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fix: spelling mistakes

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This commit is contained in:
Krzysztof Rudnicki 2023-05-13 13:33:48 +02:00
parent 45c6d66cea
commit ffaf696f51
3 changed files with 21 additions and 10 deletions
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3
.vscode/extensions.json vendored
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@ -4,6 +4,7 @@
"ms-python.pylint", "ms-python.pylint",
"mikoz.black-py", "mikoz.black-py",
"james-yu.latex-workshop", "james-yu.latex-workshop",
"kisstkondoros.vscode-gutter-preview" "kisstkondoros.vscode-gutter-preview",
"streetsidesoftware.code-spell-checker"
] ]
} }

11
.vscode/settings.json vendored
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@ -1,3 +1,12 @@
{ {
"python.linting.pylintArgs": ["--generate-members"] "python.linting.pylintArgs": [
"--generate-members"
],
"cSpell.words": [
"dtype",
"loadtxt",
"optim",
"Xbatch",
"ybatch"
]
} }

17
lab5/code/main.py
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@ -2,24 +2,25 @@
import numpy as np import numpy as np
# pytorch for deep learning models # pytorch for deep learning models
import torch import torch
# nn like neural network
import torch.nn as nn import torch.nn as nn
import torch.optim as optim import torch.optim as optim
# Pima indians describes pateitn medical data and whether they had diabetes for last 5 years # Pima indians describes patient medical data and whether they had diabetes for last 5 years
# It is binary classification (they could either have diabetes 1 or not 0) # It is binary classification (they could either have diabetes 1 or not 0)
# load the file as a matrix of numbers, # load the file as a matrix of numbers,
dataset = np.loadtxt('pima-indians-diabetes.csv', delimiter=',') dataset = np.loadtxt('pima-indians-diabetes.csv', delimiter=',')
input_columns = 8 input_columns = 8
# split into input (X) -> in this case everything beside info whether patient had diabetes or not is input # split into input (X) -> in this case everything beside info whether patient had diabetes or not is input
# We are spliting data into two subsets by using NumPy slie operator : and choose first 8 columns using 0:8 slice # We are splitting data into two subsets by using NumPy slice operator : and choose first 8 columns using 0:8 slice
X = dataset[:,0:input_columns] X = dataset[:,0:input_columns]
# and output (y) variables -> in this case we are only interested whether patient had diabetes or not as an output # and output (y) variables -> in this case we are only interested whether patient had diabetes or not as an output
# you can simplify that y = f(X) # you can simplify that y = f(X)
# We are spliting the data by using slice operator : and choosing last column # We are splitting the data by using slice operator : and choosing last column
y = dataset[:,input_columns] y = dataset[:,input_columns]
# we need to convert this data to pytorch tensors # we need to convert this data to pytorch tensors
# Pyutoarch usually operates on 32-bit floating point and NumPy by default uses 64 bit floating point # Pytorch usually operates on 32-bit floating point and NumPy by default uses 64 bit floating point
X = torch.tensor(X, dtype=torch.float32) X = torch.tensor(X, dtype=torch.float32)
# We can also correct the shape to fit what PyTorch would expect (here we are converting n vectors to n x 1 matrix) # We can also correct the shape to fit what PyTorch would expect (here we are converting n vectors to n x 1 matrix)
# This simplifies handling matrix multiplication operations (which are the basis of deep learning models) # This simplifies handling matrix multiplication operations (which are the basis of deep learning models)
@ -33,13 +34,13 @@ class PimaClassifier(nn.Module):
super().__init__() super().__init__()
# There are 3 (fully connected) layers in class, each with their activation function # There are 3 (fully connected) layers in class, each with their activation function
# creates Linear layer, it maps input to a hidden layer of 12 neurons # creates Linear layer, it maps input to a hidden layer of 12 neurons
# input features have a size of 8 (same number as number of eatures in pima indians diabetes dataset) # input features have a size of 8 (same number as number of features in pima indians diabetes dataset)
first_output_neurons = 12 first_output_neurons = 12
self.hidden1 = nn.Linear(input_columns, first_output_neurons) self.hidden1 = nn.Linear(input_columns, first_output_neurons)
# This creates ReLU (rectified linear unit) activation function applied after first hidden layer # This creates ReLU (rectified linear unit) activation function applied after first hidden layer
self.act1 = nn.ReLU() self.act1 = nn.ReLU()
# This maps the ouput of first layer (which was 12 neurons) to new hidden layer of 8 neurons # This maps the output of first layer (which was 12 neurons) to new hidden layer of 8 neurons
second_output_neurons = 8 second_output_neurons = 8
self.hidden2 = nn.Linear(first_output_neurons, second_output_neurons) self.hidden2 = nn.Linear(first_output_neurons, second_output_neurons)
# ReLU activation function applied after second hidden layer # ReLU activation function applied after second hidden layer
@ -89,7 +90,7 @@ batch_size = 10
# We split dataset into batches and pass batches one by one into a model to training loop # We split dataset into batches and pass batches one by one into a model to training loop
# after using all batches we finish one epoch and can start over again to refine the model # after using all batches we finish one epoch and can start over again to refine the model
# we use two netsed for loops for training, one is for epochs # we use two nested for loops for training, one is for epochs
for epoch in range(n_epochs): for epoch in range(n_epochs):
# and one for batches # and one for batches
for i in range(0, len(X), batch_size): for i in range(0, len(X), batch_size):
@ -103,7 +104,7 @@ for epoch in range(n_epochs):
loss = loss_fn(y_pred, ybatch) loss = loss_fn(y_pred, ybatch)
# optimize model # optimize model
optimizer.zero_grad() optimizer.zero_grad()
# callculate the innacuracy # calculate the inaccuracy
loss.backward() loss.backward()
# optimizer takes next step # optimizer takes next step
optimizer.step() optimizer.step()