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Apr 12th, 2017
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  1. import os
  2. import numpy as np
  3. import tensorflow as tf
  4. from tensorflow.python.framework import ops
  5. import matplotlib.pyplot as plt
  6. %matplotlib inline
  7. ops.reset_default_graph()
  8. import tqdm
  9. sess =tf.Session()
  10.  
  11. x_ = tf.placeholder(name="input", shape=[None, 2], dtype=tf.float32)
  12. y_ = tf.placeholder(name= "output", shape=[None, 1], dtype=tf.float32)
  13.  
  14. hidden_neurons = 15
  15. w1 = tf.Variable(tf.random_uniform(shape=[2,hidden_neurons ]))
  16. b1 = tf.Variable(tf.constant(value=0.0, shape=[hidden_neurons ], dtype=tf.float32))
  17. layer1 = tf.nn.relu(tf.add(tf.matmul(x_, w1), b1))
  18.  
  19. w2 = tf.Variable(tf.random_uniform(shape=[hidden_neurons ,1]))
  20. b2 =  tf.Variable(tf.constant(value=0.0, shape=[1], dtype=tf.float32))
  21.  
  22. nn_output = tf.nn.relu(tf.add(tf.matmul(layer1, w2), b2))
  23. gd = tf.train.GradientDescentOptimizer(0.001)
  24. loss =  tf.reduce_mean(tf.square(nn_output- y_))
  25. train_step = gd.minimize(loss)
  26. init = tf.global_variables_initializer()
  27. sess.run(init)
  28. x = np.array([[0,0],[1,0],[0,1],[1,1]])
  29. y = np.array([[0],[1],[1],[0]])
  30. for _ in range(20000):
  31.     sess.run(train_step, feed_dict={x_:x, y_:y})
  32.    
  33. print(sess.run(nn_output, feed_dict={x_:x}))
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