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# Untitled

redmage123 Oct 12th, 2017 73 Never
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1. #!/usr/bin/env python3
2. import tensorflow as tf
3. import numpy as np
4. import pandas as pd
5. import matplotlib.pyplot as plt
6. from sklearn import model_selection
7. import sys
8.
10.
11. # Shuffle our data
12. gender_df = gender_df.sample(frac=1).reset_index(drop=True)
13.
14. # We'll go ahead and split the data set into training and testing parts.
15. # 70 per cent will go to training, the rest to testing.
16. train_x,test_x, train_y, test_y = model_selection.train_test_split(gender_df['HEIGHT'],gender_df['GENDER'],test_size = 0.3)
17.
18. n_samples = train_x.shape[0]
19.
20. # These will be the placeholders for the testing and training data
21. x = tf.placeholder(tf.float32)
22. y = tf.placeholder(tf.float32)
23.
24. # Variables for the weight and bias.
25. W = tf.Variable(0,dtype = tf.float32)
26. b = tf.Variable(0,dtype = tf.float32)
27.
28. # This is our activation function to determine the probability
29. # of our gender based on height.
30. activation = tf.nn.sigmoid((W * x) + b)
31.
32. # Set our alpha value for the optimizer.
33. learning_rate = 0.001
34.
35. # cross_entropy is our cost function.
36. cross_entropy = tf.reduce_mean(-(y*tf.log(activation) + (1 - y) * tf.log(1-activation)))
37.
38. # We'll use a standard gradient descent optimizer.
40.
41. with tf.Session() as sess:
42.     sess.run(tf.global_variables_initializer())
43.
44. # Now train our jodel.
45.     for epoch in range(1000):
46.         _,l = sess.run([train_step, cross_entropy], feed_dict = {x: train_x, y:train_y})
47.     if epoch % 50 == 0:
48.             print ('loss = %f' %(l))
49.
50. # Now let's see how our model performed on the test data.
51.     correct = tf.equal(tf.argmax(activation,1), tf.argmax(y,1))
52.     accuracy = tf.reduce_mean(tf.cast(correct,'float'))
53.     print ('Accuracy: ', sess.run(accuracy,feed_dict = {x: test_x, y:test_y}))
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