In [1]:

    

import numpy
from physlearn.NeuralNet.NeuralNet import NeuralNet
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline


    

In [2]:

    

x = numpy.array([[-2, -1, 0, 1, 2]])
y = numpy.array([[0, 0, 0, 1, 1]])


    

In [3]:

    

net = NeuralNet(-1, 1)


    

In [4]:

    

net.add_input_layer(1)
net.add(5, tf.sigmoid)
net.add_output_layer(1, tf.sigmoid)


    

In [5]:

    

net.compile()


    

In [6]:

    

net.set_train_type('logistic')


    

In [7]:

    

net.run(x)


    

    
Out[7]:





array([[ 0.35129324,  0.38970408,  0.43131234,  0.47554027,  0.51545544]])

In [8]:

    

net.calculate_cost(x, y)


    

    
Out[8]:





2.8970178614065323

In [9]:

    

cost_list = net.train(x, y, 5, 1000, 1)


    

    




100%|██████████| 1000/1000 [00:00<00:00, 1753.67it/s]

In [10]:

    

plt.plot(cost_list)


    

    
Out[10]:





[<matplotlib.lines.Line2D at 0x7f575c1dab70>]






    

In [16]:

    

net.run([[10000]])


    

    
Out[16]:





array([[ 0.99986848]])

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