Model fit after each "fold" in LOOCV? Multilabel LOOCV by hand

Question

I am wondering if it is an issue (possible data leakage?) when implementing leave one out cross validation by hand if the model is fit each iteration after testing on each fold? It seems like if the model is trained on all data except for "X" and after testing on "X" the model is trained on all data other than "Y" and tested on "Y" it has seen "Y" on the first iteration. Is this actually a problem, and does my implementation of LOOCV by hand appear to be correct?

Thanks for your time!

    i = 0
	j = 0
	for i in range(0, 41):
		X_copy = X_orig[(i):(i+1)]  #Slice the ith element from the numpy array
		y_copy = y_orig[(i):(i+1)]
		X_model = X_orig 
		y_model = y_orig
		X_model = np.delete(X_model, i, axis = 0)  
		y_model = np.delete(y_model, i, axis = 0)
		model.fit(X_model, y_model, epochs=115, batch_size=28, verbose = 0)   #verbose = 0 removes learning info
		prediction = model.predict(X_copy)
		prediction[prediction>=0.5] = 1
		prediction[prediction<0.5] = 0
		print(prediction, y_copy)
		if np.array_equal(y_copy, prediction):
			j = j + 1
			#print(y_copy, prediction)
		if np.not_equal:
			#print(y_copy, prediction)
			pass
	print(j/41)  #For 41 samples in dataset

Answer 1

Why don't you use this?

from sklearn.model_selection import LeaveOneOut

loo = LeaveOneOut()
model =...

test_fold_predictions = []
for train_index, test_index in loo.split(X):
   X_train, X_test = X[train_index], X[test_index]
   y_train, y_test = y[train_index], y[test_index]
   model.fit(X_train, y_train)
   test_fold_predictions.append(model.predict(X_test))

EDIT

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD

model = Sequential()
model.add(Dense(5000, activation='relu', input_dim=X_train.shape[1]))
model.add(Dropout(0.1))
model.add(Dense(600, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(y_train.shape[1], activation='sigmoid'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy',
              optimizer=sgd)

from sklearn.model_selection import LeaveOneOut

loo = LeaveOneOut()
test_fold_predictions = []
for train_index, test_index in loo.split(X):
   X_train, X_test = X[train_index], X[test_index]
   y_train, y_test = y[train_index], y[test_index]
   model.fit(X_train, y_train, epochs=5, batch_size=2000)
   test_fold_predictions.append(model.predict(X_test))