def test_mlp():
mlp.test_mlp(n_epochs=1)
x = T.scalar()
y = T.scalar()
z = x+y
w = z*x
a = T.sqrt(5)
b = T.exp(a)
c = a ** b
d = T.log(c)
num1 = input();
num2 = input();
v = T.dvector()
f2 = function([x, y], x + y)
hello_world_op = printing.Print('aaaa')
printed_vetor = hello_world_op(v)
f = function([v], printed_vetor)
r = f([num1, num2])
m = f2(num1, num2)
import mlp
print ('testendo mlp')
mlp.test_mlp()
# treino 80 teste 20
print m
def test_mlp():
mlp.test_mlp(n_epochs=1)
def test_mlp():
t0=time.time()
mlp.test_mlp(n_epochs=5)
print >> sys.stderr, "test_mlp took %.3fs expected 118s in our buildbot"%(time.time()-t0)
from scipy.io import loadmat as load
import numpy as np
import matplotlib.pyplot as plt
import mlp
from preprocess import Preprocess
(x_train, y_train) = Preprocess(
load('train_32x32.mat')).rgb2gray().standarize().scale().flatten().get()
(x_test, y_test) = Preprocess(
load('test_32x32.mat')).rgb2gray().standarize().scale().flatten().get()
(x_valid, y_valid) = (x_train[math.floor(0.8 * len(x_train)):, :],
y_train[math.floor(0.8 * len(y_train)):, :])
(x_train, y_train) = (x_train[:math.floor(0.8 * len(x_train)), :],
y_train[:math.floor(0.8 * len(y_train)), :])
print(x_train.shape[0], 'train samples')
print(x_valid.shape[0], 'validation samples')
print(x_test.shape[0], 'test samples')
(name, model) = mlp.create_mlp(1024, 0, ('relu', 512, 0))
mlp.train_mlp(model, 50, x_train, y_train, x_valid, y_valid)
mlp.test_mlp(model, x_test, y_test)
w = np.squeeze(model.layers[0].get_weights())
hw = w[0].T
plot_weights(hw, 32)