如何从 Tensorflow 获取二值图像分类的标签预测

How to get label prediction of binary image classification from Tensorflow

如何从 Tensorflow 获取二值图像分类的标签预测?

环境:

数据集结构:

/training/<br/>
---/COVID19/<br/>
------/img1.jpg<br/>
------/img2.jpg<br/>
------/img3.jpg<br/>
---/NORMAL/<br/>
------/img4.jpg<br/>
------/img5.jpg<br/>
------/img6.jpg<br/>

制作数据集代码:

batch_size = 32
img_height = 300    
img_width = 300
epochs = 10
input_shape = (img_width, img_height, 3)
AUTOTUNE = tf.data.AUTOTUNE


dataset_url = "https://storage.googleapis.com/fdataset/Dataset.tgz"
data_dir = tf.keras.utils.get_file('training', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)

image_count = len(list(data_dir.glob('*/*.jpg')))
print(image_count)

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  seed=123,
  subset="training",
  validation_split=0.8,
  image_size=(img_width, img_height),
  batch_size=batch_size)

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  seed=123,
  subset="validation",
  validation_split=0.2,
  image_size=(img_width, img_height),
  batch_size=batch_size)

train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)

模特:

model = tf.keras.Sequential()
base_model = tf.keras.applications.DenseNet121(input_shape=input_shape,include_top=False)
base_model.trainable=True
model.add(base_model)
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(16,activation='relu'))
model.add(tf.keras.layers.Dense(1, activation="sigmoid"))

损失函数:binary_crossentropy
优化器:RMSprop
指标:accuracy

在我制作模型并对其进行训练后,我使用此代码对验证数据集进行了预测

(model.predict(val_ds) > 0.5).astype("int32")

所以我得到了这样的结果

array([[0],
   [1],
   [1],
   [0],
   [0]], dtype=int32)

然后如何将其再次转换为“COVID19”或“NORMAL”等标签,示例如下:

array([["COVID19"],
   ["NORMAL"],
   ["NORMAL"],
   ["COVID19"],
   ["COVID19"]], dtype=int32)

将所需的值映射到数组中

mapper = {1: "NORMAL", 0: "COVID19"}
np.vectorize(mapper.get)(output)