ch13_part2.py

# coding: utf-8


import sys
from python_environment_check import check_packages
import numpy as np
import torch
import torch.nn as nn
import pandas as pd
import sklearn
import sklearn.model_selection
from torch.nn.functional import one_hot
from torch.utils.data import DataLoader, TensorDataset
import torchvision 
from torchvision import transforms 

# # Machine Learning with PyTorch and Scikit-Learn  
# # -- Code Examples

# ## Package version checks

# Add folder to path in order to load from the check_packages.py script:



sys.path.insert(0, '..')


# Check recommended package versions:





d = {
    'numpy': '1.21.2',
    'pandas': '1.3.2',
    'sklearn': '1.0',
    'torch': '1.8',
    'torchvision': '0.9.0'
}
check_packages(d)


# # Chapter 13: Going Deeper -- the Mechanics of PyTorch (Part 2/3)

# **Outline**
# 
# - [Project one - predicting the fuel efficiency of a car](#Project-one----predicting-the-fuel-efficiency-of-a-car)
#   - [Working with feature columns](#Working-with-feature-columns)
#   - [Training a DNN regression model](#Training-a-DNN-regression-model)
# - [Project two - classifying MNIST handwritten digits](#Project-two----classifying-MNIST-handwritten-digits)






# ## Project one - predicting the fuel efficiency of a car
# 

# ### Working with feature columns
# 
# 







url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'
column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',
                'Acceleration', 'Model Year', 'Origin']

df = pd.read_csv(url, names=column_names,
                 na_values = "?", comment='\t',
                 sep=" ", skipinitialspace=True)

df.tail()




print(df.isna().sum())

df = df.dropna()
df = df.reset_index(drop=True)
df.tail()






df_train, df_test = sklearn.model_selection.train_test_split(df, train_size=0.8, random_state=1)
train_stats = df_train.describe().transpose()
train_stats




numeric_column_names = ['Cylinders', 'Displacement', 'Horsepower', 'Weight', 'Acceleration']

df_train_norm, df_test_norm = df_train.copy(), df_test.copy()

for col_name in numeric_column_names:
    mean = train_stats.loc[col_name, 'mean']
    std  = train_stats.loc[col_name, 'std']
    df_train_norm.loc[:, col_name] = (df_train_norm.loc[:, col_name] - mean)/std
    df_test_norm.loc[:, col_name] = (df_test_norm.loc[:, col_name] - mean)/std
    
df_train_norm.tail()




boundaries = torch.tensor([73, 76, 79])
 
v = torch.tensor(df_train_norm['Model Year'].values)
df_train_norm['Model Year Bucketed'] = torch.bucketize(v, boundaries, right=True)

v = torch.tensor(df_test_norm['Model Year'].values)
df_test_norm['Model Year Bucketed'] = torch.bucketize(v, boundaries, right=True)

numeric_column_names.append('Model Year Bucketed')






total_origin = len(set(df_train_norm['Origin']))

origin_encoded = one_hot(torch.from_numpy(df_train_norm['Origin'].values) % total_origin)
x_train_numeric = torch.tensor(df_train_norm[numeric_column_names].values)
x_train = torch.cat([x_train_numeric, origin_encoded], 1).float()
 
origin_encoded = one_hot(torch.from_numpy(df_test_norm['Origin'].values) % total_origin)
x_test_numeric = torch.tensor(df_test_norm[numeric_column_names].values)
x_test = torch.cat([x_test_numeric, origin_encoded], 1).float()




y_train = torch.tensor(df_train_norm['MPG'].values).float()
y_test = torch.tensor(df_test_norm['MPG'].values).float()






train_ds = TensorDataset(x_train, y_train)
batch_size = 8
torch.manual_seed(1)
train_dl = DataLoader(train_ds, batch_size, shuffle=True)




hidden_units = [8, 4]
input_size = x_train.shape[1]

all_layers = []
for hidden_unit in hidden_units:
    layer = nn.Linear(input_size, hidden_unit)
    all_layers.append(layer)
    all_layers.append(nn.ReLU())
    input_size = hidden_unit

all_layers.append(nn.Linear(hidden_units[-1], 1))

model = nn.Sequential(*all_layers)

model




loss_fn = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)




torch.manual_seed(1)
num_epochs = 200
log_epochs = 20 
for epoch in range(num_epochs):
    loss_hist_train = 0
    for x_batch, y_batch in train_dl:
        pred = model(x_batch)[:, 0]
        loss = loss_fn(pred, y_batch)
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()
        loss_hist_train += loss.item()
    if epoch % log_epochs==0:
        print(f'Epoch {epoch}  Loss {loss_hist_train/len(train_dl):.4f}')
 




with torch.no_grad():
    pred = model(x_test.float())[:, 0]
    loss = loss_fn(pred, y_test)
    print(f'Test MSE: {loss.item():.4f}')
    print(f'Test MAE: {nn.L1Loss()(pred, y_test).item():.4f}')


# ## Project two - classifying MNIST hand-written digits





image_path = './'
transform = transforms.Compose([transforms.ToTensor()])

mnist_train_dataset = torchvision.datasets.MNIST(root=image_path, 
                                           train=True, 
                                           transform=transform, 
                                           download=True)
mnist_test_dataset = torchvision.datasets.MNIST(root=image_path, 
                                           train=False, 
                                           transform=transform, 
                                           download=False)
 
batch_size = 64
torch.manual_seed(1)
train_dl = DataLoader(mnist_train_dataset, batch_size, shuffle=True)




hidden_units = [32, 16]
image_size = mnist_train_dataset[0][0].shape
input_size = image_size[0] * image_size[1] * image_size[2]

all_layers = [nn.Flatten()]
for hidden_unit in hidden_units:
    layer = nn.Linear(input_size, hidden_unit)
    all_layers.append(layer)
    all_layers.append(nn.ReLU())
    input_size = hidden_unit

all_layers.append(nn.Linear(hidden_units[-1], 10))
model = nn.Sequential(*all_layers)

model




loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

torch.manual_seed(1)
num_epochs = 20
for epoch in range(num_epochs):
    accuracy_hist_train = 0
    for x_batch, y_batch in train_dl:
        pred = model(x_batch)
        loss = loss_fn(pred, y_batch)
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()
        is_correct = (torch.argmax(pred, dim=1) == y_batch).float()
        accuracy_hist_train += is_correct.sum()
    accuracy_hist_train /= len(train_dl.dataset)
    print(f'Epoch {epoch}  Accuracy {accuracy_hist_train:.4f}')




pred = model(mnist_test_dataset.data / 255.)
is_correct = (torch.argmax(pred, dim=1) == mnist_test_dataset.targets).float()
print(f'Test accuracy: {is_correct.mean():.4f}') 
 


# ---
# 
# Readers may ignore the next cell.