In [1]:

    

# Необходмые команды импорта.
import sys
#import os
sys.path.append('../physlearn/')
sys.path.append('../source')
import numpy as np
from numpy import linalg as LA
import tensorflow as tf
from matplotlib import pylab as plt
from IPython.display import clear_output
from physlearn.NeuralNet.NeuralNet import NeuralNet
from physlearn.Optimizer.NelderMead.NelderMead import NelderMead
from ann_solver import AnnSolver
import d1_osc
import ann_constructor
import math_util
from visualiser import Visualiser

# Model Parameters
n_hid1 = 15
m = 200 # размер сеток обучения
M = 6 # количество выходных нейронов(базисных функций)
a = -10
b = 10
#n_hid2 = 9
%matplotlib inline

# ANN
net, net_output, net_sum, sess = ann_constructor.return_separated_net_expressions(M, n_hid1)
# Выражение, определяющеие образ выходов сети при действии гамильтонианом. Task-dependant
dim = net.return_unroll_dim()
print(dim)

solver = AnnSolver(ann = net, ground_method='gaus')
solver.define_approximation_grid(a, b, m)
solver.define_linearity_grid(M)
solver.compile()
J = solver.get_cost_func()

trial_func = solver.get_trial_func()
func_sum = tf.reduce_sum(input_tensor=trial_func, axis=0)
images = solver.get_images()
images_sum = tf.reduce_sum(input_tensor=images, axis=0)


# Оптимизация
opt_nm = NelderMead(-2.5,2.5)
opt_nm.set_epsilon_and_sd(0.3, 100)

def opt(J, dim, n_it, eps):
    optimisation_result = opt_nm.optimize(J, dim+1, n_it, eps)
    return optimisation_result


    

    




D:\Anaconda\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters






    




772

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optimisation_result = opt(J, dim, int(3e6), 1e-3)
print("J after optimisation: ", J(optimisation_result.x))
print("Информация: ", optimisation_result)


    

    




.... 153055 (5%) 174.943 it\s

In [4]:

    

xi_obs = np.linspace(a, b, 1000, endpoint=True)
vis = Visualiser(solver)
vis.plot_four(xi_obs.reshape(1, 1000))


    

    






<Figure size 432x288 with 0 Axes>






    

In [8]:

    

y1 = net.calc(trial_func, {net.x : xi_obs.reshape(1, 1000)})
for i in range(M):
    func_i = y1[i,:]
    plt.plot(xi_obs.reshape(1, 1000)[0], func_i)


    

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