i am trying to train a deep neural network to figure out that if there is a 1 and 0 present in the first two columns of X that the output is 1 otherwise its 0. but im only getting a 75% accuracy on the model!import numpy as npimport tflearnX = [[0, 0, 1],    [0, 1, 1],    [1, 0, 1],    [1, 1, 1]]Y = [[0, 1],    [1, 1],    [1, 0],    [0, 1]]Xtest = np.array([[1, 1, 1],                  [0, 1, 1],                  [1, 0, 1],                  [0, 1, 1]])# Build neural networknet = tflearn.input_data(shape=[None, 3])net = tflearn.fully_connected(net, 32, activation='sigmoid')net = tflearn.fully_connected(net, 32, activation='sigmoid')net = tflearn.fully_connected(net, 2, activation='softmax')net = tflearn.regression(net, optimizer='adam')# Define modelmodel = tflearn.DNN(net)# Start training (apply gradient descent algorithm)model.fit(X, Y, n_epoch=5000, batch_size=16, show_metric=True)pred = model.predict(Xtest)for i in range(4):    print(pred[i][0])The output should be: [0, 1, 1, 1]Training Step: 4999  | total loss: 0.50493 | time: 0.004s| Adam | epoch: 4999 | loss: 0.50493 - acc: 0.7813 -- iter: 4/4--    0.01631585881114006    0.4872587323188782    0.9684665203094482    0.019177177920937538
Max 75% accuracy! help!
machine learning;accuracy