In [1]:

    

#!/usr/bin/python2
""" Linear Regression Example """

from __future__ import absolute_import, division, print_function

import tflearn

# Regression data
X = [3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167, 7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1]
Y = [1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221, 2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3]

# Linear Regression graph
input_ = tflearn.input_data(shape=[None])
linear = tflearn.single_unit(input_)
regression = tflearn.regression(linear, optimizer = 'sgd', loss='mean_square',
                                        metric='R2', learning_rate=0.01);
m = tflearn.DNN(regression)
m.fit(X, Y, n_epoch=1000, show_metric = True, snapshot_epoch=False )

print("\n Regression result:")
print("Y = " + str(m.get_weights(linear.W)) +
        "*X + " + str(m.get_weights(linear.b)))
print("\n Test Prediction for x = 3.2, 3.3, 3.4")
print(m.predict([3.2, 3.3, 3.4]))


    

    




Training Step: 1000  | total loss: 0.15393
| SGD | epoch: 1000 | loss: 0.15393 - R2: 0.9918 -- iter: 17/17

 Regression result:
Y = [ 0.24822162]*X + [ 0.82300013]

 Test Prediction for x = 3.2, 3.3, 3.4
[1.617309331893921, 1.6421314477920532, 1.666953682899475]

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#Training Logica Operatos
import tensorflow as tf
import tflearn as t

from __future__ import absolute_import, division, print_function

X = [[0.], [1.]]
Y = [[1.], [0.]]