model.add(rbflayer)
model.add(Dense(len(oDataSet.labelsNames), activation='sigmoid'))
model.compile(loss='categorical_crossentropy', optimizer=_OPTIMIZER)
model.fit(oDataSet.attributes[oData.Training_indexes],
binarizer(oDataSet.labels[oData.Training_indexes]),
batch_size=50,
epochs=epochs,
verbose=1)
y_pred = model.predict(
oDataSet.attributes[oData.Testing_indexes]).argmax(axis=1)
y_true = oDataSet.labels[oData.Testing_indexes]
print(accuracy_score(y_true, y_pred))
print(confusion_matrix(y_true, y_pred))
oData.confusion_matrix = confusion_matrix(y_true, y_pred)
model.save('model.h5')
myArr = None
with open("model.h5", "rb") as binaryfile:
myArr = bytearray(binaryfile.read())
oData.model = myArr, model.history.history['loss']
oData.params = {
"k_fold": K_FOLD,
"GRID_RESULT": grid_result,
"GRID_VALUES_NEURON": GRID_NEURON,
"GRID_VALUES_BETA": GRID_B,
"LEARNING RATE": LEARNING_RATE,
"EPOCHS": epochs
}
oDataSet.append(oData)
print(oData)
for x, y in enumerate(base):
oDataSet.add_sample_of_attribute(
np.array(list(np.float32(y)) + [classes[x]]))
oDataSet.attributes = oDataSet.attributes.astype(float)
oDataSet.normalize_data_set()
for j in range(20):
print(j)
oData = Data(len(oDataSet.labelsNames), 31, samples=50)
oData.random_training_test_by_percent(
np.unique(classes, return_counts=True)[1], 0.8)
perc = Layered_perceptron_Logistic(learning_rate,
len(oDataSet.labelsNames))
perc.train(oDataSet.attributes[oData.Training_indexes],
oDataSet.labels[oData.Training_indexes], epochs)
oData.model = perc
oData.confusion_matrix = np.zeros(
(len(oDataSet.labelsNames), len(oDataSet.labelsNames)))
for i in oData.Testing_indexes:
data = np.matrix(np.hstack(([-1], oDataSet.attributes[i]))).T
predicted = perc.predict(data)
oData.confusion_matrix[int(oDataSet.labels[i]), predicted] += 1
print(oData)
oDataSet.append(oData)
oExp.add_data_set(
oDataSet,
description=" Experimento Dermatologia LP 20 realizaçoes.".format())
oExp.save("Objects/EXP01_4_LP_20.gzip".format())
oExp = Experiment.load("Objects/EXP01_4_LP_20.gzip".format())
print(oExp)
print(oExp.experimentResults[0].sum_confusion_matrix)
dtype=object,
usecols=-1,
delimiter=",")
for x, y in enumerate(base):
oDataSet.add_sample_of_attribute(
np.array(list(np.float32(y)) + [classes[x]]))
oDataSet.attributes = oDataSet.attributes.astype(float)
oDataSet.normalize_data_set()
for j in range(20):
print(j)
oData = Data(2, 31, samples=50)
oData.random_training_test_by_percent([100, 50], 0.8)
perc = Perceptron(learning_rate)
perc.train(oDataSet.attributes[oData.Training_indexes],
oDataSet.labels[oData.Training_indexes], epochs)
oData.model = perc
oData.confusion_matrix = np.zeros((2, 2))
for i in oData.Testing_indexes:
data = np.matrix(np.hstack(([-1], oDataSet.attributes[i]))).T
oData.confusion_matrix[int(oDataSet.labels[i]),
perc.predict(data)] += 1
oDataSet.append(oData)
oExp.add_data_set(
oDataSet, description=" Experimento iris PS 20 realizaçoes.".format())
oExp.save("Objects/EXP01_3_PS_20.gzip".format())
oExp = Experiment.load("Objects/EXP01_3_PS_20.gzip".format())
print(oExp)
print(oExp.experimentResults[0].sum_confusion_matrix)
