Why are you starting with a 32x32 image? That seems very small.

No rhyme nor reason, really. I’ll change it to 256x256, if that’s better?

Actually, if I go to 64xt64 or greater, the machine locks while trying to allocate memory.

import numpy as np
import math
import cv2
import random
import torch
from torch import flatten
from torch.autograd import Variable
import torch.nn as nn

import os.path
from os import path



img_width = 32
num_channels = 3

num_input_components = img_width*img_width*num_channels
num_output_components = 2

num_epochs = 10
learning_rate = 0.001






class Net(torch.nn.Module):
    def __init__(self, num_channels, num_output_components, all_train_files_len):
        super().__init__()
        self.model = torch.nn.Sequential(
            #Input = 3 x 32 x 32, Output = 32 x 32 x 32
            torch.nn.Conv2d(in_channels = num_channels, out_channels = 32, kernel_size = 3, padding = 1), 
            torch.nn.ReLU(),
            #Input = 32 x 32 x 32, Output = 32 x 16 x 16
            torch.nn.MaxPool2d(kernel_size=2),
  
            #Input = 32 x 16 x 16, Output = 64 x 16 x 16
            torch.nn.Conv2d(in_channels = 32, out_channels = 64, kernel_size = 3, padding = 1),
            torch.nn.ReLU(),
            #Input = 64 x 16 x 16, Output = 64 x 8 x 8
            torch.nn.MaxPool2d(kernel_size=2),
              
            #Input = 64 x 8 x 8, Output = 64 x 8 x 8
            torch.nn.Conv2d(in_channels = 64, out_channels = 64, kernel_size = 3, padding = 1),
            torch.nn.ReLU(),
            #Input = 64 x 8 x 8, Output = 64 x 4 x 4
            torch.nn.MaxPool2d(kernel_size=2),
  
            torch.nn.Flatten(),
            torch.nn.Linear(64*4*4, all_train_files_len),
            torch.nn.ReLU(),
            torch.nn.Linear(all_train_files_len, num_output_components)
        )
  
    def forward(self, x):
        return self.model(x)


"""	
def __init__(self, num_channels, num_output_components, all_train_files_len):
		# call the parent constructor
		super(Net, self).__init__()
		self.conv1 = nn.Conv2d(num_channels, img_width, kernel_size=(3,3), stride=1, padding=1)
		self.act1 = nn.ReLU()
		self.drop1 = nn.Dropout(0.3)
 
		self.conv2 = nn.Conv2d(img_width, img_width, kernel_size=(3,3), stride=1, padding=1)
		self.act2 = nn.ReLU()
		self.pool2 = nn.MaxPool2d(kernel_size=(2, 2))
 
		self.flat = nn.Flatten()
 
		self.fc3 = nn.Linear(8*img_width*img_width, 512)
		self.act3 = nn.ReLU()
		self.drop3 = nn.Dropout(0.5)
 
		self.fc4 = nn.Linear(512, num_output_components)

	def forward(self, x):
		# input 3x32x32, output 32x32x32
		x = self.act1(self.conv1(x))
		x = self.drop1(x)
		# input 32x32x32, output 32x32x32
		x = self.act2(self.conv2(x))
		# input 32x32x32, output 32x16x16
		x = self.pool2(x)
		# input 32x16x16, output 8192
		x = self.flat(x)
		# input 8192, output 512
		x = self.act3(self.fc3(x))
		x = self.drop3(x)
		# input 512, output 10
		x = self.fc4(x)
		return x
"""

"""
	def __init__(self):
		super(Net, self).__init__()
		self.hidden1 = torch.nn.Linear(num_input_components, 8192)
		self.hidden2 = torch.nn.Linear(8192, 1024) 
		self.hidden3 = torch.nn.Linear(1024, 128)
		self.predict = torch.nn.Linear(128, num_output_components)

	def forward(self, x):
		x = torch.tanh(self.hidden1(x))		
		x = torch.tanh(self.hidden2(x))
		x = torch.tanh(self.hidden3(x))
		x = self.predict(x)    # linear output
		return x
"""



class float_image:

	def __init__(self, img):
		self.img = img

class image_type:

	def __init__(self, img_type, float_img):
		self.img_type = img_type
		self.float_img = float_img




if False: #path.exists('weights_' + str(num_input_components) + '_' + str(num_epochs) + '.pth'):
	net.load_state_dict(torch.load('weights_' + str(num_input_components) + '_' + str(num_epochs) + '.pth'))
	print("loaded file successfully")
else:
	print("training...")





	all_train_files = []

#	file_count = 0

	path = 'training_set/cats/'
	filenames = next(os.walk(path))[2]

	for f in filenames:

#		file_count = file_count + 1
#		if file_count >= 10000:
#			break;

		print(path + f)
		img = cv2.imread(path + f)
		
		if (img is None) == False:

			img = img.astype(np.float32)
			res = cv2.resize(img, dsize=(img_width, img_width), interpolation=cv2.INTER_LINEAR)
			flat_file = res / 255.0
			flat_file = np.transpose(flat_file, (2, 0, 1))
			all_train_files.append(image_type(0, flat_file))

		else:
			print("image read failure")

#	file_count = 0




	path = 'training_set/dogs/'
	filenames = next(os.walk(path))[2]

	for f in filenames:

#		file_count = file_count + 1
#		if file_count >= 10000:
#			break;

		print(path + f)
		img = cv2.imread(path + f)
		
		if (img is None) == False:

			img = img.astype(np.float32)
			res = cv2.resize(img, dsize=(img_width, img_width), interpolation=cv2.INTER_LINEAR)
			flat_file = res / 255.0
			flat_file = np.transpose(flat_file, (2, 0, 1))
			all_train_files.append(image_type(1, flat_file))

		else:
			print("image read failure")



	


	net = Net(num_channels, num_output_components, len(all_train_files))
	optimizer = torch.optim.Adam(net.parameters(), lr = learning_rate)
	loss_func = torch.nn.MSELoss()


	batch = np.zeros((len(all_train_files), num_channels, img_width, img_width), dtype=np.float32)
	ground_truth = np.zeros((len(all_train_files), num_output_components), dtype=np.float32)
	
	for epoch in range(num_epochs):
		
		random.shuffle(all_train_files)

		count = 0

		for i in all_train_files:

			batch[count] = i.float_img
		
			if i.img_type == 0: # cat
				ground_truth[count][0] = 1
				ground_truth[count][1] = 0
			elif i.img_type == 1: # dog
				ground_truth[count][0] = 0
				ground_truth[count][1] = 1

			count = count + 1
	
		x = Variable(torch.from_numpy(batch))
		y = Variable(torch.from_numpy(ground_truth))


		prediction = net(x)	 
		loss = loss_func(prediction, y)

		print(epoch, loss)

		optimizer.zero_grad()	 # clear gradients for next train
		loss.backward()		 # backpropagation, compute gradients
		optimizer.step()		# apply gradients



	#torch.save(net.state_dict(), 'weights_' + str(num_input_components) + '_' + str(num_epochs) + '.pth')



path = 'test_set/cats/'
filenames = next(os.walk(path))[2]

cat_count = 0
total_count = 0

for f in filenames:

#	print(path + f)
	img = cv2.imread(path + f).astype(np.float32)
			
	if (img is None) == False:
		img = img.astype(np.float32)
		res = cv2.resize(img, dsize=(img_width, img_width), interpolation=cv2.INTER_LINEAR)
		flat_file = res / 255.0
		flat_file = np.transpose(flat_file, (2, 0, 1))

	else:
		print("image read failure")
		continue

	batch = torch.zeros((1, num_channels, img_width, img_width), dtype=torch.float32)
	batch[0] = torch.from_numpy(flat_file)

	prediction = net(Variable(batch))

	if prediction[0][0] > prediction[0][1]:
		cat_count = cat_count + 1

	total_count = total_count + 1
#	print(batch)
#		print(prediction)

print(cat_count / total_count)
print(total_count)





path = 'test_set/dogs/'
filenames = next(os.walk(path))[2]

dog_count = 0
total_count = 0

for f in filenames:

#	print(path + f)
	img = cv2.imread(path + f).astype(np.float32)
			
	if (img is None) == False:
		img = img.astype(np.float32)
		res = cv2.resize(img, dsize=(img_width, img_width), interpolation=cv2.INTER_LINEAR)
		flat_file = res / 255.0
		flat_file = np.transpose(flat_file, (2, 0, 1))

	else:

		print("image read failure")
		continue

	batch = torch.zeros((1, num_channels, img_width, img_width), dtype=torch.float32)
	batch[0] = torch.from_numpy(flat_file)

	prediction = net(Variable(batch))

	if prediction[0][0] < prediction[0][1]:
		dog_count = dog_count + 1

	total_count = total_count + 1
#	print(batch)
#		print(prediction)

print(dog_count / total_count)
print(total_count)

For the record, this dataset is full of broken files:

https://www.kaggle.com/datasets/karakaggle/kaggle-cat-vs-dog-dataset

OK, so I could get this code to work with 64x64 images:

https://github.com/sjhalayka/pytorch_cats_vs_dogs/tree/8d726d05d127bcb74bdfd05df7998c45a3052a63​

The thing of it is, it only scores 75% correctly.

So it's better than the 32x32? Can you keep pushing? What else can you change that might make the network better?

If you couldn't get anything over 50%, it would mean the network is not working and maybe we could help you debug it. But once you “have signal”, you can play around with it and get improvements. If you don't know what else to try, see what has worked for other people and try to implement any of their ideas. There are lots of things to try: Bigger networks, normalization, regularization, data augmentation, residual connections…

I don't think you are going to get much more from interacting with this forum.

alvaro said:
I don't think you are going to get much more from interacting with this forum.

I'm only good at memes and searches. :D

F python.

Thanks for everything!

fleabay said:

alvaro said:
I don't think you are going to get much more from interacting with this forum.

I'm only good at memes and searches. :D

F python.

Do you do networks in C++?

What do you call the algorithm that uses neural networks, competing with each other, like genetics?