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SimpleMLP.py

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+# %% Импорт библиотек
+import torch
+import torch.nn as nn
+
+# %% Входные переменные
+input_ = 32*32
+hidden_1 = 5
+hidden_2 = 12
+output = 2
+learning_rate = 1e-3
+
+weights_1 = torch.rand(size=(input_, hidden_1), requires_grad=True)
+weights_2 = torch.rand(size=(hidden_1, hidden_2), requires_grad=True)
+weights_3 = torch.rand(size=(hidden_2, output), requires_grad=True)
+
+random_input = torch.rand(1, input_)
+
+true_label = torch.zeros(1, output)
+true_label[0][0] = 1
+# %% Цикл обучения
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.SGD([weights_1, weights_2, weights_3], lr=learning_rate)
+
+hidden_layer_1 = random_input @ weights_1
+hidden_layer_2 = hidden_layer_1 @ weights_2
+output_layer = hidden_layer_2 @ weights_3
+
+loss = criterion(output_layer, true_label)
+print(loss)
+
+optimizer.zero_grad()
+loss.backward()
+optimizer.step()
+
+print(weights_1._grad)
+print(weights_2._grad)
+print(weights_3._grad)