In [1]:

    

from physlearn.NeuralNet.NeuralNetPro import NeuralNetPro
import numpy
import copy
import tensorflow as tf
from math import pi
import matplotlib.pyplot as plt
from physlearn.Optimizer import DifferentialEvolution
from physlearn.Optimizer import RandomEvolutionStrategy
%matplotlib inline


    

In [2]:

    

x_train = numpy.linspace(0, 2 * pi, 15).reshape(1, 15)
y_train = numpy.sin(x_train[0]).reshape(1, 15)


    

In [3]:

    

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


    

In [4]:

    

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


    

    
Out[4]:





[<matplotlib.lines.Line2D at 0x7f2f87fd4a90>]






    

In [5]:

    

net = NeuralNetPro(-1, 1)


    

In [6]:

    

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


    

In [7]:

    

net.compile()


    

In [8]:

    

net.set_train_type('prediction')


    

In [9]:

    

dim = net.return_unroll_dim()


    

In [10]:

    

net.set_random_matrixes()


    

In [11]:

    

net.calculate_cost(x_train, y_train)


    

    
Out[11]:





0.82819698289912891

In [12]:

    

def cost(params):
    net.roll_matrixes(params)
    return net.calculate_cost(x_train, y_train)


    

In [13]:

    

res = DifferentialEvolution.optimize(cost, 500, dim, 300, f=0.3, p=0.9, min_element=-1, max_element=1)


    

    




100%|██████████| 300/300 [00:55<00:00,  5.51it/s]

In [14]:

    

net.roll_matrixes(res)


    

In [15]:

    

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[15]:





[<matplotlib.lines.Line2D at 0x7f2f88000a20>]






    

In [16]:

    

net.calculate_cost(x_train, y_train)


    

    
Out[16]:





0.02390859990915839

In [21]:

    

net.roll_matrixes(res)
res_new = RandomEvolutionStrategy.optimize(cost, dim, 0.01, 40, 30000, 0.005, x0 = copy.deepcopy(res))
cost_list = res_new[0]
params = res_new[1]
plt.plot(cost_list)


    

    




100%|██████████| 30000/30000 [11:54<00:00, 42.18it/s]






    
Out[21]:





[<matplotlib.lines.Line2D at 0x7f2f800d0f98>]






    

In [22]:

    

net.roll_matrixes(params)


    

In [23]:

    

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[23]:





[<matplotlib.lines.Line2D at 0x7f2f8264eb00>]






    

In [24]:

    

net.calculate_cost(x_train, y_train)


    

    
Out[24]:





0.0059302775082353178

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