1

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 np import tflearn

```
X = [[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 network
net = 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 model
model = 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
```