domacaNeuronky

#f(x,y)=(x-y^2)/(x+y+1) interval <0,2>,<0,2>

import numpy as np
import tensorflow as tf

from keras.layers import Dense, Activation
from keras.models import Sequential

def funkcia(x, y):
    return (x - y**2)/(x + y + 1)

model = Sequential()
model.add(Dense(4, activation='tanh', input_dim=2))
model.add(Dense(3, activation='tanh'))
model.add(Dense(1, activation='tanh'))
model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['accuracy'])

# generovanie data a labels
data = []

for i in np.arange(0.0, 10.0, 0.2):
    for j in np.arange(0.0, 10.0, 0.2):
        data.append([i,j])

#data = np.random.random((1000, 2))

#print(np.matrix(data))

labels = []
for i in range(len(data)):
    labels.append(funkcia(data[i][0], data[i][1]))

dataMax = np.max(data)
labelsMax = np.max(labels)
maximum = max(dataMax, labelsMax)

data = data / maximum
labels = labels / maximum

#print(np.matrix(labels))

#aa=model.weights
#print(aa)

# Train the model, iterating on the data in batches of 32 samples
model.fit(data, labels, epochs=100, batch_size=32)

#aa=model.weights
#print(aa)

_, accuracy = model.evaluate(data, labels)
print('Accuracy: %.2f' % (accuracy*100))
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Experimenty
# web:   https://www.tensorflow.org/guide/keras

#aa=model.weights
#print(aa)