Section 1.1

This is the third notebook.

import sys

assert sys.version_info >= (3, 7)

from packaging import version
import sklearn

assert version.parse(sklearn.__version__) >= version.parse("1.0.1")

import matplotlib.pyplot as plt

plt.rc('font', size=12)
plt.rc('axes', labelsize=14, titlesize=14)
plt.rc('legend', fontsize=12)
plt.rc('xtick', labelsize=10)
plt.rc('ytick', labelsize=10)

import numpy as np

np.random.seed(42)

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression

# Download and prepare the data
data_root = "https://github.com/ageron/data/raw/main/"
lifesat = pd.read_csv(data_root + "lifesat/lifesat.csv")
X = lifesat[["GDP per capita (USD)"]].values
y = lifesat[["Life satisfaction"]].values

# Visualize the data
lifesat.plot(kind='scatter', grid=True,
             x="GDP per capita (USD)", y="Life satisfaction")
plt.axis([23_500, 62_500, 4, 9])
plt.show()

# Select a linear model
model = LinearRegression()

# Train the model
model.fit(X, y)

# Make a prediction for Cyprus
X_new = [[37_655.2]]  # Cyprus' GDP per capita in 2020
print(model.predict(X_new)) # outputs [[6.30165767]]

[[6.30165767]]

# Select a 3-Nearest Neighbors regression model
from sklearn.neighbors import KNeighborsRegressor

model = KNeighborsRegressor(n_neighbors=3)

# Train the model
model.fit(X, y)

# Make a prediction for Cyprus
print(model.predict(X_new)) # outputs [[6.33333333]]

[[6.33333333]]

import wandb
import random

# start a new wandb run to track this script
wandb.init(
    # set the wandb project where this run will be logged
    project="test-project",

    # track hyperparameters and run metadata
    config={
    "learning_rate": 0.02,
    "architecture": "CNN",
    "dataset": "CIFAR-100",
    "epochs": 10,
    }
)

# simulate training
epochs = 10
offset = random.random() / 5
for epoch in range(2, epochs):
    acc = 1 - 2 ** -epoch - random.random() / epoch - offset
    loss = 2 ** -epoch + random.random() / epoch + offset

    # log metrics to wandb
    wandb.log({"acc": acc, "loss": loss})

# [optional] finish the wandb run, necessary in notebooks
wandb.finish()

Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.
wandb: Currently logged in as: kithminrw (kithminrw-ai). Use `wandb login --relogin` to force relogin

Tracking run with wandb version 0.17.0

Run data is saved locally in /Users/kithminrw/Documents/GitHub/ml-nn-zero2hero/notebooks/01/wandb/run-20240515_090928-2pcb5zzn

Syncing run leafy-valley-3 to Weights & Biases (docs)

View project at https://wandb.ai/kithminrw-ai/test-project

View run at https://wandb.ai/kithminrw-ai/test-project/runs/2pcb5zzn

Run history:

acc	▁▄▇██▇▇█
loss	█▇▅▁▃▂▁▁

Run summary:

acc	0.81072
loss	0.1592

View run leafy-valley-3 at: https://wandb.ai/kithminrw-ai/test-project/runs/2pcb5zzn
View project at: https://wandb.ai/kithminrw-ai/test-project
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)

Find logs at: ./wandb/run-20240515_090928-2pcb5zzn/logs