What is the catagories of the UDP port?
时间: 2024-05-24 22:10:21 浏览: 14
The categories of UDP ports are:
1. Well-known ports (0-1023): These ports are reserved for services that are widely used and standardized, such as HTTP (80), FTP (21), and DNS (53).
2. Registered ports (1024-49151): These ports are reserved for applications that have been registered with the Internet Assigned Numbers Authority (IANA).
3. Dynamic or private ports (49152-65535): These ports are used by applications for temporary or private purposes and are not registered with IANA.
相关问题
from keras import applications from keras.preprocessing.image import ImageDataGenerator from keras import optimizers from keras.models import Sequential, Model from keras.layers import Dropout, Flatten, Dense img_width, img_height = 256, 256 batch_size = 16 epochs = 50 train_data_dir = 'C:/Users/Z-/Desktop/kaggle/train' validation_data_dir = 'C:/Users/Z-/Desktop/kaggle/test1' OUT_CATAGORIES = 1 nb_train_samples = 2000 nb_validation_samples = 100 base_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=(img_width, img_height, 3)) base_model.summary() for layer in base_model.layers[:15]: layer.trainable = False top_model = Sequential() top_model.add(Flatten(input_shape=base_model.output_shape[1:])) top_model.add(Dense(256, activation='relu')) top_model.add(Dropout(0.5)) top_model.add(Dense(OUT_CATAGORIES, activation='sigmoid')) model = Model(inputs=base_model.input, outputs=top_model(base_model.output)) model.compile(loss='binary_crossentropy', optimizer=optimizers.SGD(learning_rate=0.0001, momentum=0.9), metrics=['accuracy']) train_datagen = ImageDataGenerator(rescale=1. / 255, horizontal_flip=True) test_datagen = ImageDataGenerator(rescale=1. / 255) train_generator = train_datagen.flow_from_directory( train_data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='binary') validation_generator = test_datagen.flow_from_directory( validation_data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='binary', shuffle=False ) model.fit_generator( train_generator, steps_per_epoch=nb_train_samples / batch_size, epochs=epochs, validation_data=validation_generator, validation_steps=nb_validation_samples / batch_size, verbose=2, workers=12 ) score = model.evaluate_generator(validation_generator, nb_validation_samples / batch_size) scores = model.predict_generator(validation_generator, nb_validation_samples / batch_size)看看这段代码有什么错误
在段代码中,有一些错误和需要注意的地方:
1. `img_width` 和 `img_height` 变量没有被定义,需要先定义它们的值。
2. `OUT_CATAGORIES` 变量也没有被定义,需要给它赋值。
3. 在使用 `train_generator` 和 `validation_generator` 时,`steps_per_epoch` 和 `validation_steps` 应该为整数值,而不是浮点数。可以使用 `//` 运算符来进行整除操作。
4. 在评估模型的性能时,`evaluate_generator` 和 `predict_generator` 方法的参数应该是整数值,而不是浮点数。可以使用 `int()` 函数来将浮点数转换为整数。
下面是修改后的代码:
```python
from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential, Model
from keras.layers import Dropout, Flatten, Dense
img_width, img_height = 256, 256
batch_size = 16
epochs = 50
train_data_dir = 'C:/Users/Z-/Desktop/kaggle/train'
validation_data_dir = 'C:/Users/Z-/Desktop/kaggle/test1'
OUT_CATAGORIES = 1
nb_train_samples = 2000
nb_validation_samples = 100
base_model = applications.VGG16(weights='imagenet',
include_top=False,
input_shape=(img_width, img_height, 3))
base_model.summary()
for layer in base_model.layers[:15]:
layer.trainable = False
top_model = Sequential()
top_model.add(Flatten(input_shape=base_model.output_shape[1:]))
top_model.add(Dense(256, activation='relu'))
top_model.add(Dropout(0.5))
top_model.add(Dense(OUT_CATAGORIES, activation='sigmoid'))
model = Model(inputs=base_model.input,
outputs=top_model(base_model.output))
model.compile(loss='binary_crossentropy',
optimizer=optimizers.SGD(learning_rate=0.0001, momentum=0.9),
metrics=['accuracy'])
train_datagen = ImageDataGenerator(rescale=1. / 255,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='binary',
shuffle=False)
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size,
verbose=2,
workers=12)
score = model.evaluate_generator(validation_generator, int(nb_validation_samples / batch_size))
scores = model.predict_generator(validation_generator, int(nb_validation_samples / batch_size))
```
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