Vineet Kaushik - 1 year ago 160

Python Question

I am trying to use a deep neural network architecture to classify against a binary label value - -1 and +1. Here is my code to do it in

`tensorflow`

`import tensorflow as tf`

import numpy as np

from preprocess import create_feature_sets_and_labels

train_x,train_y,test_x,test_y = create_feature_sets_and_labels()

x = tf.placeholder('float', [None, 5])

y = tf.placeholder('float')

n_nodes_hl1 = 500

n_nodes_hl2 = 500

n_nodes_hl3 = 500

n_classes = 1

batch_size = 100

def neural_network_model(data):

hidden_1_layer = {'weights':tf.Variable(tf.random_normal([5, n_nodes_hl1])),

'biases':tf.Variable(tf.random_normal([n_nodes_hl1]))}

hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),

'biases':tf.Variable(tf.random_normal([n_nodes_hl2]))}

hidden_3_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),

'biases':tf.Variable(tf.random_normal([n_nodes_hl3]))}

output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),

'biases':tf.Variable(tf.random_normal([n_classes]))}

l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['biases'])

l1 = tf.nn.relu(l1)

l2 = tf.add(tf.matmul(l1, hidden_2_layer['weights']), hidden_2_layer['biases'])

l2 = tf.nn.relu(l2)

l3 = tf.add(tf.matmul(l2, hidden_3_layer['weights']), hidden_3_layer['biases'])

l3 = tf.nn.relu(l3)

output = tf.transpose(tf.add(tf.matmul(l3, output_layer['weights']), output_layer['biases']))

return output

def train_neural_network(x):

prediction = neural_network_model(x)

cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(prediction, y))

optimizer = tf.train.AdamOptimizer().minimize(cost)

hm_epochs = 10

with tf.Session() as sess:

sess.run(tf.initialize_all_variables())

for epoch in range(hm_epochs):

epoch_loss = 0

i = 0

while i < len(train_x):

start = i

end = i + batch_size

batch_x = np.array(train_x[start:end])

batch_y = np.array(train_y[start:end])

_, c = sess.run([optimizer, cost], feed_dict={x: batch_x,

y: batch_y})

epoch_loss += c

i+=batch_size

print('Epoch', epoch, 'completed out of', hm_epochs, 'loss:', epoch_loss)

# correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))

# accuracy = tf.reduce_mean(tf.cast(correct, 'float'))

print (test_x.shape)

accuracy = tf.nn.l2_loss(prediction-y,name="squared_error_test_cost")/test_x.shape[0]

print('Accuracy:', accuracy.eval({x: test_x, y: test_y}))

train_neural_network(x)

This is the output I get when I run this:

`('Epoch', 0, 'completed out of', 10, 'loss:', -8400.2424869537354)`

('Epoch', 1, 'completed out of', 10, 'loss:', -78980.956665039062)

('Epoch', 2, 'completed out of', 10, 'loss:', -152401.86713409424)

('Epoch', 3, 'completed out of', 10, 'loss:', -184913.46441650391)

('Epoch', 4, 'completed out of', 10, 'loss:', -165563.44775390625)

('Epoch', 5, 'completed out of', 10, 'loss:', -360394.44857788086)

('Epoch', 6, 'completed out of', 10, 'loss:', -475697.51550292969)

('Epoch', 7, 'completed out of', 10, 'loss:', -588638.92993164062)

('Epoch', 8, 'completed out of', 10, 'loss:', -745006.15966796875)

('Epoch', 9, 'completed out of', 10, 'loss:', -900172.41955566406)

(805, 5)

('Accuracy:', 5.8077128e+09)

I don't understand if the values I am getting are correct as there is a real dearth of non-MNIST binary classification examples. The accuracy is nothing like what I expected. I was expecting a percentage instead of that large value.

I am also somewhat unsure of the theory behind machine learning which is why I can't tell the correctness of my approach using tensorflow.

Can someone please tell me if my approach towards binary classification is correct?

Also is the accuracy part of my code correct?

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Answer Source

From this:

a binary label value - -1 and +1

. . . I am assuming your values in `train_y`

and `test_y`

are actually -1.0 and +1.0

This is not going to work very well with your chosen loss function `sigmoid_cross_entropy_with_logits`

- which assumes 0.0 and +1.0. The negative `y`

values are causing mayhem! However, the loss function choice is good for binary classification. I suggest change your `y`

values to 0 and 1.

In addition, technically the output of your network is not the final prediction. The loss function `sigmoid_cross_entropy_with_logits`

is designed to work with a network with sigmoid transfer function in the output layer, although you have got it right that the loss function is applied *before* this is done. So your training code appears correct

I'm not 100% sure about the `tf.transpose`

though - I would see what happens if you remove that, personally I.e.

```
output = tf.add(tf.matmul(l3, output_layer['weights']), output_layer['biases'])
```

Either way, this is the "logit" output, but not your prediction. The value of `output`

can get high for very confident predictions, which probably explains your very high values later due to missing the sigmoid function. So add a prediction tensor (this represents the probability/confidence that the example is in the positive class):

```
prediction = tf.sigmoid(output)
```

You can use that to calculate accuracy. Your accuracy calculation should not be based on L2 error, but sum of correct values - closer to the code you had commented out (which appears to be from a multiclass classification). For a comparison with true/false for binary classification, you need to threshold the predictions, and compare with the true labels. Something like this:

```
predicted_class = tf.greater(prediction,0.5)
correct = tf.equal(predicted_class, tf.equal(y,1.0))
accuracy = tf.reduce_mean( tf.cast(correct, 'float') )
```

The accuracy value should be between 0.0 and 1.0. If you want as a percentage, just multiply by 100 of course.