activation=activation, kernel_initializer=kernel_initializer)) classificador.add(Dropout(dropout)) classificador.add(Dense(units=1, activation='sigmoid')) opt = keras.optimizers.Adam(learning_rate=learning_rate, decay=decay, beta_1=beta_1, beta_2=beta_2) classificador.compile(optimizer=opt, loss=loss, metrics=['binary_accuracy']) classificador.fit(x_train, y_train, batch_size=batch_size, epochs=epochs) qtd_param = classificador.count_params() print('Number of Parameters: ', qtd_param) print('Calculating the ROC curve...') previsoes_rna = classificador.predict(x_valid) prob_rna = previsoes_rna previsoes_rna = (previsoes_rna > 0.5) previsoes_num_rna = [] for i in previsoes_rna: if i: previsoes_num_rna.append(1) else: previsoes_num_rna.append(0) previsoes_rna = np.array(previsoes_num_rna)