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This notebook builds a model graph to learn XOR, then trains the model.

First, do some imports.



In [1]:

    
import argparse
import math

import numpy as np

import tensorflow as tf

tf.logging.set_verbosity(tf.logging.INFO)

Now, we'll define a function to build our model graph. Note the definition of the loss and training ops.



In [2]:

    
def make_graph(features, labels, num_hidden=8):
  hidden_weights = tf.Variable(tf.truncated_normal(
      [2, num_hidden],
      stddev=1/math.sqrt(2)
  ))

  # Shape [4, num_hidden]
  hidden_activations = tf.nn.relu(tf.matmul(features, hidden_weights))

  output_weights = tf.Variable(tf.truncated_normal(
      [num_hidden, 1],
      stddev=1/math.sqrt(num_hidden)
  ))

  # Shape [4, 1]
  logits = tf.matmul(hidden_activations, output_weights)

  # Shape [4]
  predictions = tf.sigmoid(tf.squeeze(logits))
  loss = tf.reduce_mean(tf.square(predictions - tf.to_float(labels)))

  gs = tf.Variable(0, trainable=False)
  train_op = tf.train.GradientDescentOptimizer(0.2).minimize(
      loss, global_step=gs)

  return train_op, loss, gs

Build the graph -- define the placeholders, and call make_graph(). Then add an op to init the variables.



In [3]:

    
graph = tf.Graph()
num_steps = 5000

with graph.as_default():
  features = tf.placeholder(tf.float32, shape=[4, 2])
  labels = tf.placeholder(tf.int32, shape=[4])

  train_op, loss, gs = make_graph(features, labels)
  init = tf.global_variables_initializer()

In a Session, run a training loop using a small input dataset. You'll see the final loss value output after a short pause.



In [4]:

    
with tf.Session(graph=graph) as sess:
  init.run()
  step = 0
  xy = np.array([
      [True, False],
      [True, True],
      [False, False],
      [False, True]
  ], dtype=np.float)
  y_ = np.array([True, False, False, True], dtype=np.int32)
  while step < num_steps:

    _, step, loss_value = sess.run(
        [train_op, gs, loss],
        feed_dict={features: xy, labels: y_}
    )
  tf.logging.info('Final loss is: {}'.format(loss_value))









    



INFO:tensorflow:Final loss is: 0.0627002418041