Is
test_x, test2_x, test_y, test2_y = train_test_split(test_x, test_y, test_size=0.001, random_state=134515, stratify=test_y)
doing anything (reshaping?) besides splitting the dataset? I have a trained model and I’m getting completely different accuracies (0.85 vs. 0.52!) if I make a forward pass with the whole dataset or with 99.99% of the dataset (after uncommenting the relevant line in the code below). Obviously the two images not included in test_x after the split don’t explain the difference so I 'm guessing the split command is something else but I can’t figure out what it is!
loading dataset
test = pd.read_csv(‘img_label_list04.csv’)
test.head()
#---------
loading test images
test_img = []
for img_name in tqdm(test[‘image_names’]):
# defining the image path
image_path = ‘d:/Pytorch/Data/leaves/set04/’ + img_name
img = imread(image_path)
# normalizing the pixel values
img = img/255
# resizing the image to (224,224,3)
img = resize(img, output_shape=(224,224,3), mode=‘constant’, anti_aliasing=True)
# converting the type of pixel to float 32
img = img.astype(‘float32’)
# appending the image into the list
test_img.append(img)
converting the list to numpy array
test_x = np.array(test_img)
test_x.shape
defining the target
test_y = train[‘infected_or_not’].values
#-----------------
split the data sest (or not)
test_x, test2_x, test_y, test2_y = train_test_split(test_x, test_y, test_size=0.001, random_state=134515, stratify=test_y)
shape of test data
test_x.shape, test_y.shape
converting the test images into torch format
test_x = test_x.reshape(len(test_x), 3, 224, 224)
test_x  = torch.from_numpy(test_x)
test_y = test_y.astype(int)
test_y = torch.from_numpy(test_y)
data_y = []
label_y = []
inputs,labels = test_x, test_y
for i in tqdm(range(int(test_x.shape[0]/batch_size))):
input_data = inputs[i*batch_size:(i+1)*batch_size]
label_data = labels[i*batch_size:(i+1)*batch_size]
input_data , label_data = Variable(input_data.cuda()),Variable(label_data.cuda())
x = model.features(input_data)
data_y.extend(x.data.cpu().numpy())
label_y.extend(label_data.data.cpu().numpy())
#--------------------------
converting the features into torch format
x_test  = torch.from_numpy(np.array(data_y))
x_test = x_test.view(x_test.size(0), -1)
y_test  = torch.from_numpy(np.array(label_y))
#--------------------------
prediction for test set
prediction_test = []
target_test = []
permutation = torch.randperm(x_test.size()[0])
for i in tqdm(range(0,x_test.size()[0], batch_size)):
indices = permutation[i:i+batch_size]
batch_x, batch_y = x_test[indices], y_test[indices]
if torch.cuda.is_available():
    batch_x, batch_y = batch_x.cuda(), batch_y.cuda()

with torch.no_grad():
    output = model.classifier(batch_x.cuda())

softmax = torch.exp(output).cpu()
prob = list(softmax.numpy())
predictions = np.argmax(prob, axis=1)
prediction_test.append(predictions)
target_test.append(batch_y)

test accuracy
accuracy_test = []
for i in range(len(prediction_test)):
np_target_test=target_test[i].cpu().numpy()
accuracy_test.append(accuracy_score(np_target_test,prediction_test[i]))
accuracy.append(accuracy_score(np_target_test,prediction_test[i]))
print(‘test accuracy: \t’, np.average(accuracy_test))