70 lines
1.8 KiB
Python
70 lines
1.8 KiB
Python
|
#!/usr/bin/python3
|
||
|
|
|
||
|
|
import torch
|
||
|
|
import pandas as pd
|
||
|
|
from sklearn.model_selection import train_test_split
|
||
|
|
|
||
|
|
data = pd.read_csv('iris.data.multilabel', sep=',', header=None)
|
||
|
|
NAMES_DICT = {
|
||
|
|
'Iris-setosa': 0,
|
||
|
|
'Iris-versicolor': 1,
|
||
|
|
'Iris-virginica': 2}
|
||
|
|
|
||
|
|
data[5] = data[4].apply(lambda x: NAMES_DICT[x])
|
||
|
|
|
||
|
|
x = torch.tensor(data[[0,1,2,3]].values, dtype=torch.float)
|
||
|
|
y = torch.tensor(data[5], dtype=torch.long)
|
||
|
|
|
||
|
|
X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=42)
|
||
|
|
|
||
|
|
|
||
|
|
class Network(torch.nn.Module):
|
||
|
|
|
||
|
|
def __init__(self):
|
||
|
|
super(Network, self).__init__()
|
||
|
|
self.fc = torch.nn.Linear(4, 3)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
x = self.fc(x)
|
||
|
|
x = torch.nn.functional.softmax(x)
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
network = Network()
|
||
|
|
optimizer = torch.optim.SGD(network.parameters(), lr=0.002)
|
||
|
|
criterion = torch.nn.CrossEntropyLoss(reduction='sum')
|
||
|
|
|
||
|
|
samples_in_batch = 5
|
||
|
|
|
||
|
|
for epoch in range(3000):
|
||
|
|
|
||
|
|
network.train()
|
||
|
|
for i in range(0, len(X_train), samples_in_batch):
|
||
|
|
batch_x = X_train[i:i + samples_in_batch]
|
||
|
|
batch_y = y_train[i:i + samples_in_batch]
|
||
|
|
optimizer.zero_grad()
|
||
|
|
ypredicted = network(batch_x)
|
||
|
|
|
||
|
|
loss = criterion(ypredicted, batch_y)
|
||
|
|
|
||
|
|
loss.backward()
|
||
|
|
optimizer.step()
|
||
|
|
|
||
|
|
network.eval()
|
||
|
|
predicted_correct = 0
|
||
|
|
loss_sum = 0
|
||
|
|
for i in range(0, len(X_test), samples_in_batch):
|
||
|
|
batch_x = X_test[i:i + samples_in_batch]
|
||
|
|
batch_y = y_test[i:i + samples_in_batch]
|
||
|
|
optimizer.zero_grad()
|
||
|
|
ypredicted = network(batch_x)
|
||
|
|
y_most_probable_class = torch.max(ypredicted,1)[1]
|
||
|
|
|
||
|
|
loss = criterion(ypredicted, batch_y)
|
||
|
|
|
||
|
|
predicted_correct += sum(y_most_probable_class == batch_y).item()
|
||
|
|
|
||
|
|
|
||
|
|
accuracy = 100 * predicted_correct / len(y_test)
|
||
|
|
print('{:.3}'.format(loss.item()), "\t => ", accuracy, '% accuracy')
|