[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -1,15 +1,15 @@
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.0.1
+    rev: v4.5.0
     hooks:
     -   id: check-yaml
     -   id: end-of-file-fixer
     -   id: trailing-whitespace
 -   repo: https://github.com/psf/black
-    rev: 21.11b1
+    rev: 24.2.0
     hooks:
     -   id: black
 -   repo: https://github.com/kynan/nbstripout
-    rev: 0.5.0
+    rev: 0.7.1
     hooks:
     -   id: nbstripout

notebooks/02-jax-idioms/04-optimized-learning.ipynb

@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "frequent-field",
+   "id": "0",
    "metadata": {
     "tags": []
    },
@@ -18,7 +18,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "opened-virgin",
+   "id": "1",
    "metadata": {
     "tags": []
    },
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "lasting-express",
+   "id": "2",
    "metadata": {},
    "source": [
     "# Optimized Learning\n",
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "forward-process",
+   "id": "3",
    "metadata": {},
    "source": [
     "## Autograd to JAX\n",
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "correct-cyprus",
+   "id": "4",
    "metadata": {},
    "source": [
     "## Example: Transforming a function into its derivative\n",
@@ -68,7 +68,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "demanding-opportunity",
+   "id": "5",
    "metadata": {
     "tags": []
    },
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "forty-lindsay",
+   "id": "6",
    "metadata": {},
    "source": [
     "Here's another example using a polynomial function:\n",
@@ -105,7 +105,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "neutral-neighbor",
+   "id": "7",
    "metadata": {
     "tags": []
    },
@@ -128,7 +128,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "steady-bikini",
+   "id": "8",
    "metadata": {},
    "source": [
     "## Using grad to solve minimization problems\n",
@@ -147,7 +147,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "opponent-modification",
+   "id": "9",
    "metadata": {
     "tags": []
    },
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "beautiful-theory",
+   "id": "10",
    "metadata": {},
    "source": [
     "We know from calculus that the sign of the second derivative tells us whether we have a minima or maxima at a point.\n",
@@ -178,7 +178,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "former-syracuse",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -189,15 +189,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "surrounded-plain",
+   "id": "12",
    "metadata": {},
    "source": [
     "Grad is composable an arbitrary number of times. You can keep calling grad as many times as you like."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "brazilian-atlas",
+   "id": "13",
    "metadata": {},
    "source": [
     "## Maximum likelihood estimation\n",
@@ -216,7 +216,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "confidential-sympathy",
+   "id": "14",
    "metadata": {
     "tags": []
    },
@@ -236,7 +236,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "atlantic-excellence",
+   "id": "15",
    "metadata": {},
    "source": [
     "Our estimation task will necessitate calculating the total joint log likelihood of our data under a Gaussian model.\n",
@@ -248,7 +248,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "known-terrain",
+   "id": "16",
    "metadata": {
     "tags": []
    },
@@ -263,7 +263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "terminal-census",
+   "id": "17",
    "metadata": {},
    "source": [
     "If you're wondering why we use `log_sigma` rather than `sigma`, it is a choice made for practical reasons.\n",
@@ -280,7 +280,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dominant-delight",
+   "id": "18",
    "metadata": {
     "tags": []
    },
@@ -293,7 +293,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "equal-brazilian",
+   "id": "19",
    "metadata": {},
    "source": [
     "Now, we can create the gradient function of our negative log likelihood.\n",
@@ -307,7 +307,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "meaning-scanning",
+   "id": "20",
    "metadata": {
     "tags": []
    },
@@ -322,7 +322,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "hourly-miller",
+   "id": "21",
    "metadata": {},
    "source": [
     "Now, we can do the gradient descent step!"
@@ -331,7 +331,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cosmetic-perception",
+   "id": "22",
    "metadata": {
     "tags": []
    },
@@ -347,15 +347,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "defensive-family",
+   "id": "23",
    "metadata": {},
    "source": [
     "And voila! We have gradient descended our way to the maximum likelihood parameters :)."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "constant-account",
+   "id": "24",
    "metadata": {},
    "source": [
     "## Exercise: Where is the gold? It's at the minima!\n",
@@ -368,7 +368,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "focal-climate",
+   "id": "25",
    "metadata": {
     "tags": []
    },
@@ -383,7 +383,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "massive-corps",
+   "id": "26",
    "metadata": {},
    "source": [
     "It should be evident from here that there are two minima in the function.\n",
@@ -398,7 +398,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "opened-beads",
+   "id": "27",
    "metadata": {
     "tags": []
    },
@@ -420,7 +420,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "brown-violation",
+   "id": "28",
    "metadata": {},
    "source": [
     "Now, implement the optimization loop!"
@@ -429,7 +429,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alternative-wisdom",
+   "id": "29",
    "metadata": {
     "tags": []
    },
@@ -450,7 +450,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "alternative-iraqi",
+   "id": "30",
    "metadata": {},
    "source": [
     "## Exercise: programming a robot that only moves along one axis\n",
@@ -464,7 +464,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "operational-advantage",
+   "id": "31",
    "metadata": {
     "tags": []
    },
@@ -490,7 +490,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ecological-asian",
+   "id": "32",
    "metadata": {},
    "source": [
     "For your reference we have the function plotted below."
@@ -499,7 +499,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "convenient-optics",
+   "id": "33",
    "metadata": {
     "tags": []
    },
@@ -531,7 +531,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "loaded-labor",
+   "id": "34",
    "metadata": {},
    "outputs": [],
    "source": []

src/dl_workshop/answers.py

@@ -1,6 +1,7 @@
 """
 Answers to the main tutorial notebooks.
 """
+
 import jax.numpy as np
 import numpy.random as npr
 from jax import grad
@@ -81,7 +82,7 @@ def logistic_loss(params, model, x, y):
 
 
 def f(w):
-    return w ** 2 + 3 * w - 5
+    return w**2 + 3 * w - 5
 
 
 def df(w):

src/dl_workshop/jax_idioms.py

@@ -93,7 +93,7 @@ def randomness_ex_3(key, num_realizations: int, grw_draw: Callable):
 
 def goldfield(x, y):
     """All credit to https://www.analyzemath.com/calculus/multivariable/maxima_minima.html for this function."""
-    return (2 * x ** 2) - (4 * x * y) + (y ** 4 + 2)
+    return (2 * x**2) - (4 * x * y) + (y**4 + 2)
 
 
 def grad_ex_1():

src/setup.py

@@ -1,4 +1,5 @@
 """Setup script."""
+
 from setuptools import find_packages, setup
 
 setup(name="dl_workshop", version="0.1", packages=find_packages())