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)