小数小数点表示的精度，查全率，精度和f测度

我正在进行CNN实验，并使用准确性，召回率，准确性和f量度指标评估构建的分类器。

结果值仅是两位小数（即0.95）。我如何至少有4个小数位数（即0.9532）

此外，如何将结果值四舍五入以具有四舍五入的数字的下限或上限？

from keras.models import Sequential
from keras.layers import Conv2D,Activation,MaxPooling2D,Dense,Flatten,Dropout
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from IPython.display import display
import matplotlib.pyplot as plt
from PIL import Image
from sklearn.metrics import classification_report, confusion_matrix
import keras
from keras.layers import BatchNormalization
from keras.optimizers import Adam
import pickle
from keras.models import load_model
classifier = load_model('32_With_Dropout_rl_001_1_layer_2.h5')
classifier1 = load_model('32_With_Dropout_rl_001_2_layers_2.h5')
classifier2 = load_model('32_With_Dropout_rl_001_3_layers_2.h5')
test_datagen = ImageDataGenerator(rescale = 1./255)
batchsize=10
test_set = test_datagen.flow_from_directory('/home/osboxes/Downloads/Downloads/Journal_Paper/Benign_Malicious/Spectrogram/Test/',
                                           target_size = (200,200),
                                           batch_size = batchsize,
                       shuffle=False,
                                           class_mode ='categorical')
Y_pred = classifier.predict_generator(test_set, steps= 1023 // batchsize+1)
y_pred = np.argmax(Y_pred, axis=1)
print('Confusion Matrix 1 layer')
print(confusion_matrix(test_set.classes, y_pred))
print('Classification Report')
target_names = test_set.classes
class_labels = list(test_set.class_indices.keys()) 
target_names = ['Bening', 'Malicious'] 
report = classification_report(test_set.classes, y_pred, target_names=class_labels)
print(report) 

Y_pred = classifier1.predict_generator(test_set, steps= 1023 // batchsize+1)
y_pred = np.argmax(Y_pred, axis=1)
print('Confusion Matrix 2 layers')
print(confusion_matrix(test_set.classes, y_pred))
print('Classification Report')
target_names = test_set.classes
class_labels = list(test_set.class_indices.keys()) 
target_names = ['Bening', 'Malicious'] 
report = classification_report(test_set.classes, y_pred, target_names=class_labels)
print(report) 

Y_pred = classifier2.predict_generator(test_set, steps= 1023 // batchsize+1)
y_pred = np.argmax(Y_pred, axis=1)
print('Confusion Matrix 3 layers')
print(confusion_matrix(test_set.classes, y_pred))
print('Classification Report')
target_names = test_set.classes
class_labels = list(test_set.class_indices.keys()) 
target_names = ['Bening', 'Malicious'] 
report = classification_report(test_set.classes, y_pred, target_names=class_labels)
print(report) 


#f = open('32_With_Dropout_rl_001_1_layer', 'rb')
#history = pickle.load(f)

#f = open('32_With_Dropout_rl_001_2_layers', 'rb')
#history1 = pickle.load(f)


#f = open('32_With_Dropout_rl_001_3_layers', 'rb')
#history2 = pickle.load(f)

问题来源：stackoverflow