89 lines
2.2 KiB
Python
89 lines
2.2 KiB
Python
import os
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import norch
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import norch.distributed as dist
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import norch.distributed
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import norch.nn as nn
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import norch.optim as optim
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from norch.utils.data.dataloader import DataLoader
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from norch.nn.parallel import DistributedDataParallel
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from norch.utils.data.distributed import DistributedSampler
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from norch.norchvision import transforms as T
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import numpy as np
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import matplotlib.pyplot as plt
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import random
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random.seed(1)
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def main():
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BATCH_SIZE = 32
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device = "cpu"
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epochs = 10
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transform = T.Compose(
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[
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T.ToTensor(),
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T.Reshape([-1, 784, 1])
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]
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)
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target_transform = T.Compose(
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[
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T.ToTensor()
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]
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)
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train_data, test_data = norch.norchvision.datasets.MNIST.splits(transform=transform, target_transform=target_transform)
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train_loader = norch.utils.data.DataLoader(train_data, batch_size=BATCH_SIZE)
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class MyModel(nn.Module):
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def __init__(self):
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super(MyModel, self).__init__()
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self.fc1 = nn.Linear(784, 30)
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self.sigmoid1 = nn.Sigmoid()
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self.fc2 = nn.Linear(30, 10)
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self.sigmoid2 = nn.Sigmoid()
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def forward(self, x):
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out = self.fc1(x)
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out = self.sigmoid1(out)
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out = self.fc2(out)
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out = self.sigmoid2(out)
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return out
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model = MyModel().to(device)
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model = DistributedDataParallel(model)
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.SGD(model.parameters(), lr=0.01)
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loss_list = []
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for epoch in range(epochs):
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for idx, batch in enumerate(train_loader):
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inputs, target = batch
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inputs = inputs.to(device)
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target = target.to(device)
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outputs = model(inputs)
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loss = criterion(outputs, target)
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optimizer.zero_grad()
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print("#####################\n\nantes backward")
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print(model.module.fc1.bias.grad)
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loss.backward()
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print(model.module.fc1.bias.grad)
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print("\n\n\n###############\n\npós backward")
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optimizer.step()
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print("@@")
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break
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break
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if __name__ == "__main__":
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main()
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