np.random.seed(18)
# 1,2 initializing
train_size = 20000
batch_size = 100
epochs = 300
lr = 0.001
verbose = 1
neurons = [15, 5]
opt_name = "Adam"
optimizer = Adam(lr=lr)
goal_loss = 0.0001
(x_train, y_train), (x_test,
y_test) = dataset3.load_data(train_size=train_size,
show=True)
model = Sequential()
model.add(
Dense(neurons[0],
input_dim=2,
kernel_initializer='he_uniform',
bias_initializer='he_uniform',
activation='relu'))
model.add(
Dense(neurons[1],
kernel_initializer='he_uniform',
bias_initializer='he_uniform',
activation='linear'))
from mpl_toolkits.mplot3d import Axes3D
import lab0.src.dataset3 as dataset3
print(
"\n\nПостроим поверхность ошибки в плоскости двух параметров: ширина РБФ-функции spread и"
)
print("объем обучающей выборки.\n")
maes = [0, 0, 0, 0]
train_size = [24000, 12000, 5000, 2000]
std = [0.00035, 0.001, 0.0035, 0.01]
for j in range(0, 4):
(x_train, y_train), (x_test,
y_test) = dataset3.load_data(train_size=train_size[j],
show=False)
pnn = algorithms.PNN(std=std[j], verbose=False)
pnn.train(x_train, y_train)
y_predicted = pnn.predict(x_test)
mae = (np.abs(y_test - y_predicted)).mean()
plt_x_zero = np.empty(0)
plt_y_zero = np.empty(0)
plt_x_one = np.empty(0)
plt_y_one = np.empty(0)
# Task 2, part 1
import matplotlib.pyplot as plt
import numpy as np
from neupy import algorithms
import lab0.src.dataset3 as dataset3
print(
"\n\nСравним и визуализируем случаи, когда spread больше, меньше и равен оптимальному значению для"
)
print("случая деления плоскости 2 класса\nВсе графики подписаны\n")
(x_train, y_train), (x_test, y_test) = dataset3.load_data(train_size=12000,
show=False)
titles = [
"\n\nspread greater than necessary", "\n\nspread optimal",
"\n\nspread less than necessary"
]
spreads = [0.1, 0.001, 0.0001]
for spread, title in zip(spreads, titles):
pnn = algorithms.PNN(std=spread, verbose=False)
pnn.train(x_train, y_train)
y_predicted = pnn.predict(x_test)
mae = (np.abs(y_test - y_predicted)).mean()
plt_x_zero = np.empty(0)
plt_y_zero = np.empty(0)
import numpy as np
from keras.layers import Dense
from keras.models import Sequential
import sys
sys.path.append("../..")
from lib.custom import hard_lim
from lab0.src import dataset3
train_size = 4000
# load data from dataset1
(x_train, y_train), (x_test, y_test) = dataset3.load_data(train_size=train_size, show=True)
# creating model with weights initialization
model = Sequential()
model.add(Dense(12, input_dim=x_train.shape[1], activation=hard_lim, name='First',
weights=list([np.array([[0.0, 0.0, 1.0, 1.0, 1.0, -1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0]], dtype=float),
np.array([-0.6, -0.2, -0.6, -0.2, -1.2, -0.4, -0.45, -0.35, -0.55, -0.45, -0.35, -0.25],
dtype=float)])))
model.add(Dense(2, activation=hard_lim, name='Second',
weights=list([np.array([[-1.0, 1.0], # y1
[1.0, 0.0], # y2
[-1.0, 0.0], # y3
[1.0, 0.0], # y4
[0.0, -1.0], # y5
[0.0, -1.0], # y6
[1.0, 0.0], # y7
[-1.0, 0.0], # y8