72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
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#!/usr/bin/python3
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import torch
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import pandas as pd
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from sklearn.model_selection import train_test_split
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data = pd.read_csv('iris.data.multilabel', sep=',', header=None)
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NAMES_DICT = {
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'Iris-setosa': 0,
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'Iris-versicolor': 1,
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'Iris-virginica': 2}
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data[5] = data[4].apply(lambda x: NAMES_DICT[x])
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x = torch.tensor(data[[0,1,2,3]].values, dtype=torch.float)
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y = torch.tensor(data[5], dtype=torch.long)
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X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=42)
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class Network(torch.nn.Module):
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def __init__(self):
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super(Network, self).__init__()
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self.fc1 = torch.nn.Linear(4, 4)
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self.fc2 = torch.nn.Linear(4, 3)
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def forward(self, x):
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x = self.fc1(x)
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x = self.fc2(x)
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x = torch.nn.functional.softmax(x)
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return x
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network = Network()
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optimizer = torch.optim.SGD(network.parameters(), lr=0.002)
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criterion = torch.nn.CrossEntropyLoss(reduction='sum')
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samples_in_batch = 5
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for epoch in range(3000):
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network.train()
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for i in range(0, len(X_train), samples_in_batch):
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batch_x = X_train[i:i + samples_in_batch]
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batch_y = y_train[i:i + samples_in_batch]
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optimizer.zero_grad()
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ypredicted = network(batch_x)
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loss = criterion(ypredicted, batch_y)
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loss.backward()
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optimizer.step()
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network.eval()
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predicted_correct = 0
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loss_sum = 0
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for i in range(0, len(X_test), samples_in_batch):
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batch_x = X_test[i:i + samples_in_batch]
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batch_y = y_test[i:i + samples_in_batch]
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optimizer.zero_grad()
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ypredicted = network(batch_x)
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y_most_probable_class = torch.max(ypredicted,1)[1]
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loss = criterion(ypredicted, batch_y)
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predicted_correct += sum(y_most_probable_class == batch_y).item()
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accuracy = 100 * predicted_correct / len(y_test)
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print('{:.3}'.format(loss.item()), "\t => ", accuracy, '% accuracy')
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