layers.globalaveragepooling2d
时间: 2023-05-02 22:04:15 浏览: 75
b'layers.globalaveragepooling2d' 是 Keras 深度学习库中的一个函数,用于计算二维图像张量在通道维度上的全局平均池化,将每个通道上所有的值相加并取平均值作为该通道的输出。这可以帮助减少模型参数数量并防止过拟合。
相关问题
def conv_block(inputs, filters): x = layers.BatchNormalization()(inputs) x = layers.Activation('relu')(x) x = layers.Conv2D(filters, 1, padding='same')(x) x = layers.BatchNormalization()(x) x = layers.Activation('relu')(x) x = layers.Conv2D(filters, 3, padding='same')(x) x = layers.Conv2D(filters, 1, padding='same')(x) return x def dense_block(inputs, filters, n_layers): x = inputs for i in range(n_layers): conv = conv_block(x, filters) x = layers.Concatenate()([x, conv]) return x def transition_block(inputs, compression): filters = int(inputs.shape[-1] * compression) x = layers.BatchNormalization()(inputs) x = layers.Activation('relu')(x) x = layers.Conv2D(filters, 1, padding='same')(x) x = layers.AveragePooling2D(2)(x) return x def Inception_block(inputs, filters): x1 = layers.Conv2D(filters, 1, padding='same', activation='relu')(inputs) x2 = layers.Conv2D(filters, 1, padding='same', activation='relu')(inputs) x2 = layers.Conv2D(filters, 3, padding='same', activation='relu')(x2) x3 = layers.Conv2D(filters, 1, padding='same', activation='relu')(inputs) x3 = layers.Conv2D(filters, 5, padding='same', activation='relu')(x3) x4 = layers.MaxPooling2D(3, strides=1, padding='same')(inputs) x4 = layers.Conv2D(filters, 1, padding='same', activation='relu')(x4) x = layers.Concatenate()([x1, x2, x3, x4]) return x inputs = keras.Input(shape=(224, 224, 3)) x = layers.Conv2D(64, 7, strides=2, padding='same')(inputs) x = layers.BatchNormalization()(x) x = layers.Activation('relu')(x) x = layers.MaxPooling2D(3, strides=2, padding='same')(x) x = dense_block(x, 32, 6) x = transition_block(x, 0.5) x = Inception_block(x, 64) x = dense_block(x, 32, 12) x = transition_block(x, 0.5) x = Inception_block(x, 128) x = dense_block(x, 32, 48) x = transition_block(x, 0.5) x = Inception_block(x, 256) x = layers.GlobalAveragePooling2D()(x) outputs = layers.Dense(10, activation='softmax')(x) model = keras.Model(inputs, outputs)这串代码有问题
这段代码并没有明显的语法错误,但是其中使用的函数和模块并未导入。需要在代码文件开头导入所需的模块和函数,例如:
```
from tensorflow import keras
from tensorflow.keras import layers
def conv_block(inputs, filters):
x = layers.BatchNormalization()(inputs)
x = layers.Activation('relu')(x)
x = layers.Conv2D(filters, 1, padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(filters, 3, padding='same')(x)
x = layers.Conv2D(filters, 1, padding='same')(x)
return x
def dense_block(inputs, filters, n_layers):
x = inputs
for i in range(n_layers):
conv = conv_block(x, filters)
x = layers.Concatenate()([x, conv])
return x
def transition_block(inputs, compression):
filters = int(inputs.shape[-1] * compression)
x = layers.BatchNormalization()(inputs)
x = layers.Activation('relu')(x)
x = layers.Conv2D(filters, 1, padding='same')(x)
x = layers.AveragePooling2D(2)(x)
return x
def Inception_block(inputs, filters):
x1 = layers.Conv2D(filters, 1, padding='same', activation='relu')(inputs)
x2 = layers.Conv2D(filters, 1, padding='same', activation='relu')(inputs)
x2 = layers.Conv2D(filters, 3, padding='same', activation='relu')(x2)
x3 = layers.Conv2D(filters, 1, padding='same', activation='relu')(inputs)
x3 = layers.Conv2D(filters, 5, padding='same', activation='relu')(x3)
x4 = layers.MaxPooling2D(3, strides=1, padding='same')(inputs)
x4 = layers.Conv2D(filters, 1, padding='same', activation='relu')(x4)
x = layers.Concatenate()([x1, x2, x3, x4])
return x
inputs = keras.Input(shape=(224, 224, 3))
x = layers.Conv2D(64, 7, strides=2, padding='same')(inputs)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
x = layers.MaxPooling2D(3, strides=2, padding='same')(x)
x = dense_block(x, 32, 6)
x = transition_block(x, 0.5)
x = Inception_block(x, 64)
x = dense_block(x, 32, 12)
x = transition_block(x, 0.5)
x = Inception_block(x, 128)
x = dense_block(x, 32, 48)
x = transition_block(x, 0.5)
x = Inception_block(x, 256)
x = layers.GlobalAveragePooling2D()(x)
outputs = layers.Dense(10, activation='softmax')(x)
model = keras.Model(inputs, outputs)
```
def model_load(IMG_SHAPE=(224, 224, 3), class_num=12): # 加载预训练的mobilenet模型 base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE, include_top=False, weights='imagenet') # 将模型的主干参数进行冻结 base_model.trainable = False model = tf.keras.models.Sequential([ # 进行归一化的处理 tf.keras.layers.experimental.preprocessing.Rescaling(1. / 127.5, offset=-1, input_shape=IMG_SHAPE), # 设置主干模型 base_model, # 对主干模型的输出进行全局平均池化 tf.keras.layers.GlobalAveragePooling2D(), # 通过全连接层映射到最后的分类数目上 tf.keras.layers.Dense(class_num, activation='softmax') ]) model.summary() # 模型训练的优化器为adam优化器,模型的损失函数为交叉熵损失函数 model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) return model
这段代码定义了一个函数`model_load`,用于加载预训练的MobileNetV2模型,并根据给定的图像形状和类别数创建一个新的模型。
首先,代码使用`tf.keras.applications.MobileNetV2`加载了一个预训练的MobileNetV2模型,其中`input_shape`参数指定了输入图像的形状,`include_top=False`表示不包含顶部的全连接层,`weights='imagenet'`表示使用在ImageNet数据集上预训练的权重。
接下来,通过设置`base_model.trainable = False`将模型的主干参数冻结,即不参与训练过程。
然后,使用`tf.keras.models.Sequential`创建一个序列模型,并按顺序添加各个层:
1. `tf.keras.layers.experimental.preprocessing.Rescaling`层用于对输入图像进行归一化处理。
2. 将预训练的MobileNetV2模型作为主干模型添加到序列模型中。
3. `tf.keras.layers.GlobalAveragePooling2D`层用于对主干模型的输出进行全局平均池化操作。
4. `tf.keras.layers.Dense`层通过全连接层将特征映射到最后的类别数目上,并使用softmax激活函数进行分类。
随后,通过调用`model.summary()`方法来打印模型的摘要信息。
最后,使用`model.compile()`方法配置模型的优化器为Adam优化器,损失函数为交叉熵损失函数,评估指标为准确率。
函数最后返回创建的模型。
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