Ошибка «Последовательный объект не имеет веса атрибута» при построении модели CNN ⇐ Python

[Anonymous]

Код: Выделить всё

from google.colab import drive
drive.mount('/gdrive', force_remount=True)

import os
import numpy as np
import torch
import torch.nn as nn
from sklearn.metrics import accuracy_score
from torch.utils.data import (DataLoader, RandomSampler, TensorDataset)
from keras.datasets import mnist

class MNIST_CNN(nn.Module):
def __init__(self, config):
super(MNIST_CNN, self).__init__()

self.conv1 = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)

self.conv2 = nn.Sequential(
nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)

self.fnn = nn.Sequential(
nn.Linear(7*7*64, 10, bias=True)
)

nn.init.xavier_uniform_(self.fnn.weight)

def forward(self,input_features):
output = self.conv1(input_features)
output = self.conv2(output)
output = output.view(output.size(0), -1)
hyothesis = self.fnn(output)
return hyothesis

def load_dataset():
(train_X, train_Y), (test_X, test_Y) = mnist.load_data()

train_X = train_X.reshape(-1, 1, 28, 28)
test_X = test_X.reshape(-1, 1, 28, 28)

train_X = torch.tensor(train_X, dtype=torch.float)
train_Y = torch.tensor(train_Y, dtype=torch.long)
test_X = torch.tensor(test_X, dtype=torch.float)
test_Y = torch.tensor(test_Y, dtype=torch.long)

return (train_X, train_Y), (test_X, test_Y)

def tensor2list(input_tensor):
return input_tensor.cpu().detach().numpy().tolist()

def do_test(model, test_dataloader):
model.eval()

predicts, golds = [], []

with torch.no_grad():
for step, batch in enumerate(test_dataloader):
batch = tuple(t.cuda() for t in batch)
input_features, labels = batch

hypothesis = model(input_features)
print("size of hypothesis", hypothesis.size())
logits = torch.argmax(hypothesis, -1)

x = tensor2list(logits)
y = tensor2list(labels)

predicts.extend(x)
golds.extend(y)

print("PRED=", predicts)
print("GOLD=", golds)
print("Accuracy={0:f}\n".format(accuracy_score(golds, predicts)))

def test(config):
model = MNIST_CNN(config).cuda()

model.load_state_dict(torch.load(os.path.join(config["output_dir"], config["model_name"])))

(_, _), (features, labels) = load_dataset()

test_features = TensorDataset(features, labels)
test_dataloader = DataLoader(test_features, shuffle=True, batch_size=config["batch_size"])

do_test(model, test_dataloader)

def train(config):
model = MNIST_CNN(config).cuda()

(input_features, labels), (_, _) = load_dataset()

train_features = TensorDataset(input_features, labels)
train_dataloader = DataLoader(train_features, shuffle=True, batch_size=config["batch_size"])

loss_func = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=config["learn_rate"])

for epoch in range(config["epochs"]):
model.train()

costs = []

for step, batch in enumerate(train_dataloader):
batch = tuple(t.cuda() for t in batch)
input_features, labels = batch

optimizer.zero_grad()

hypothesis = model(input_features)
cost = loss_func(hypothesis, labels)

cost.backward()
optimizer.step()

costs.append(cost.data.item())

print("Average Loss={0:f}".format(np.mean(costs)))
torch.save(model.state_dict(), os.path.join(config["output_dir"], "epoch_{0:d}.pt"))
do_test(model, train_dataloader)

if(__name__=="__main__"):
root_dir = "/gdrive/My Drive/24-2/MachineLearning"
output_dir = os.path.join(root_dir, "output")
if not os.path.exists(output_dir):
os.makedirs(output_dir)

config = {"mode":  "test",
"model_name":"epoch_{0:d}.pt".format(10),
"output_dir":output_dir,
"learn_rate":0.001,
"batch_size":32,
"epoch":10,
}

if(config["mode"] == "train"):
train(config)
else:
test(config)

из этого кода я получил AttributeError: объект «Последовательный» не имеет атрибута «вес».
Я попробовал nn.init.xavier_uniform_(self.fnn[0].weight ), но это привело к ошибке ниже.
RuntimeError: Ошибки при загрузке state_dict для MNIST_CNN:
Отсутствуют ключи в state_dict: "conv1.0.weight", "conv1.0.bias", "conv2.0.weight", "conv2.0.bias", "fnn.0.weight", "fnn.0.bias".
Неожиданные ключи в state_dict: «linear1.weight», «linear1.bias», «linear2.weight», «linear2.bias».
как я могу решить эту проблему ? пожалуйста, помогите мне.

Подробнее здесь: https://stackoverflow.com/questions/791 ... -cnn-model