reg = 1e-4
seed = 783
ng_eps = 10e-6
l_eps = 1e-6
max_iter = 1000
verbose = 0
#############################
#########################################
# Construct the Monk1 objective function
# and define a w0 with the seed
#########################################
np.random.seed(seed=seed)
print("Load Monk DataSet")
X_train, Y_train = load_monk(monk, "train")
print("Build the model")
model = Mlp()
model.add(4, input=17, kernel_initializer=0.003, kernel_regularizer=reg)
model.add(1, kernel_initializer=0.003, kernel_regularizer=reg)
#############################
# L-BFGS
#############################
c1 = 1e-4
c2 = .9
m = 30
ln_maxiter = 100
#############################
optimizer = LBFGS(m=m,
c1=c1,
import sys
from os import path
sys.path.insert(0, "../")
sys.path.insert(0, "./")
from isanet.model import Mlp
from isanet.optimizer import SGD
from isanet.datasets.monk import load_monk
from isanet.utils.model_utils import printMSE, printAcc, plotHistory
import numpy as np
import time
print("Load Monk DataSet")
X_train, Y_train = load_monk("2", "train")
X_test, Y_test = load_monk("2", "test")
print("Build the model")
tk_reg = 0 #.000001
w_start = 0.7
model = Mlp()
model.add(6, input=17, kernel_initializer=w_start, kernel_regularizer=tk_reg)
model.add(6, kernel_initializer=w_start, kernel_regularizer=tk_reg)
model.add(1, kernel_initializer=w_start, kernel_regularizer=tk_reg)
model.set_optimizer(SGD(lr=0.812, momentum=0.8, nesterov=True))
# Batch
start_time = time.time()
model.fit(
X_train,
Y_train,
epochs=800,
def optimize_monk_f(monk="1",
reg=0,
seed=1,
optimizer=None,
max_iter=1000,
verbose=0):
"""Allows you to build a target fusion from a monk dataset,
and then optimize it with a specific "optimizer".
The function is built as follows:
f_monk = MSE_monk + reg*||w||^2
Parameters
----------
monk : String
Allow to specify the monk dataset.
Accepted values:
"1" : monk1
"2" : monk2
"3" : monk3
reg : float
lamda value used in the regualrization term
reg must be in 0 <= reg <= 1
seed : int
Allow to specify a starting point
optimizer : isanet.optimizer
Allow to specify the optimizer used in the process.
Must be passed!
max_iter : int
Define the max iteration in the optimization
process as stop criteria.
verbose : int [0, 1, 2, 3]
Define the verbosity of the process.
"0" : no output
"1" : only iteration output
"2" : iteration and optimizer log at each iteration
"3" : same as above, plus the line search log
Returns
-------
model.history, optimizer.history, (end - start)
It return the model and optimizer history plus the time of the whole process
"""
if monk not in ["1", "2", "3"]:
raise Exception("wrong monk function - accepeted only: '1','2', '3'")
if optimizer is None:
raise Exception("an optimatizer must be specifed")
np.random.seed(seed=seed)
X_train, Y_train = load_monk(monk, "train")
kernel_initializer = {
"1": [0.003, 0.003],
"2": [1 / np.sqrt(17), 1 / np.sqrt(4)],
"3": [0.003, 0.003]
}
model = Mlp()
model.add(4,
input=17,
kernel_initializer=kernel_initializer[monk][0],
kernel_regularizer=reg)
model.add(1,
kernel_initializer=kernel_initializer[monk][1],
kernel_regularizer=reg)
model.set_optimizer(optimizer)
start = time.time()
model.fit(X_train, Y_train, epochs=max_iter, verbose=verbose)
end = time.time()
return model.history, optimizer.history, (end - start)
