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tflearn/examples/basics/logical.py
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# -*- coding: utf-8 -*- | |
""" | |
Simple Example to train logical operators | |
""" | |
from __future__ import absolute_import, division, print_function | |
import tensorflow.compat.v1 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.]])) |