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2868b0b Mar 31, 2016
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# -*- coding: utf-8 -*-
"""
Simple Example to train logical operators
"""
from __future__ import absolute_import, division, print_function
import tensorflow as tf
import tflearn
# Logical NOT operator
X = [[0.], [1.]]
Y = [[1.], [0.]]
# Graph definition
with tf.Graph().as_default():
g = tflearn.input_data(shape=[None, 1])
g = tflearn.fully_connected(g, 128, activation='linear')
g = tflearn.fully_connected(g, 128, activation='linear')
g = tflearn.fully_connected(g, 1, activation='sigmoid')
g = tflearn.regression(g, optimizer='sgd', learning_rate=2.,
loss='mean_square')
# Model training
m = tflearn.DNN(g)
m.fit(X, Y, n_epoch=100, snapshot_epoch=False)
# Test model
print("Testing NOT operator")
print("NOT 0:", m.predict([[0.]]))
print("NOT 1:", m.predict([[1.]]))
# Logical OR operator
X = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]]
Y = [[0.], [1.], [1.], [1.]]
# Graph definition
with tf.Graph().as_default():
g = tflearn.input_data(shape=[None, 2])
g = tflearn.fully_connected(g, 128, activation='linear')
g = tflearn.fully_connected(g, 128, activation='linear')
g = tflearn.fully_connected(g, 1, activation='sigmoid')
g = tflearn.regression(g, optimizer='sgd', learning_rate=2.,
loss='mean_square')
# Model training
m = tflearn.DNN(g)
m.fit(X, Y, n_epoch=100, snapshot_epoch=False)
# Test model
print("Testing OR operator")
print("0 or 0:", m.predict([[0., 0.]]))
print("0 or 1:", m.predict([[0., 1.]]))
print("1 or 0:", m.predict([[1., 0.]]))
print("1 or 1:", m.predict([[1., 1.]]))
# Logical AND operator
X = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]]
Y = [[0.], [0.], [0.], [1.]]
# Graph definition
with tf.Graph().as_default():
g = tflearn.input_data(shape=[None, 2])
g = tflearn.fully_connected(g, 128, activation='linear')
g = tflearn.fully_connected(g, 128, activation='linear')
g = tflearn.fully_connected(g, 1, activation='sigmoid')
g = tflearn.regression(g, optimizer='sgd', learning_rate=2.,
loss='mean_square')
# Model training
m = tflearn.DNN(g)
m.fit(X, Y, n_epoch=100, snapshot_epoch=False)
# Test model
print("Testing AND operator")
print("0 and 0:", m.predict([[0., 0.]]))
print("0 and 1:", m.predict([[0., 1.]]))
print("1 and 0:", m.predict([[1., 0.]]))
print("1 and 1:", m.predict([[1., 1.]]))
'''
Going further: Graph combination with multiple optimizers
Create a XOR operator using product of NAND and OR operators
'''
# Data
X = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]]
Y_nand = [[1.], [1.], [1.], [0.]]
Y_or = [[0.], [1.], [1.], [1.]]
# Graph definition
with tf.Graph().as_default():
# Building a network with 2 optimizers
g = tflearn.input_data(shape=[None, 2])
# Nand operator definition
g_nand = tflearn.fully_connected(g, 32, activation='linear')
g_nand = tflearn.fully_connected(g_nand, 32, activation='linear')
g_nand = tflearn.fully_connected(g_nand, 1, activation='sigmoid')
g_nand = tflearn.regression(g_nand, optimizer='sgd',
learning_rate=2.,
loss='binary_crossentropy')
# Or operator definition
g_or = tflearn.fully_connected(g, 32, activation='linear')
g_or = tflearn.fully_connected(g_or, 32, activation='linear')
g_or = tflearn.fully_connected(g_or, 1, activation='sigmoid')
g_or = tflearn.regression(g_or, optimizer='sgd',
learning_rate=2.,
loss='binary_crossentropy')
# XOR merging Nand and Or operators
g_xor = tflearn.merge([g_nand, g_or], mode='elemwise_mul')
# Training
m = tflearn.DNN(g_xor)
m.fit(X, [Y_nand, Y_or], n_epoch=400, snapshot_epoch=False)
# Testing
print("Testing XOR operator")
print("0 xor 0:", m.predict([[0., 0.]]))
print("0 xor 1:", m.predict([[0., 1.]]))
print("1 xor 0:", m.predict([[1., 0.]]))
print("1 xor 1:", m.predict([[1., 1.]]))