from ingine import ann
"""getting dataset"""
(X_train, Y_train), (X_test, Y_test) = get_dataset()
categorizer = ann.get_categorizer(X_train, y_train, num_cat=10)
"""check out how it works!"""print(categorizer(X_train[0]), Y_train[0])
Regression:
from ingine import ann
"""getting dataset"""
(X_train, Y_train), (X_test, Y_test)****= get_dataset()
regression = ann.get_regression(X_train, y_train)
"""check out how it works!"""print(regression(X_train[0]), Y_train[0])
Custom layer configuration:
from ingine import ann
from keras.layers import Dense
"""getting dataset"""
(X_train, Y_train), (X_test, Y_test) = get_dataset()
# defining layers
layers = [Dense(100, input_dim=100, activation="softsign", kernel_initializer="normal"),
Dense(20, activation="softsign", kernel_initializer="normal"),
Dense(10, activation="softsign", kernel_initializer="normal"),
Dense(100, activation="softsign", kernel_initializer="normal")]
customnn = ann.get_customnn(X_train, Y_train, layers= layers)
"""check out how it works!"""print(customnn(X_train[0]), Y_train[0])
Evolutional optimizer:
from ingine import ga
import random as rnd
"""define an example creature"""
data = [1, 2, 3, 4]
"""define a representation function of creature"""defcreate_individual(data):
return data[:]
"""define a crossover function"""defcrossover(creature1, creature2):
r1 = [rnd.randint(1, 10) for _ inrange(4)]
r2 = [rnd.randint(1, 10) for _ inrange(4)]
return r1, r2
"""define an mutation function"""defmutate(creature):
a = rnd.randint(0, len(creature)-1)
b = rnd.randint(0, len(creature)-1)
creature[a], creature[b] = creature[a] +1, creature[b] +1"""define a selection function"""defselection(population):
return rnd.choice(population)
"""define a fitness function"""deffitness(creature, data):
returnabs(sum(creature) -100)
"""getting an optimizer"""
optimiser = ga.get_optimizer(data,
fitness,
maximise_fitness=False,
create_individual= create_individual,
mutate= mutate,
crossover= crossover)
"""check out how it works!"""
res = optimiser()[1]
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