问题描述
import sys,os
import cv2
import numpy as np
from tqdm import tqdm
REBUILD_DATA = True
import matplotlib.pyplot as plt
class ArtOrNot():
f = open('Art2','w')
f.write('oof')
f.close()
IMG_SIZE = 50
ART = (r'C:\Users\Kyel\Desktop\Python projects\ART')
NOTART = (r'C:\Users\Kyel\Desktop\Python projects\NOTART')
LABELS = {ART: 1,NOTART: 0}
training_data = []
artcount = 0
notartcount = 0
def make_training_data(self):
for label in self.LABELS:
print(label)
for f in tqdm(os.listdir(label)):
try:
path = os.path.join(label,f)
img = cv2.imread(path)
img = cv2.resize(img,(self.IMG_SIZE,self.IMG_SIZE))
#gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#(img) = (grey)
self.training_data.append([np.array(img),np.eye(2)[self.LABELS[label]]])
if label == self.ART:
self.artcount += 1
print(self.artcount)
elif label == self.NOTART:
self.notartcount += 1
except Exception as e:
pass
#print(training_data)
np.random.shuffle(self.training_data)
np.save("training_data.npy",self.training_data)
print("Art:",self.artcount)
print("NotART",self.notartcount)
if REBUILD_DATA:
artornot = ArtOrNot()
artornot.make_training_data()
training_data = np.load("training_data.npy",allow_pickle=True)
print(len(training_data))
import matplotlib.pyplot as plt
plt.imshow(training_data[1][0])
plt.show()
import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1,32,5)
self.conv2 = nn.Conv2d(32,64,5)
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)
self.fc2 = nn.Linear(512,2)
def convs(self,x):
x = F.max_pool2d(F.relu(self.conv1(x)),(2,2))
x = F.max_pool2d(F.relu(self.conv2(x)),2))
x = F.max_pool2d(F.relu(self.conv3(x)),2))
print(x[0].shape)
if self._to_linear is None:
self._to_linear = x[0].shape[0]*x[0].shape[1]*x[0].shape[2]
return x
def forward(self,x):
x = self.convs(x)
x = x.view(-1,self._to_linear)
c = F.relu(self.fc1(x))
x = self.fc2(x)
#return F.softmax(x,dim =1)
net = Net()
print(net)
import torch.optim as optim
optimizer = optim.Adam(net.parameters(),lr=0.001)
#loss_function = torch.nn.functional.mse_loss()
X = torch.Tensor([i[0] for i in training_data]).view(-1,50)
X = X/255.0
y = torch.Tensor([i[1] for i in training_data])
VAL_PCT = 0.2
val_size = int(len(X)*VAL_PCT)
print(val_size)
train_X = X[:-val_size]
#print(train_X)
train_y = X[:-val_size]
#print(train_y)
test_X = X[-val_size:]
print(test_X)
test_y = y[-val_size:]
print(test_y)
#print(X)
print(len(X))
#test_X = 8
#print(len(train_X),print(test_X))
#print(len(train_X))
#print(len(train_X))
#print(test_X)
BATCH_SIZE = 4
EPOCHS = 10
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(-1,50)
batch_y = train_y[i:i+BATCH_SIZE]
input = torch.randn(3,5,requires_grad=True)
target = torch.randn(3,5)
output = loss(input,target)
output.backward()
optimizer.step()
print(output)
#print(f"Epoch: {epoch}. Loss: {loss}")
#print(loss)
#print(len(train_X))
#print(len(test_X))
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(10,50))[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))
所以我知道从神经网络出来的数据大小是2500，当我更改参数时，又遇到另一个错误，说它期望4D张量，而我只给了3D张量？有帮助吗？
解决方法
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