def test_process(self):
iris_mat_train, iris_label_train = dataset.load_iris("sample_data", "training", one_hot=True)
iris_mat_test, iris_label_test = dataset.load_iris("sample_data", "testing", one_hot=True)
fnn = FNN(iris_mat_train, iris_label_train, [2])
fnn.fit(lr = 0.001, epoch = 4000, err_th = 0.00001, batch_size = 30)
error_rate = autotest.eval_predict(fnn, iris_mat_test, iris_label_test, self.logging, one_hot=True)
self.tlog("iris predict (with fnn) error rate :" + str(error_rate))
def test_process(self):
train_mat = [\
[0.12, 0.25],\
[3.24, 4.33],\
[0.14, 0.45],\
[7.30, 4.23],\
]
train_label = [[0,1],[1,0],[0,1],[1,0]] # out bit is 1
fnn = FNN(train_mat, train_label, [3])
fnn.fit(lr = 0.01, epoch = 2000, err_th = 0.001, batch_size = 4)
r1 = autotest.eval_predict_one(fnn,[0.10,0.33],[0, 1],self.logging, one_hot=True)
r2 = autotest.eval_predict_one(fnn,[4.40,4.37],[1, 0],self.logging, one_hot=True)
def test_process(self):
dg_mat_train, dg_label_train = dataset.load_mnist("sample_data",
"training",
one_hot=True)
dg_mat_test, dg_label_test = dataset.load_mnist("sample_data",
"testing",
one_hot=True)
fnn = FNN(dg_mat_train, dg_label_train, [400, 100])
fnn.fit(lr=0.01, epoch=1000, err_th=0.00001, batch_size=100)
error_rate = autotest.eval_predict(fnn,
dg_mat_test,
dg_label_test,
self.logging,
one_hot=True)
self.tlog("digit predict (with fnn) error rate :" + str(error_rate))
def test_process(self):
iris_mat_train, iris_label_train = dataset.load_iris("sample_data",
"training",
one_hot=True)
iris_mat_test, iris_label_test = dataset.load_iris("sample_data",
"testing",
one_hot=True)
fnn = FNN(iris_mat_train, iris_label_train, [2])
fnn.fit(lr=0.001, epoch=4000, err_th=0.00001, batch_size=30)
error_rate = autotest.eval_predict(fnn,
iris_mat_test,
iris_label_test,
self.logging,
one_hot=True)
self.tlog("iris predict (with fnn) error rate :" + str(error_rate))
def test_process(self):
train_mat = [\
[0.12, 0.25],\
[3.24, 4.33],\
[0.14, 0.45],\
[7.30, 4.23],\
]
train_label = [[0, 1], [1, 0], [0, 1], [1, 0]] # out bit is 1
fnn = FNN(train_mat, train_label, [3])
fnn.fit(lr=0.01, epoch=2000, err_th=0.001, batch_size=4)
r1 = autotest.eval_predict_one(fnn, [0.10, 0.33], [0, 1],
self.logging,
one_hot=True)
r2 = autotest.eval_predict_one(fnn, [4.40, 4.37], [1, 0],
self.logging,
one_hot=True)
