Image 我已经建立了一个序列图像分类模型，我想知道如何为它绘制混淆矩阵

我已经建立了一个序列图像分类模型，我想知道如何为它绘制混淆矩阵。这是一个二元分类模型-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

TRAIN_PATH = "/Users/shambhavisharma/Documents/CovidDataset/Train"
VAL_PATH = "/Users/shambhavisharma/Documents/CovidDataset/Test"
model = Sequential()
model.add(Conv2D(32,kernel_size=(3,3),activation='relu',input_shape=(224,224,3)))
model.add(Conv2D(64,(3,3),activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Conv2D(64,(3,3),activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Conv2D(128,(3,3),activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(64,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1,activation='sigmoid'))

model.compile(loss=keras.losses.binary_crossentropy,optimizer='adam',metrics=['accuracy'])
train_datagen = image.ImageDataGenerator(
rescale = 1./255,

shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True,
)

test_datagen = image.ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
'/Users/shambhavisharma/Documents/CovidDataset/Train',
target_size = (224,224),
batch_size = 32,
class_mode = 'binary')
validation_generator = test_dataset.flow_from_directory(
'/Users/shambhavisharma/Documents/CovidDataset/Val',
target_size = (224,224),
batch_size = 32,
class_mode = 'binary')
hist = model.fit_generator(
train_generator,
steps_per_epoch=10,
epochs = 30,
validation_data = validation_generator,
validation_steps=2
)