In [21]:

    

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


    

In [22]:

    

x_train = numpy.linspace(0, 2 * pi, 30).reshape(1, 30)
y_train = numpy.sin(x_train[0]).reshape(1, 30)


    

In [23]:

    

x_cv = numpy.linspace(0, 2 * pi, 1000).reshape(1, 1000)
y_cv = numpy.sin(x_cv[0]).reshape(1, 1000)


    

In [24]:

    

plt.plot(x_train[0], y_train[0], 'x', color='red')


    

    
Out[24]:





[<matplotlib.lines.Line2D at 0x21d58a48940>]






    

In [25]:

    

net = NeuralNet(-1, 1)


    

In [26]:

    

net.add_input_layer(1)
net.add(10, tf.sigmoid)
net.add_output_layer(1, net.linear)


    

In [27]:

    

net.compile()


    

In [28]:

    

net.set_train_type('prediction')


    

In [29]:

    

cost_list = net.train(x_train, y_train, 5, 30000, 0.1)
plt.plot(list(map(lambda item: item ** (-1), cost_list)))


    

    




100%|██████████████████████████████████████████████████████████████████████████| 30000/30000 [00:10<00:00, 2952.58it/s]






    
Out[29]:





[<matplotlib.lines.Line2D at 0x21d58d1f400>]






    

In [30]:

    

net.calculate_cost(x_train, y_train)


    

    
Out[30]:





0.001500068960113422

In [31]:

    

y_pred = net.run(x_cv)
plt.plot(x_train[0], y_train[0], 'x', color='red')
plt.plot(x_cv[0], y_pred[0])


    

    
Out[31]:





[<matplotlib.lines.Line2D at 0x21d58cf1cc0>]






    

In [32]:

    

res = net.unroll_matrixes()


    

In [33]:

    

res[0]


    

    
Out[33]:





[(array([[-1.15091717],
         [ 0.22875304],
         [ 0.37095079],
         [-0.88138196],
         [-0.45491523],
         [-0.96166073],
         [-2.16725196],
         [-0.45716576],
         [-0.35028395],
         [-1.77260216]]), array([[ 1.66324539],
         [-0.12660553],
         [ 0.3926052 ],
         [-0.92717115],
         [ 1.79901331],
         [ 5.69109085],
         [ 1.01958073],
         [ 1.82154574],
         [-0.02795594],
         [ 6.17501362]])),
 (array([[ 1.89630988,  0.14896772,  0.08176532,  0.42900751, -0.84548297,
          -2.52183042, -2.32210254, -0.86795163, -0.80078559,  3.00372402]]),
  array([[1.26520015]]))]

In [34]:

    

unroll_vector = res[2]


    

In [35]:

    

net.roll_matrixes(unroll_vector)


    

In [36]:

    

sess = net.return_session()
output = net.return_graph()


    

In [37]:

    

y_pred = sess.run(output, {net.x: x_cv})
plt.plot(x_train[0], y_train[0], 'x', color='red')
plt.plot(x_cv[0], y_pred[0])


    

    
Out[37]:





[<matplotlib.lines.Line2D at 0x21d58c77a20>]






    

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