0.01 to compensate for bin size\n",
" # 4. Weight Gaussian by probability at position x and by 0.01 to componensate for bin size\n",
" # 5. Accumulate weighted Gaussian in marginal at time t.\n",
" # 6. Multiply result by 0.01 to compensate for bin size\n",
" # Replace this line:\n",