def train(self,
data_set: NNData,
epochs=100,
verbosity=2,
order=NNData.Order.RANDOM):
"""Trains the model."""
self._verbose = 0
self._epochs = epochs
self._epoch_counter = 0
rmse_list = []
epoch_list = []
rmse_avg = []
if not data_set.number_of_samples(data_set.Set.TRAIN):
raise FFBPNetwork.EmptySetException
for epoch in range(epochs):
data_set.prime_data(data_set.Set.TRAIN, order)
while not data_set.pool_is_empty(data_set.Set.TRAIN):
feature_labels_pair = data_set.get_one_item(data_set.Set.TRAIN)
label_list = feature_labels_pair[1]
feature_list = feature_labels_pair[0]
expected_list = label_list
actual_list = []
for feature, input_node in zip(feature_list,
self.layers.input_nodes):
input_node.set_input(feature)
for node in self.layers.output_nodes:
actual_list.append(node.node_value)
for label, actual in zip(label_list, self.layers.output_nodes):
actual.set_expected(label)
try:
ret_rmse = FFBPNetwork.rmse_calculator(
actual_list, expected_list)
self._epoch_counter += 1
rmse_list.append(ret_rmse)
print(
f'SAMPLE: {feature_list}, EXPECTED: {label_list}, RMSE VALUE: {ret_rmse}'
)
except IndexError:
print("Zero Division Error Due to Empty Set.")
# report the final RMSE
print(f'**************** FINAL REPORT *****************')
for rmse in range(0, len(rmse_list), 100):
epoch_list.append(rmse)
print(f'EPOCH {rmse} = {rmse_list[rmse]}')
print(f'**************** AVG. RMSE *****************')
avg_mean = np.mean(rmse_list)
rmse_avg.append(avg_mean)
print(f'RMSE: {avg_mean}')
self.verbosity_print(verbosity)
self.train_scores_mean.clear()
self.train_sizes.clear()
self.train_sizes = list(set(epoch_list))[:20]
self.train_scores_mean = rmse_avg[:len(self.train_sizes)]
def test(self, data_set: NNData, order=NNData.Order.SEQUENTIAL):
"""Does the testing of the dataset."""
self._verbose = 0
self._epoch_counter = 0
if not data_set.number_of_samples(data_set.Set.TEST):
raise FFBPNetwork.EmptySetException
data_set.prime_data(data_set.Set.TEST, order)
rmse_counter = 0
total_rmse = 0
rmse_collection = []
while not data_set.pool_is_empty(data_set.Set.TEST):
feature_labels_pair = data_set.get_one_item(data_set.Set.TEST)
label_list = feature_labels_pair[1]
feature_list = feature_labels_pair[0]
expected_list = label_list
actual_list = []
for feature, input_node in zip(feature_list,
self.layers.input_nodes):
input_node.set_input(feature)
for node in self.layers.output_nodes:
actual_list.append(node.node_value)
for label, actual in zip(label_list, self.layers.output_nodes):
actual.set_expected(label)
try:
ret_rmse = FFBPNetwork.rmse_calculator(expected_list,
actual_list)
rmse_collection.append(ret_rmse)
self._verbose += 1
print(f'INPUT: {feature_list}, EXPECTED: {label_list}, '
f'OUTPUT: {actual_list}, RMSE VALUE: {ret_rmse}')
total_rmse += ret_rmse
rmse_counter += 1
except IndexError:
print("Zero Division Error Due to Empty Set.")
print(f'**************** FINAL RMSE REPORT *****************')
RMSE = total_rmse / float(rmse_counter)
print(f'RMSE: {RMSE}')
self.verbosity_print()
self.validation_scores_mean.clear()
self.validation_scores_mean = rmse_collection[:len(self.train_sizes)]
def run_sin():
print(f'Start Timer: {time.perf_counter()}')
network = FFBPNetwork(1, 1)
network.add_hidden_layer(3)
# network.alter_learning_rate(.8)
network.dataset_name = "SIN DATASET"
sin_X = [[0], [0.01], [0.02], [0.03], [0.04], [0.05], [0.06], [0.07], [0.08], [0.09], [0.1], [0.11], [0.12],
[0.13], [0.14], [0.15], [0.16], [0.17], [0.18], [0.19], [0.2], [0.21], [0.22], [0.23], [0.24], [0.25],
[0.26], [0.27], [0.28], [0.29], [0.3], [0.31], [0.32], [0.33], [0.34], [0.35], [0.36], [0.37], [0.38],
[0.39], [0.4], [0.41], [0.42], [0.43], [0.44], [0.45], [0.46], [0.47], [0.48], [0.49], [0.5], [0.51],
[0.52], [0.53], [0.54], [0.55], [0.56], [0.57], [0.58], [0.59], [0.6], [0.61], [0.62], [0.63], [0.64],
[0.65], [0.66], [0.67], [0.68], [0.69], [0.7], [0.71], [0.72], [0.73], [0.74], [0.75], [0.76], [0.77],
[0.78], [0.79], [0.8], [0.81], [0.82], [0.83], [0.84], [0.85], [0.86], [0.87], [0.88], [0.89], [0.9],
[0.91], [0.92], [0.93], [0.94], [0.95], [0.96], [0.97], [0.98], [0.99], [1], [1.01], [1.02], [1.03],
[1.04], [1.05], [1.06], [1.07], [1.08], [1.09], [1.1], [1.11], [1.12], [1.13], [1.14], [1.15], [1.16],
[1.17], [1.18], [1.19], [1.2], [1.21], [1.22], [1.23], [1.24], [1.25], [1.26], [1.27], [1.28], [1.29],
[1.3], [1.31], [1.32], [1.33], [1.34], [1.35], [1.36], [1.37], [1.38], [1.39], [1.4], [1.41], [1.42],
[1.43], [1.44], [1.45], [1.46], [1.47], [1.48], [1.49], [1.5], [1.51], [1.52], [1.53], [1.54], [1.55],
[1.56], [1.57]]
sin_Y = [[0], [0.00999983333416666], [0.0199986666933331], [0.0299955002024957], [0.0399893341866342],
[0.0499791692706783], [0.0599640064794446], [0.0699428473375328], [0.0799146939691727],
[0.089878549198011], [0.0998334166468282], [0.109778300837175], [0.119712207288919],
[0.129634142619695], [0.139543114644236], [0.149438132473599], [0.159318206614246],
[0.169182349066996], [0.179029573425824], [0.188858894976501], [0.198669330795061], [0.2084598998461],
[0.218229623080869], [0.227977523535188], [0.237702626427135], [0.247403959254523],
[0.257080551892155], [0.266731436688831], [0.276355648564114], [0.285952225104836], [0.29552020666134],
[0.305058636443443], [0.314566560616118], [0.324043028394868], [0.333487092140814],
[0.342897807455451], [0.35227423327509], [0.361615431964962], [0.370920469412983], [0.380188415123161],
[0.389418342308651], [0.398609327984423], [0.40776045305957], [0.416870802429211], [0.425939465066],
[0.43496553411123], [0.44394810696552], [0.452886285379068], [0.461779175541483], [0.470625888171158],
[0.479425538604203], [0.488177246882907], [0.496880137843737], [0.505533341204847],
[0.514135991653113], [0.522687228930659], [0.531186197920883], [0.539632048733969],
[0.548023936791874], [0.556361022912784], [0.564642473395035], [0.572867460100481],
[0.581035160537305], [0.58914475794227], [0.597195441362392], [0.60518640573604], [0.613116851973434],
[0.62098598703656], [0.628793024018469], [0.636537182221968], [0.644217687237691], [0.651833771021537],
[0.659384671971473], [0.666869635003698], [0.674287911628145], [0.681638760023334],
[0.688921445110551], [0.696135238627357], [0.70327941920041], [0.710353272417608], [0.717356090899523],
[0.724287174370143], [0.731145829726896], [0.737931371109963], [0.744643119970859],
[0.751280405140293], [0.757842562895277], [0.764328937025505], [0.770738878898969],
[0.777071747526824], [0.783326909627483], [0.78950373968995], [0.795601620036366], [0.801619940883777],
[0.807558100405114], [0.813415504789374], [0.819191568300998], [0.82488571333845], [0.83049737049197],
[0.836025978600521], [0.841470984807897], [0.846831844618015], [0.852108021949363],
[0.857298989188603], [0.862404227243338], [0.867423225594017], [0.872355482344986],
[0.877200504274682], [0.881957806884948], [0.886626914449487], [0.891207360061435],
[0.895698685680048], [0.900100442176505], [0.904412189378826], [0.908633496115883],
[0.912763940260521], [0.916803108771767], [0.920750597736136], [0.92460601240802], [0.928368967249167],
[0.932039085967226], [0.935616001553386], [0.939099356319068], [0.942488801931697],
[0.945783999449539], [0.948984619355586], [0.952090341590516], [0.955100855584692],
[0.958015860289225], [0.960835064206073], [0.963558185417193], [0.966184951612734],
[0.968715100118265], [0.971148377921045], [0.973484541695319], [0.975723357826659],
[0.977864602435316], [0.979908061398614], [0.98185353037236], [0.983700814811277], [0.98544972998846],
[0.98710010101385], [0.98865176285172], [0.990104560337178], [0.991458348191686], [0.992712991037588],
[0.993868363411645], [0.994924349777581], [0.99588084453764], [0.996737752043143], [0.997494986604054],
[0.998152472497548], [0.998710143975583], [0.999167945271476], [0.999525830605479],
[0.999783764189357], [0.999941720229966], [0.999999682931835]]
data = NNData(sin_X, sin_Y, .1)
print("----------------------------------------")
print("Train")
network.train(data, 1000, order=NNData.Order.RANDOM)
print("----------------------------------------")
print("Test")
network.test(data)
network.plot_learning_curve()
print(f'End Timer: {time.perf_counter()}')
return data
def run_iris():
print(f'Start Timer: {time.perf_counter()}')
network = FFBPNetwork(4, 3)
network.add_hidden_layer(4)
network.dataset_name = "IRIS DATASET"
Iris_X = [[5.1, 3.5, 1.4, 0.2], [4.9, 3, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2], [4.6, 3.1, 1.5, 0.2],
[5, 3.6, 1.4, 0.2], [5.4, 3.9, 1.7, 0.4], [4.6, 3.4, 1.4, 0.3], [5, 3.4, 1.5, 0.2],
[4.4, 2.9, 1.4, 0.2], [4.9, 3.1, 1.5, 0.1], [5.4, 3.7, 1.5, 0.2], [4.8, 3.4, 1.6, 0.2],
[4.8, 3, 1.4, 0.1], [4.3, 3, 1.1, 0.1], [5.8, 4, 1.2, 0.2], [5.7, 4.4, 1.5, 0.4],
[5.4, 3.9, 1.3, 0.4], [5.1, 3.5, 1.4, 0.3], [5.7, 3.8, 1.7, 0.3], [5.1, 3.8, 1.5, 0.3],
[5.4, 3.4, 1.7, 0.2], [5.1, 3.7, 1.5, 0.4], [4.6, 3.6, 1, 0.2], [5.1, 3.3, 1.7, 0.5],
[4.8, 3.4, 1.9, 0.2], [5, 3, 1.6, 0.2], [5, 3.4, 1.6, 0.4], [5.2, 3.5, 1.5, 0.2],
[5.2, 3.4, 1.4, 0.2], [4.7, 3.2, 1.6, 0.2], [4.8, 3.1, 1.6, 0.2], [5.4, 3.4, 1.5, 0.4],
[5.2, 4.1, 1.5, 0.1], [5.5, 4.2, 1.4, 0.2], [4.9, 3.1, 1.5, 0.1], [5, 3.2, 1.2, 0.2],
[5.5, 3.5, 1.3, 0.2], [4.9, 3.1, 1.5, 0.1], [4.4, 3, 1.3, 0.2], [5.1, 3.4, 1.5, 0.2],
[5, 3.5, 1.3, 0.3], [4.5, 2.3, 1.3, 0.3], [4.4, 3.2, 1.3, 0.2], [5, 3.5, 1.6, 0.6],
[5.1, 3.8, 1.9, 0.4], [4.8, 3, 1.4, 0.3], [5.1, 3.8, 1.6, 0.2], [4.6, 3.2, 1.4, 0.2],
[5.3, 3.7, 1.5, 0.2], [5, 3.3, 1.4, 0.2], [7, 3.2, 4.7, 1.4], [6.4, 3.2, 4.5, 1.5],
[6.9, 3.1, 4.9, 1.5], [5.5, 2.3, 4, 1.3], [6.5, 2.8, 4.6, 1.5], [5.7, 2.8, 4.5, 1.3],
[6.3, 3.3, 4.7, 1.6], [4.9, 2.4, 3.3, 1], [6.6, 2.9, 4.6, 1.3], [5.2, 2.7, 3.9, 1.4], [5, 2, 3.5, 1],
[5.9, 3, 4.2, 1.5], [6, 2.2, 4, 1], [6.1, 2.9, 4.7, 1.4], [5.6, 2.9, 3.6, 1.3], [6.7, 3.1, 4.4, 1.4],
[5.6, 3, 4.5, 1.5], [5.8, 2.7, 4.1, 1], [6.2, 2.2, 4.5, 1.5], [5.6, 2.5, 3.9, 1.1],
[5.9, 3.2, 4.8, 1.8], [6.1, 2.8, 4, 1.3], [6.3, 2.5, 4.9, 1.5], [6.1, 2.8, 4.7, 1.2],
[6.4, 2.9, 4.3, 1.3], [6.6, 3, 4.4, 1.4], [6.8, 2.8, 4.8, 1.4], [6.7, 3, 5, 1.7], [6, 2.9, 4.5, 1.5],
[5.7, 2.6, 3.5, 1], [5.5, 2.4, 3.8, 1.1], [5.5, 2.4, 3.7, 1], [5.8, 2.7, 3.9, 1.2],
[6, 2.7, 5.1, 1.6], [5.4, 3, 4.5, 1.5], [6, 3.4, 4.5, 1.6], [6.7, 3.1, 4.7, 1.5],
[6.3, 2.3, 4.4, 1.3], [5.6, 3, 4.1, 1.3], [5.5, 2.5, 4, 1.3], [5.5, 2.6, 4.4, 1.2],
[6.1, 3, 4.6, 1.4], [5.8, 2.6, 4, 1.2], [5, 2.3, 3.3, 1], [5.6, 2.7, 4.2, 1.3], [5.7, 3, 4.2, 1.2],
[5.7, 2.9, 4.2, 1.3], [6.2, 2.9, 4.3, 1.3], [5.1, 2.5, 3, 1.1], [5.7, 2.8, 4.1, 1.3],
[6.3, 3.3, 6, 2.5], [5.8, 2.7, 5.1, 1.9], [7.1, 3, 5.9, 2.1], [6.3, 2.9, 5.6, 1.8],
[6.5, 3, 5.8, 2.2], [7.6, 3, 6.6, 2.1], [4.9, 2.5, 4.5, 1.7], [7.3, 2.9, 6.3, 1.8],
[6.7, 2.5, 5.8, 1.8], [7.2, 3.6, 6.1, 2.5], [6.5, 3.2, 5.1, 2], [6.4, 2.7, 5.3, 1.9],
[6.8, 3, 5.5, 2.1], [5.7, 2.5, 5, 2], [5.8, 2.8, 5.1, 2.4], [6.4, 3.2, 5.3, 2.3], [6.5, 3, 5.5, 1.8],
[7.7, 3.8, 6.7, 2.2], [7.7, 2.6, 6.9, 2.3], [6, 2.2, 5, 1.5], [6.9, 3.2, 5.7, 2.3],
[5.6, 2.8, 4.9, 2], [7.7, 2.8, 6.7, 2], [6.3, 2.7, 4.9, 1.8], [6.7, 3.3, 5.7, 2.1],
[7.2, 3.2, 6, 1.8], [6.2, 2.8, 4.8, 1.8], [6.1, 3, 4.9, 1.8], [6.4, 2.8, 5.6, 2.1],
[7.2, 3, 5.8, 1.6], [7.4, 2.8, 6.1, 1.9], [7.9, 3.8, 6.4, 2], [6.4, 2.8, 5.6, 2.2],
[6.3, 2.8, 5.1, 1.5], [6.1, 2.6, 5.6, 1.4], [7.7, 3, 6.1, 2.3], [6.3, 3.4, 5.6, 2.4],
[6.4, 3.1, 5.5, 1.8], [6, 3, 4.8, 1.8], [6.9, 3.1, 5.4, 2.1], [6.7, 3.1, 5.6, 2.4],
[6.9, 3.1, 5.1, 2.3], [5.8, 2.7, 5.1, 1.9], [6.8, 3.2, 5.9, 2.3], [6.7, 3.3, 5.7, 2.5],
[6.7, 3, 5.2, 2.3], [6.3, 2.5, 5, 1.9], [6.5, 3, 5.2, 2], [6.2, 3.4, 5.4, 2.3], [5.9, 3, 5.1, 1.8]]
Iris_Y = [[1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ],
[1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ],
[1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ],
[1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ],
[1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ],
[1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ],
[1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ], [1, 0, 0, ],
[1, 0, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ],
[0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ],
[0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ],
[0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ],
[0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ],
[0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ],
[0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ], [0, 1, 0, ],
[0, 1, 0, ], [0, 1, 0, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ],
[0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ],
[0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ],
[0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ],
[0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ],
[0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ],
[0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ],
[0, 0, 1, ], [0, 0, 1, ], [0, 0, 1, ]]
data = NNData(Iris_X, Iris_Y, .7)
print("----------------------------------------")
print("Train")
# network.alter_learning_rate(.8)
network.train(data, 100, order=NNData.Order.RANDOM)
print("----------------------------------------")
print("Test")
network.test(data, order=NNData.Order.SEQUENTIAL)
network.plot_learning_curve()
print(f'End Timer: {time.perf_counter()}')
return data