TypeError: new(): argument 'size' must be tuple of ints, 但是在使用 pytorch 时在 pos 2 找到类型为 NoneType 的元素，使用 nn.linear

Question

文件“C:\Users\J2\Desktop\Pytorchseries\thenn.py”，第 50 行，在 net = Net() TypeError: new(): argument 'size' must be tuple of ints, but found element of type NoneType at at位置 2

如果它有帮助，我正在关注 sentdex pytorch 教程。 任何帮助，将不胜感激。 我是机器学习的新手，我希望这会奏效。 请帮帮我！

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
import tqdm

training_data = np.load('training_data.npy', allow_pickle=True)
print(len(training_data))

X = torch.Tensor([i[0] for i in training_data]).view(-1,50,50)
X = X/255.0
y = torch.Tensor([i[1] for i in training_data])

plt.imshow(X[0], cmap='gray')
print(y[0])

class Net(nn.Module):
    def __init__(self):
        super().__init__() # just run the init of parent class (nn.Module)
        self.conv1 = nn.Conv2d(1, 32, 5) # input is 1 image, 32 output channels, 5x5 kernel / window
        self.conv2 = nn.Conv2d(32, 64, 5) # input is 32, bc the first layer output 32. Then we say the output will be 64 channels, 5x5 kernel / window
        self.conv3 = nn.Conv2d(64, 128, 5)

        x = torch.randn(50,50).view(-1,1,50,50)
        self._to_linear = None
        self.convs(x)

        self.fc1 = nn.Linear(self._to_linear, 512) #flattening.
        self.fc2 = nn.Linear(512, 2) # 512 in, 2 out bc we're doing 2 classes (dog vs cat).
        

    def convs(self, x):
        # max pooling over 2x2
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2))
        x = F.max_pool2d(F.relu(self.conv3(x)), (2, 2))

    def forward(self, x):
        x = self.convs(x)
        x = x.view(-1, self._to_linear)  # .view is reshape ... this flattens X before 
        x = F.relu(self.fc1(x))
        x = self.fc2(x) # bc this is our output layer. No activation here.
        return F.softmax(x, dim=1)

        if self._to_linear is None:
            self._to_linear = x[0].shape[0]*x[0].shape[1]*x[0].shape[2]
        return x

net = Net()
print(net)

import torch.optim as optim

optimizer = optim.Adam(net.parameters(), lr=0.001)
loss_function = nn.MSELoss()

X = torch.Tensor([i[0] for i in training_data]).view(-1,50,50)
X = X/255.0
y = torch.Tensor([i[1] for i in training_data])

VAL_PCT = 0.1  # lets reserve 10% of our data for validation
val_size = int(len(X)*VAL_PCT)
print(val_size)

train_X = X[:-val_size]
train_y = y[:-val_size]

test_X = X[-val_size:]
test_y = y[-val_size:]

print(len(train_X), len(test_X))

BATCH_SIZE = 100
EPOCHS = 1

for epoch in range(EPOCHS):
    for i in tqdm(range(0, len(train_X), BATCH_SIZE)): # from 0, to the len of x, stepping BATCH_SIZE at a time. [:50] ..for now just to dev
        #print(f"{i}:{i+BATCH_SIZE}")
        batch_X = train_X[i:i+BATCH_SIZE].view(-1, 1, 50, 50)
        batch_y = train_y[i:i+BATCH_SIZE]

        net.zero_grad()

        outputs = net(batch_X)
        loss = loss_function(outputs, batch_y)
        loss.backward()
        optimizer.step()    # Does the update

    print(f"Epoch: {epoch}. Loss: {loss}")

correct = 0
total = 0
with torch.no_grad():
    for i in tqdm(range(len(test_X))):
        real_class = torch.argmax(test_y[i])
        net_out = net(test_X[i].view(-1, 1, 50, 50))[0]  # returns a list, 
        predicted_class = torch.argmax(net_out)

        if predicted_class == real_class:
            correct += 1
        total += 1
print("Accuracy: ", round(correct/total, 3))

Answer 1

问题在于self._to_linear 。 您在__init__中将其用作：

self._to_linear = None    

self.convs(x)
self.fc1 = nn.Linear(self._to_linear, 512) #flattening.

对nn.Linear的调用将其作为参数。 此参数应等于线性层中输入特征的数量，并且不能为None ，因为该值将决定层的形状（权重和偏差的数量）。 如何解决此问题取决于您要实现的目标。