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)