Search is not available for this dataset
repo_id stringlengths 12 110 | file_path stringlengths 24 164 | content stringlengths 3 89.3M | __index_level_0__ int64 0 0 |
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public_repos/dl-fundamentals/unit02-pytorch-tensors | public_repos/dl-fundamentals/unit02-pytorch-tensors/2.6-revisiting-perceptron/perceptron-tensor.ipynb | # !conda install numpy pandas matplotlib --yes# !conda install watermark# !pip install torch%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchimport pandas as pd
df = pd.read_csv("perceptron_toydata-truncated.txt", sep="\t")
dfX_train = df[["x1", "x2"]].values
y_train = df["label"].values# New:
import... | 0 |
public_repos/dl-fundamentals/unit02-pytorch-tensors | public_repos/dl-fundamentals/unit02-pytorch-tensors/2.6-revisiting-perceptron/perceptron_toydata-truncated.txt | x1 x2 label
0.77 -1.14 0
-0.33 1.44 0
0.91 -3.07 0
-0.37 -1.91 0
-0.63 -1.53 0
0.39 -1.99 0
-0.49 -2.74 0
-0.68 -1.52 0
-0.10 -3.43 0
-0.05 -1.95 0
3.88 0.65 1
0.73 2.97 1
0.83 3.94 1
1.59 1.25 1
1.14 3.91 1
1.73 2.80 1
1.31 1.85 1
1.56 3.85 1
1.23 2.54 1
1.33 2.03 1
| 0 |
public_repos/dl-fundamentals/unit02-pytorch-tensors/2.6-revisiting-perceptron | public_repos/dl-fundamentals/unit02-pytorch-tensors/2.6-revisiting-perceptron/images/perceptron-part2.ipynb | # !conda install numpy pandas matplotlib --yes# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlibimport pandas as pd
df = pd.read_csv("perceptron_toydata-truncated.txt", sep="\t")
dfX_train = df[["x1", "x2"]].values
y_train = df["label"].valuesX_trainX_train.shapey_trainy_train.shape... | 0 |
public_repos/dl-fundamentals/unit02-pytorch-tensors/2.6-revisiting-perceptron | public_repos/dl-fundamentals/unit02-pytorch-tensors/2.6-revisiting-perceptron/images/perceptron-part3.ipynb | # !conda install numpy pandas matplotlib --yes# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlibimport pandas as pd
df = pd.read_csv("perceptron_toydata-truncated.txt", sep="\t")
dfX_train = df[["x1", "x2"]].values
y_train = df["label"].valuesX_trainX_train.shapey_trainy_train.shape... | 0 |
public_repos/dl-fundamentals/unit02-pytorch-tensors | public_repos/dl-fundamentals/unit02-pytorch-tensors/2.2-tensors/torch-tensors.ipynb | a = 1.
print(a)import torch
a = torch.tensor(1.)
print(a)print(a.shape)a = [1., 2., 3.]
print(a)a = torch.tensor([1., 2., 3.])
print(a)print(a.shape)a = torch.tensor([[1., 2., 3.],
[2., 3., 4.]])
a.shapea = torch.tensor([[[1., 2., 3.],
[2., 3., 4.]],
[[5., 6., 7.]... | 0 |
public_repos/dl-fundamentals/unit02-pytorch-tensors/exercises | public_repos/dl-fundamentals/unit02-pytorch-tensors/exercises/2_perceptron-matmul/exercise_2_perceptron-matmul.ipynb | import torch
class Perceptron:
def __init__(self):
self.weights = torch.tensor([2.86, 1.98])
self.bias = torch.tensor(-3.0)
def forward(self, x):
weighted_sum_z = torch.dot(x, self.weights) + self.bias
if weighted_sum_z > 0.0:
prediction = torch.tensor(1.0)
... | 0 |
public_repos/dl-fundamentals/unit02-pytorch-tensors/exercises | public_repos/dl-fundamentals/unit02-pytorch-tensors/exercises/2_perceptron-matmul/README.md | # EXERCISES
## Exercise 2: Make the Perceptron More Efficient Using Matrix Multiplication
In this exercise, you are going to modify the `Perceptron` class such that it creates the predictions for multiple input examples all at once.
Link to exercise notebook: [https://github.com/Lightning-AI/dl-fundamentals/blob/ma... | 0 |
public_repos/dl-fundamentals/unit02-pytorch-tensors/exercises/solutions | public_repos/dl-fundamentals/unit02-pytorch-tensors/exercises/solutions/unit02_exercise_1/solution_ex_1.ipynb | # !conda install numpy pandas matplotlib --yes# !conda install watermark# !pip install torch%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchimport pandas as pd
df = pd.read_csv("perceptron_toydata-truncated.txt", sep="\t")
dfX_train = df[["x1", "x2"]].values
y_train = df["label"].values# New:
import... | 0 |
public_repos/dl-fundamentals/unit02-pytorch-tensors/exercises/solutions | public_repos/dl-fundamentals/unit02-pytorch-tensors/exercises/solutions/unit02_exercise_2/solution_ex2.ipynb | import torch
class Perceptron:
def __init__(self):
self.weights = torch.tensor([2.86, 1.98])
self.bias = torch.tensor(-3.0)
def forward(self, x):
weighted_sum_z = torch.dot(x, self.weights) + self.bias
if weighted_sum_z > 0.0:
prediction = torch.tensor(1.0)
... | 0 |
public_repos/dl-fundamentals/unit02-pytorch-tensors/exercises | public_repos/dl-fundamentals/unit02-pytorch-tensors/exercises/1_torch-where/perceptron_toydata-truncated.txt | x1 x2 label
0.77 -1.14 0
-0.33 1.44 0
0.91 -3.07 0
-0.37 -1.91 0
-0.63 -1.53 0
0.39 -1.99 0
-0.49 -2.74 0
-0.68 -1.52 0
-0.10 -3.43 0
-0.05 -1.95 0
3.88 0.65 1
0.73 2.97 1
0.83 3.94 1
1.59 1.25 1
1.14 3.91 1
1.73 2.80 1
1.31 1.85 1
1.56 3.85 1
1.23 2.54 1
1.33 2.03 1
| 0 |
public_repos/dl-fundamentals/unit02-pytorch-tensors/exercises | public_repos/dl-fundamentals/unit02-pytorch-tensors/exercises/1_torch-where/README.md | # EXERCISES
## Exercise 1: Introducing More PyTorch Functions To Make Your Code More Efficient
The goal of this exercise is to simplify the code implementation
of the `Perceptron` class. For this, we are going to use the `torch.where` function for the `forward` method.
Your task is to learn about `torch.where` [us... | 0 |
public_repos/dl-fundamentals/unit02-pytorch-tensors/exercises | public_repos/dl-fundamentals/unit02-pytorch-tensors/exercises/1_torch-where/exercise_1_torch-where.ipynb | # !conda install numpy pandas matplotlib --yes# !conda install watermark# !pip install torch%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchimport pandas as pd
df = pd.read_csv("perceptron_toydata-truncated.txt", sep="\t")
dfX_train = df[["x1", "x2"]].values
y_train = df["label"].values# New:
import... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets | public_repos/dl-fundamentals/unit04-multilayer-nets/4.5-mlp-regression/4.5-mlp-regression-part2.ipynb | %load_ext watermark
%watermark -v -p torch,matplotlib --condaimport torch
X_train = torch.tensor(
[258.0, 270.0, 294.0, 320.0, 342.0, 368.0, 396.0, 446.0, 480.0, 586.0]
).view(-1, 1)
y_train = torch.tensor(
[236.4, 234.4, 252.8, 298.6, 314.2, 342.2, 360.8, 368.0, 391.2, 390.8]
)import matplotlib.pyplot as plt... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets | public_repos/dl-fundamentals/unit04-multilayer-nets/4.1-softmax/loss-cheatsheet.md | # PyTorch Loss Function Cheatsheet
PyTorch Loss-Input Confusion (Cheatsheet)
- [`torch.nn.functional.binary_cross_entropy`](https://pytorch.org/docs/stable/generated/torch.nn.functional.binary_cross_entropy.html) takes logistic sigmoid values as inputs
- [`torch.nn.functional.binary_cross_entropy_with_logits`](https:... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets | public_repos/dl-fundamentals/unit04-multilayer-nets/4.4-dataloaders/4.4-dataloaders-part4-define-and-run.ipynb | %load_ext watermark
%watermark -v -p matplotlib,numpy,pandas,torchvision,torch --condaimport os
import matplotlib.pyplot as plt
import pandas as pd
from PIL import Image
from torch.utils.data import Dataset
class MyDataset(Dataset):
def __init__(self, csv_path, img_dir, transform=None):
df = pd.read_cs... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets | public_repos/dl-fundamentals/unit04-multilayer-nets/4.4-dataloaders/4.4-dataloaders-part3-download-and-prep.ipynb | # !pip install gitpython%load_ext watermark
%watermark -v -p git,pandas --condaimport os
from git import Repo
if not os.path.exists("mnist-pngs"):
Repo.clone_from("https://github.com/rasbt/mnist-pngs", "mnist-pngs")import pandas as pd
df_train = pd.read_csv('mnist-pngs/train.csv')
df_train.head()df_test = pd.read... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets | public_repos/dl-fundamentals/unit04-multilayer-nets/4.4-dataloaders/4.4-dataloaders-part4-define-and-run.py | import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torchvision.utils as vutils
from PIL import Image
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms
from watermark import watermark
class MyDataset(Dataset):
def __init__(self, csv_path, img_... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets/4.3-mlp-pytorch | public_repos/dl-fundamentals/unit04-multilayer-nets/4.3-mlp-pytorch/4.3-mlp-pytorch-part3-5-mnist/4.3-mlp-pytorch-part3-mnist.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch torchvision torchaudio# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchfrom torch.utils.data import DataLoader
from torchvision import datasets, transforms
train_dataset = datasets.MNIST(
root="./mnist", t... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets/4.3-mlp-pytorch | public_repos/dl-fundamentals/unit04-multilayer-nets/4.3-mlp-pytorch/4.3-mlp-pytorch-part3-5-mnist/4.3-mlp-pytorch-part5-mnist.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch torchvision torchaudio# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchfrom torch.utils.data import DataLoader
from torchvision import datasets, transforms
train_dataset = datasets.MNIST(
root="./mnist", t... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets/4.3-mlp-pytorch | public_repos/dl-fundamentals/unit04-multilayer-nets/4.3-mlp-pytorch/4.3-mlp-pytorch-part3-5-mnist/helper_plotting.py | import os
import matplotlib.pyplot as plt
import numpy as np
import torch
def plot_training_loss(
minibatch_loss_list,
num_epochs,
iter_per_epoch,
results_dir=None,
averaging_iterations=100,
):
plt.figure()
ax1 = plt.subplot(1, 1, 1)
ax1.plot(
range(len(minibatch_loss_list)),... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets/4.3-mlp-pytorch | public_repos/dl-fundamentals/unit04-multilayer-nets/4.3-mlp-pytorch/4.3-mlp-pytorch-part3-5-mnist/4.3-mlp-pytorch-part4-mnist.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch torchvision torchaudio# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchfrom torch.utils.data import DataLoader
from torchvision import datasets, transforms
train_dataset = datasets.MNIST(
root="./mnist", t... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets/4.3-mlp-pytorch | public_repos/dl-fundamentals/unit04-multilayer-nets/4.3-mlp-pytorch/4.3-mlp-pytorch-part1-2-xor/4.3-mlp-pytorch-part2-xor.ipynb | # !conda install numpy pandas matplotlib scikit-learn --yes# !pip install torch torchvision torchaudio# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torch,scikit-learn --condaimport pandas as pd
df = pd.read_csv("xor.csv")
df# !pip install scikit-learnX = df[["x1", "x2"]].values... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets/4.3-mlp-pytorch | public_repos/dl-fundamentals/unit04-multilayer-nets/4.3-mlp-pytorch/4.3-mlp-pytorch-part1-2-xor/4.3-mlp-pytorch-part1-xor.ipynb | # !conda install numpy pandas matplotlib scikit-learn --yes# !pip install torch torchvision torchaudio# !conda install watermark --yes%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torch,scikit-learn --condaimport pandas as pd
df = pd.read_csv("xor.csv")
df# !pip install scikit-learnX = df[["x1", "x2"]].... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets/4.3-mlp-pytorch | public_repos/dl-fundamentals/unit04-multilayer-nets/4.3-mlp-pytorch/4.3-mlp-pytorch-part1-2-xor/xor.csv | x1,x2,class label
0.7813059935422636,1.062983772073353,0
-1.060523847336408,-1.0955499996814952,0
0.6321253589114496,0.6740281575632703,0
-1.4247121071777453,0.5352029348696286,1
1.383160687312169,1.368509769346175,0
0.9454882801858628,-0.049414190613787885,1
0.9071847956199112,-1.024796266169805,1
-0.9775608440170122,... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets/exercises | public_repos/dl-fundamentals/unit04-multilayer-nets/exercises/1_changing-layers/exercise_1_changing-layers.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch torchvision torchaudio# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchfrom torch.utils.data import DataLoader
from torchvision import datasets, transforms
train_dataset = datasets.MNIST(
root="./mnist", t... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets/exercises | public_repos/dl-fundamentals/unit04-multilayer-nets/exercises/1_changing-layers/README.md | # EXERCISES
## Exercise 1: Changing the Number of Layers
In this exercise, we are toying around with the multilayer perceptron architecture from Unit 4.3.
In Unit 4.3, we fit the following multilayer perceptron on the MNIST dataset:
```python
class PyTorchMLP(torch.nn.Module):
def __init__(self, num_features,... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets/exercises | public_repos/dl-fundamentals/unit04-multilayer-nets/exercises/1_changing-layers/helper_plotting.py | import os
import matplotlib.pyplot as plt
import numpy as np
import torch
def plot_training_loss(
minibatch_loss_list,
num_epochs,
iter_per_epoch,
results_dir=None,
averaging_iterations=100,
):
plt.figure()
ax1 = plt.subplot(1, 1, 1)
ax1.plot(
range(len(minibatch_loss_list)),... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets/exercises/solutions | public_repos/dl-fundamentals/unit04-multilayer-nets/exercises/solutions/exercise_2/exercise_2_sol_fashion-mnist.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch torchvision torchaudio# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchfrom torch.utils.data import DataLoader
from torchvision import datasets, transforms
def load_mnist(path, kind='train'):
import os
... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets/exercises/solutions | public_repos/dl-fundamentals/unit04-multilayer-nets/exercises/solutions/exercise_2/helper_plotting.py | import os
import matplotlib.pyplot as plt
import numpy as np
import torch
def plot_training_loss(
minibatch_loss_list,
num_epochs,
iter_per_epoch,
results_dir=None,
averaging_iterations=100,
):
plt.figure()
ax1 = plt.subplot(1, 1, 1)
ax1.plot(
range(len(minibatch_loss_list)),... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets/exercises/solutions | public_repos/dl-fundamentals/unit04-multilayer-nets/exercises/solutions/exercise_1/ex1_sol1_short.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch torchvision torchaudio# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchfrom torch.utils.data import DataLoader
from torchvision import datasets, transforms
train_dataset = datasets.MNIST(
root="./mnist", t... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets/exercises/solutions | public_repos/dl-fundamentals/unit04-multilayer-nets/exercises/solutions/exercise_1/ex1_sol2_deep.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch torchvision torchaudio# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchfrom torch.utils.data import DataLoader
from torchvision import datasets, transforms
train_dataset = datasets.MNIST(
root="./mnist", t... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets/exercises/solutions | public_repos/dl-fundamentals/unit04-multilayer-nets/exercises/solutions/exercise_1/helper_plotting.py | import os
import matplotlib.pyplot as plt
import numpy as np
import torch
def plot_training_loss(
minibatch_loss_list,
num_epochs,
iter_per_epoch,
results_dir=None,
averaging_iterations=100,
):
plt.figure()
ax1 = plt.subplot(1, 1, 1)
ax1.plot(
range(len(minibatch_loss_list)),... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets/exercises | public_repos/dl-fundamentals/unit04-multilayer-nets/exercises/2_fashion-mnist/README.md | # EXERCISES
## Exercise 2: Implementing a Custom Dataset Class for Fashion MNIST
In this exercise, we are going to train the multilayer perceptron from Unit 4.3 on a new dataset, [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist), based on the PyTorch Dataset concepts introduced in Unit 4.4.
Fashion M... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets/exercises | public_repos/dl-fundamentals/unit04-multilayer-nets/exercises/2_fashion-mnist/helper_plotting.py | import os
import matplotlib.pyplot as plt
import numpy as np
import torch
def plot_training_loss(
minibatch_loss_list,
num_epochs,
iter_per_epoch,
results_dir=None,
averaging_iterations=100,
):
plt.figure()
ax1 = plt.subplot(1, 1, 1)
ax1.plot(
range(len(minibatch_loss_list)),... | 0 |
public_repos/dl-fundamentals/unit04-multilayer-nets/exercises | public_repos/dl-fundamentals/unit04-multilayer-nets/exercises/2_fashion-mnist/exercise_2_fashion-mnist.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch torchvision torchaudio# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchfrom torch.utils.data import DataLoader
from torchvision import datasets, transforms
def load_mnist(path, kind='train'):
import os
... | 0 |
public_repos/dl-fundamentals/unit03-pytorch-training | public_repos/dl-fundamentals/unit03-pytorch-training/3.6-logreg-in-pytorch/logreg-part3.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch torchvision torchaudio# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchimport pandas as pd
df = pd.read_csv("perceptron_toydata-truncated.txt", sep="\t")
dfX_train = df[["x1", "x2"]].values
y_train = df["label... | 0 |
public_repos/dl-fundamentals/unit03-pytorch-training | public_repos/dl-fundamentals/unit03-pytorch-training/3.6-logreg-in-pytorch/logreg-part1.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch torchvision torchaudio# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchimport pandas as pd
df = pd.read_csv("perceptron_toydata-truncated.txt", sep="\t")
dfX_train = df[["x1", "x2"]].values
y_train = df["label... | 0 |
public_repos/dl-fundamentals/unit03-pytorch-training | public_repos/dl-fundamentals/unit03-pytorch-training/3.6-logreg-in-pytorch/logreg-part2.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch torchvision torchaudio# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchimport pandas as pd
df = pd.read_csv("perceptron_toydata-truncated.txt", sep="\t")
dfX_train = df[["x1", "x2"]].values
y_train = df["label... | 0 |
public_repos/dl-fundamentals/unit03-pytorch-training | public_repos/dl-fundamentals/unit03-pytorch-training/3.6-logreg-in-pytorch/perceptron_toydata-truncated.txt | x1 x2 label
0.77 -1.14 0
-0.33 1.44 0
0.91 -3.07 0
-0.37 -1.91 0
-0.63 -1.53 0
0.39 -1.99 0
-0.49 -2.74 0
-0.68 -1.52 0
-0.10 -3.43 0
-0.05 -1.95 0
3.88 0.65 1
0.73 2.97 1
0.83 3.94 1
1.59 1.25 1
1.14 3.91 1
1.73 2.80 1
1.31 1.85 1
1.56 3.85 1
1.23 2.54 1
1.33 2.03 1
| 0 |
public_repos/dl-fundamentals/unit03-pytorch-training/exercises/solutions | public_repos/dl-fundamentals/unit03-pytorch-training/exercises/solutions/unit03_exercise_2/data_banknote_authentication.txt | 3.6216,8.6661,-2.8073,-0.44699,0
4.5459,8.1674,-2.4586,-1.4621,0
3.866,-2.6383,1.9242,0.10645,0
3.4566,9.5228,-4.0112,-3.5944,0
0.32924,-4.4552,4.5718,-0.9888,0
4.3684,9.6718,-3.9606,-3.1625,0
3.5912,3.0129,0.72888,0.56421,0
2.0922,-6.81,8.4636,-0.60216,0
3.2032,5.7588,-0.75345,-0.61251,0
1.5356,9.1772,-2.2718,-0.73535... | 0 |
public_repos/dl-fundamentals/unit03-pytorch-training/exercises/solutions | public_repos/dl-fundamentals/unit03-pytorch-training/exercises/solutions/unit03_exercise_2/solution_ex_2.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchimport pandas as pddf = pd.read_csv("data_banknote_authentication.txt", header=None)
df.head()X_features = df[[0, 1, 2, 3]].values
y_labels = df[4].valuesX_featur... | 0 |
public_repos/dl-fundamentals/unit03-pytorch-training/exercises/solutions | public_repos/dl-fundamentals/unit03-pytorch-training/exercises/solutions/unit03_exercise_1/data_banknote_authentication.txt | 3.6216,8.6661,-2.8073,-0.44699,0
4.5459,8.1674,-2.4586,-1.4621,0
3.866,-2.6383,1.9242,0.10645,0
3.4566,9.5228,-4.0112,-3.5944,0
0.32924,-4.4552,4.5718,-0.9888,0
4.3684,9.6718,-3.9606,-3.1625,0
3.5912,3.0129,0.72888,0.56421,0
2.0922,-6.81,8.4636,-0.60216,0
3.2032,5.7588,-0.75345,-0.61251,0
1.5356,9.1772,-2.2718,-0.73535... | 0 |
public_repos/dl-fundamentals/unit03-pytorch-training/exercises/solutions | public_repos/dl-fundamentals/unit03-pytorch-training/exercises/solutions/unit03_exercise_1/solution_ex_1.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchimport pandas as pddf = pd.read_csv("data_banknote_authentication.txt", header=None)
df.head()X_features = df[[0, 1, 2, 3]].values
y_labels = df[4].valuesX_featur... | 0 |
public_repos/dl-fundamentals/unit03-pytorch-training/exercises | public_repos/dl-fundamentals/unit03-pytorch-training/exercises/2_standardization/data_banknote_authentication.txt | 3.6216,8.6661,-2.8073,-0.44699,0
4.5459,8.1674,-2.4586,-1.4621,0
3.866,-2.6383,1.9242,0.10645,0
3.4566,9.5228,-4.0112,-3.5944,0
0.32924,-4.4552,4.5718,-0.9888,0
4.3684,9.6718,-3.9606,-3.1625,0
3.5912,3.0129,0.72888,0.56421,0
2.0922,-6.81,8.4636,-0.60216,0
3.2032,5.7588,-0.75345,-0.61251,0
1.5356,9.1772,-2.2718,-0.73535... | 0 |
public_repos/dl-fundamentals/unit03-pytorch-training/exercises | public_repos/dl-fundamentals/unit03-pytorch-training/exercises/2_standardization/exercise_2_standardization.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchimport pandas as pddf = pd.read_csv("data_banknote_authentication.txt", header=None)
df.head()X_features = df[[0, 1, 2, 3]].values
y_labels = df[4].valuesX_featur... | 0 |
public_repos/dl-fundamentals/unit03-pytorch-training/exercises | public_repos/dl-fundamentals/unit03-pytorch-training/exercises/2_standardization/README.md | # EXERCISES
## Exercise 2: Standardization
This exercise is an extension of Exercise 1. Here, the goal is to add code to standardize the features such that they have a mean of 0 and a standard deviation of 1 as discussed in Unit 3.7.
Link to exercise notebook: [exercise_2_standardization.ipynb](https://github.com/L... | 0 |
public_repos/dl-fundamentals/unit03-pytorch-training/exercises | public_repos/dl-fundamentals/unit03-pytorch-training/exercises/1_banknotes/data_banknote_authentication.txt | 3.6216,8.6661,-2.8073,-0.44699,0
4.5459,8.1674,-2.4586,-1.4621,0
3.866,-2.6383,1.9242,0.10645,0
3.4566,9.5228,-4.0112,-3.5944,0
0.32924,-4.4552,4.5718,-0.9888,0
4.3684,9.6718,-3.9606,-3.1625,0
3.5912,3.0129,0.72888,0.56421,0
2.0922,-6.81,8.4636,-0.60216,0
3.2032,5.7588,-0.75345,-0.61251,0
1.5356,9.1772,-2.2718,-0.73535... | 0 |
public_repos/dl-fundamentals/unit03-pytorch-training/exercises | public_repos/dl-fundamentals/unit03-pytorch-training/exercises/1_banknotes/exercise_1_banknotes.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchimport pandas as pddf = pd.read_csv("data_banknote_authentication.txt", header=None)
df.head()X_features = df[[0, 1, 2, 3]].values
y_labels = df[4].valuesX_featur... | 0 |
public_repos/dl-fundamentals/unit03-pytorch-training/exercises | public_repos/dl-fundamentals/unit03-pytorch-training/exercises/1_banknotes/README.md | # EXERCISES
## Exercise 1: Banknote Authentication
In this exercise, we are applying logistic regression to a banknote authentication dataset to distinguish between genuine and forged bank notes.
**The dataset consists of 1372 examples and 4 features for binary classification.** The features are
1. variance of a ... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips | public_repos/dl-fundamentals/unit06-dl-tips/6.1-checkpointing/shared_utilities.py | import lightning as L
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
import torch.nn.functional as F
import torchmetrics
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader, Dataset
class Lig... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips | public_repos/dl-fundamentals/unit06-dl-tips/6.1-checkpointing/6.1-part2-inspect-dataset.ipynb | # !conda install jupyterlab numpy pandas matplotlib watermark sklearn --yes# !pip install torch torchvision torchaudio# !pip install lightning%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torch,lightning,scikit-learn --condafrom shared_utilities import CustomDataModule
dm = CustomDataModule()
dm.setup("... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips | public_repos/dl-fundamentals/unit06-dl-tips/6.1-checkpointing/6.1-part3-checkpointing.ipynb | %load_ext watermark
%watermark -p torch,lightningimport lightning as L
import torch
from lightning.pytorch.loggers import CSVLogger
from shared_utilities import CustomDataModule, LightningModelclass PyTorchMLP(torch.nn.Module):
def __init__(self, num_features, num_classes):
super().__init__()
self... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips | public_repos/dl-fundamentals/unit06-dl-tips/6.2-learning-rates/6.2-part5-0-no-scheduler.ipynb | %load_ext watermark
%watermark -p torch,lightning --condaimport lightning as L
import torch
from lightning.pytorch.loggers import CSVLogger
from shared_utilities import CustomDataModule, LightningModel, PyTorchMLPtorch.manual_seed(123)
dm = CustomDataModule()
pytorch_model = PyTorchMLP(num_features=100, num_classes=2... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips | public_repos/dl-fundamentals/unit06-dl-tips/6.2-learning-rates/shared_utilities.py | import lightning as L
import numpy as np
import torch
import torch.nn.functional as F
import torchmetrics
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader, Dataset
class PyTorchMLP(torch.nn.Module):
def __init__(self, num... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips | public_repos/dl-fundamentals/unit06-dl-tips/6.2-learning-rates/6.2-part5-2-scheduler-plateau.ipynb | %load_ext watermark
%watermark -p torch,lightning --condaimport lightning as L
import torch
import torch.nn.functional as F
import torchmetrics
from lightning.pytorch.loggers import CSVLogger
from shared_utilities import CustomDataModule, PyTorchMLPclass LightningModel(L.LightningModule):
def __init__(self, model,... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips | public_repos/dl-fundamentals/unit06-dl-tips/6.2-learning-rates/6.2-part5-3-scheduler-cosine.ipynb | %load_ext watermark
%watermark -p torch,lightningimport lightning as L
import torch
import torch.nn.functional as F
import torchmetrics
from lightning.pytorch.loggers import CSVLogger
from shared_utilities import CustomDataModule, PyTorchMLPnum_epochs = 100import matplotlib.pyplot as plt
model = torch.nn.Linear(1, 1)... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips | public_repos/dl-fundamentals/unit06-dl-tips/6.2-learning-rates/6.2-part3-learning-rates-finder.ipynb | %load_ext watermark
%watermark -p torch,lightning --condaimport lightning as L
import torch
from lightning.pytorch.loggers import CSVLogger
from shared_utilities import CustomDataModule, LightningModel, PyTorchMLPtorch.manual_seed(123)
dm = CustomDataModule()
pytorch_model = PyTorchMLP(num_features=100, num_classes=2... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips | public_repos/dl-fundamentals/unit06-dl-tips/6.2-learning-rates/6.2-part5-1-scheduler-step.ipynb | %load_ext watermark
%watermark -p torch,lightning --condaimport lightning as L
import torch
import torch.nn.functional as F
import torchmetrics
from lightning.pytorch.loggers import CSVLogger
from shared_utilities import CustomDataModule, PyTorchMLPclass LightningModel(L.LightningModule):
def __init__(self, model,... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt | public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt/6.5-part3-cli-configurable/shared_utilities.py | import lightning as L
import numpy as np
import torch
import torch.nn.functional as F
import torchmetrics
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader, Dataset
class PyTorchMLP2(torch.nn.Module):
def __init__(self, nu... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt | public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt/6.5-part3-cli-configurable/README.md | See https://github.com/Lightning-AI/lightning-hpo
```
python mlp_cli.py --help
```
```
<class 'shared_utilities.LightningModel'>:
--model CONFIG Path to a configuration file.
--model.model MODEL (type: Optional[Any], default: null)
--model.learning_rate LEARNING_RATE
(type... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt | public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt/6.5-part3-cli-configurable/mlp_cli2.py | import sys
from lightning.pytorch.callbacks import ModelCheckpoint
from lightning.pytorch.cli import LightningCLI
from shared_utilities import CustomDataModule, LightningModel2
from watermark import watermark
if __name__ == "__main__":
print(watermark(packages="torch,lightning"))
print(f"The provided argume... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt | public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt/6.5-part4-lightning-hpo/sweeper.py | import os.path as ops
import optuna
from lightning import LightningApp
from lightning_hpo import Sweep
from lightning_hpo.algorithm.optuna import OptunaAlgorithm
from lightning_hpo.distributions.distributions import (
Categorical,
IntUniform,
LogUniform,
)
app = LightningApp(
Sweep(
script_pat... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt | public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt/6.5-part4-lightning-hpo/shared_utilities.py | import lightning as L
import numpy as np
import torch
import torch.nn.functional as F
import torchmetrics
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader, Dataset
class PyTorchMLP2(torch.nn.Module):
def __init__(self, nu... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt | public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt/6.5-part4-lightning-hpo/README.md | See https://github.com/Lightning-AI/lightning-hpo
Run as
```
python -m lightning run app sweeper.py
``` | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt | public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt/6.5-part4-lightning-hpo/mlp_cli2.py | import sys
from lightning.pytorch.callbacks import ModelCheckpoint
from lightning.pytorch.cli import LightningCLI
from shared_utilities import CustomDataModule, LightningModel2
from watermark import watermark
if __name__ == "__main__":
print(watermark(packages="torch,lightning"))
print(f"The provided argume... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt | public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt/6.5-part2-cli-simple/mlp_cli.py | import sys
from lightning.pytorch.callbacks import ModelCheckpoint
from lightning.pytorch.cli import LightningCLI
from shared_utilities import CustomDataModule, LightningModel, PyTorchMLP
from watermark import watermark
if __name__ == "__main__":
print(watermark(packages="torch,lightning"))
print(f"The prov... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt | public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt/6.5-part2-cli-simple/shared_utilities.py | import lightning as L
import numpy as np
import torch
import torch.nn.functional as F
import torchmetrics
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader, Dataset
class PyTorchMLP(torch.nn.Module):
def __init__(self, num... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt | public_repos/dl-fundamentals/unit06-dl-tips/6.5-hparam-opt/6.5-part2-cli-simple/README.md | See https://github.com/Lightning-AI/lightning-hpo
```
python mlp_cli.py --help
```
```
<class 'shared_utilities.LightningModel'>:
--model CONFIG Path to a configuration file.
--model.model MODEL (type: Optional[Any], default: null)
--model.learning_rate LEARNING_RATE
(type... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/6.8-part1-run-fast.ipynb | %load_ext watermark
%watermark -p torch,lightning,torchmetrics --condaimport lightning as L
import torch
import torch.nn.functional as F
import torchmetrics
from lightning.pytorch.loggers import CSVLogger
from shared_utilities import CustomDataModuleclass PyTorchMLP(torch.nn.Module):
def __init__(self, num_feature... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/6.8-part2-model-summary.ipynb | %load_ext watermark
%watermark -p torch,lightning,torchmetrics --condaimport lightning as L
import torch
import torch.nn.functional as F
import torchmetrics
from lightning.pytorch.loggers import CSVLogger
from shared_utilities import CustomDataModuleclass PyTorchMLP(torch.nn.Module):
def __init__(self, num_feature... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/shared_utilities.py | import lightning as L
import numpy as np
import torch
import torch.nn.functional as F
import torchmetrics
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader, Dataset
class LightningModel(L.LightningModule):
def __init__(sel... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/6.8-part3-overfit-batches.ipynb | %load_ext watermark
%watermark -p torch,lightning,torchmetrics --condaimport lightning as L
import torch
import torch.nn.functional as F
import torchmetrics
from lightning.pytorch.loggers import CSVLogger
from shared_utilities import CustomDataModuleclass PyTorchMLP(torch.nn.Module):
def __init__(self, num_feature... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/lightning_logs | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/lightning_logs/version_7/hparams.yaml | learning_rate: 0.15
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/lightning_logs | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/lightning_logs/version_4/hparams.yaml | learning_rate: 0.15
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/lightning_logs | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/lightning_logs/version_8/hparams.yaml | learning_rate: 0.15
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/lightning_logs | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/lightning_logs/version_3/hparams.yaml | learning_rate: 0.15
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/lightning_logs | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/lightning_logs/version_6/hparams.yaml | learning_rate: 0.15
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/lightning_logs | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/lightning_logs/version_5/hparams.yaml | learning_rate: 0.15
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/lightning_logs | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/lightning_logs/version_0/hparams.yaml | learning_rate: 0.15
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/lightning_logs | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/lightning_logs/version_1/hparams.yaml | learning_rate: 0.15
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/lightning_logs | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/lightning_logs/version_2/hparams.yaml | learning_rate: 0.15
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model/version_7/hparams.yaml | learning_rate: 0.15
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model/version_7/metrics.csv | val_loss,val_acc,epoch,step,train_acc
0.6934193968772888,0.5625,0,0,
,,0,0,0.40625
0.7030431628227234,0.53125,1,1,
,,1,1,0.84375
0.715786337852478,0.5,2,2,
,,2,2,0.90625
0.7386151552200317,0.53125,3,3,
,,3,3,0.96875
0.7486401796340942,0.5,4,4,
,,4,4,0.96875
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model/version_4/hparams.yaml | learning_rate: 0.15
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model/version_4/metrics.csv | train_loss,epoch,step,val_loss,val_acc,train_acc
0.612589418888092,0,49,,,
0.6312921643257141,0,99,,,
,0,99,0.5533653497695923,0.7256249785423279,
,0,99,,,0.6734374761581421
0.5817499756813049,1,149,,,
0.5988766551017761,1,199,,,
,1,199,0.5394373536109924,0.7418749928474426,
,1,199,,,0.7281249761581421
0.53695744276046... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model/version_8/hparams.yaml | learning_rate: 0.15
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model/version_8/metrics.csv | val_loss,val_acc,epoch,step,train_acc,test_acc
0.7795097827911377,0.515625,0,1,,
,,0,1,0.484375,
0.7538867592811584,0.53125,1,3,,
,,1,3,0.765625,
0.7527895569801331,0.53125,2,5,,
,,2,5,0.875,
0.7662935853004456,0.53125,3,7,,
,,3,7,0.921875,
0.7927659749984741,0.546875,4,9,,
,,4,9,0.9375,
,,5,10,,0.6255000233650208
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model/version_3/hparams.yaml | learning_rate: 0.15
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model/version_6/hparams.yaml | learning_rate: 0.15
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model/version_6/metrics.csv | val_loss,val_acc,epoch,step,train_acc,train_loss,test_acc
0.6020881533622742,0.6728515625,0,31,,,
,,0,31,0.6376953125,,
,,1,49,,0.5551405549049377,
0.589432954788208,0.69921875,1,63,,,
,,1,63,0.701171875,,
0.5851702094078064,0.7080078125,2,95,,,
,,2,95,0.7275390625,,
,,3,99,,0.4422959089279175,
0.5829908847808838,0.715... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model/version_5/hparams.yaml | learning_rate: 0.15
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model/version_5/metrics.csv | train_loss,epoch,step,val_loss,val_acc,train_acc
0.612589418888092,0,49,,,
0.6312921643257141,0,99,,,
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0.53695744276046... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model/version_0/hparams.yaml | learning_rate: 0.15
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model/version_0/metrics.csv | train_loss,epoch,step,val_loss,val_acc,train_acc
0.6941855549812317,0,49,,,
0.6088317632675171,0,99,,,
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0.47560206055641174,0,449,,,
,0,449,0.4812189042... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model/version_1/hparams.yaml | learning_rate: 0.15
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model/version_1/metrics.csv | train_loss,epoch,step,val_loss,val_acc,train_acc
0.6941855549812317,0,49,,,
0.6088317632675171,0,99,,,
0.5148017406463623,0,149,,,
0.5205519795417786,0,199,,,
0.49875062704086304,0,249,,,
0.575430691242218,0,299,,,
0.6062870621681213,0,349,,,
0.45823806524276733,0,399,,,
0.47560206055641174,0,449,,,
,0,449,0.4812189042... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model/version_2/hparams.yaml | learning_rate: 0.15
| 0 |
public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model | public_repos/dl-fundamentals/unit06-dl-tips/6.8-debugging/logs/my-model/version_2/metrics.csv | train_loss,epoch,step,val_loss,val_acc,train_acc,test_acc
0.6941855549812317,0,49,,,,
0.6088317632675171,0,99,,,,
0.5148017406463623,0,149,,,,
0.5205519795417786,0,199,,,,
0.49875062704086304,0,249,,,,
0.575430691242218,0,299,,,,
0.6062870621681213,0,349,,,,
0.45823806524276733,0,399,,,,
0.47560206055641174,0,449,,,,
,... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/exercises | public_repos/dl-fundamentals/unit06-dl-tips/exercises/2_adam-with-weight-decay/exercise-2.ipynb | %load_ext watermark
%watermark -p torch,lightningimport lightning as L
import torch
import torch.nn.functional as F
import torchmetrics
from lightning.pytorch.loggers import CSVLogger
from shared_utilities import CustomDataModule, PyTorchMLPnum_epochs = 100class LightningModel(L.LightningModule):
def __init__(self... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/exercises | public_repos/dl-fundamentals/unit06-dl-tips/exercises/2_adam-with-weight-decay/README.md | The task of this exercise is to experiment with weight decay. Note that we haven't covered weight decay in the lecture. Related to Dropout (which we covered in Unit 6), weight decay is a regularization technique used in training neural networks to prevent overfitting.
Traditionally, a related method called L2-regulari... | 0 |
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