Conv2D(32, (3, 2), padding='same', activation='relu', input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3)),
时间: 2023-10-22 08:05:42 浏览: 22
这是一个 Keras 中的 Conv2D 层,它具有以下参数:
- 32:表示卷积核的数量,也就是输出的深度。
- (3, 2):表示卷积核的大小,这里是一个 3x2 的矩阵。
- padding='same':表示使用边界填充,保持输出图像与输入图像的大小相同。
- activation='relu':表示使用 ReLU 激活函数。
- input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3):表示输入的图像大小为 IMAGE_SIZE x IMAGE_SIZE,通道数为 3。这是第一层,因此需要指定输入的形状。
相关问题
def MEAN_Spot(opt): # channel 1 inputs1 = layers.Input(shape=(42,42,1)) conv1 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs1) bn1 = layers.BatchNormalization()(conv1) pool1 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn1) do1 = layers.Dropout(0.3)(pool1) # channel 2 inputs2 = layers.Input(shape=(42,42,1)) conv2 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs2) bn2 = layers.BatchNormalization()(conv2) pool2 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn2) do2 = layers.Dropout(0.3)(pool2) # channel 3 inputs3 = layers.Input(shape=(42,42,1)) conv3 = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs3) bn3 = layers.BatchNormalization()(conv3) pool3 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn3) do3 = layers.Dropout(0.3)(pool3) # merge 1 merged = layers.Concatenate()([do1, do2, do3]) # interpretation 1 merged_conv = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(merged) merged_pool = layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2,2))(merged_conv) flat = layers.Flatten()(merged_pool) flat_do = layers.Dropout(0.2)(flat) # outputs outputs = layers.Dense(1, activation='linear', name='spot')(flat_do) #Takes input u, v, os model = keras.models.Model(inputs=[inputs1, inputs2, inputs3], outputs=[outputs]) model.compile( loss={'spot':'mse'}, optimizer=opt, metrics={'spot':tf.keras.metrics.MeanAbsoluteError()}, ) return model 更改模型加入CBAM模块
可以尝试在每个卷积层后加入CBAM模块,CBAM模块可以用来增强模型的注意力机制。
以下是修改后的代码:
```python
from keras.layers import GlobalAveragePooling2D, Reshape, Dense, multiply, Permute
def channel_attention(input_feature, ratio=8):
channel_axis = 1 if K.image_data_format() == "channels_first" else -1
channel = input_feature._keras_shape[channel_axis]
shared_layer_one = Dense(channel//ratio,
activation='relu',
kernel_initializer='he_normal',
use_bias=True,
bias_initializer='zeros')
shared_layer_two = Dense(channel,
kernel_initializer='he_normal',
use_bias=True,
bias_initializer='zeros')
avg_pool = GlobalAveragePooling2D()(input_feature)
avg_pool = Reshape((1,1,channel))(avg_pool)
assert avg_pool._keras_shape[1:] == (1,1,channel)
avg_pool = shared_layer_one(avg_pool)
assert avg_pool._keras_shape[1:] == (1,1,channel//ratio)
avg_pool = shared_layer_two(avg_pool)
assert avg_pool._keras_shape[1:] == (1,1,channel)
max_pool = GlobalMaxPooling2D()(input_feature)
max_pool = Reshape((1,1,channel))(max_pool)
assert max_pool._keras_shape[1:] == (1,1,channel)
max_pool = shared_layer_one(max_pool)
assert max_pool._keras_shape[1:] == (1,1,channel//ratio)
max_pool = shared_layer_two(max_pool)
assert max_pool._keras_shape[1:] == (1,1,channel)
cbam_feature = Add()([avg_pool,max_pool])
cbam_feature = Activation('sigmoid')(cbam_feature)
if K.image_data_format() == "channels_first":
cbam_feature = Permute((3, 1, 2))(cbam_feature)
return multiply([input_feature, cbam_feature])
def spatial_attention(input_feature):
kernel_size = 7
if K.image_data_format() == "channels_first":
channel = input_feature._keras_shape[1]
cbam_feature = Permute((2,3,1))(input_feature)
else:
channel = input_feature._keras_shape[-1]
cbam_feature = input_feature
avg_pool = Lambda(lambda x: K.mean(x, axis=3, keepdims=True))(cbam_feature)
assert avg_pool._keras_shape[-1] == 1
max_pool = Lambda(lambda x: K.max(x, axis=3, keepdims=True))(cbam_feature)
assert max_pool._keras_shape[-1] == 1
concat = Concatenate(axis=3)([avg_pool, max_pool])
assert concat._keras_shape[-1] == 2
cbam_feature = Conv2D(filters = 1,
kernel_size=kernel_size,
strides=1,
padding='same',
activation='sigmoid',
kernel_initializer='he_normal',
use_bias=False)(concat)
assert cbam_feature._keras_shape[-1] == 1
if K.image_data_format() == "channels_first":
cbam_feature = Permute((3, 1, 2))(cbam_feature)
return multiply([input_feature, cbam_feature])
def CBAM(input_feature):
cbam_feature = channel_attention(input_feature)
cbam_feature = spatial_attention(cbam_feature)
return cbam_feature
def MEAN_Spot_CBAM(opt): # channel 1
inputs1 = layers.Input(shape=(42,42,1))
conv1 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs1)
bn1 = layers.BatchNormalization()(conv1)
cbam1 = CBAM(bn1)
pool1 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(cbam1)
do1 = layers.Dropout(0.3)(pool1)
# channel 2
inputs2 = layers.Input(shape=(42,42,1))
conv2 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs2)
bn2 = layers.BatchNormalization()(conv2)
cbam2 = CBAM(bn2)
pool2 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(cbam2)
do2 = layers.Dropout(0.3)(pool2)
# channel 3
inputs3 = layers.Input(shape=(42,42,1))
conv3 = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs3)
bn3 = layers.BatchNormalization()(conv3)
cbam3 = CBAM(bn3)
pool3 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(cbam3)
do3 = layers.Dropout(0.3)(pool3)
# merge 1
merged = layers.Concatenate()([do1, do2, do3])
# interpretation 1
merged_conv = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(merged)
merged_cbam = CBAM(merged_conv)
merged_pool = layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2,2))(merged_cbam)
flat = layers.Flatten()(merged_pool)
flat_do = layers.Dropout(0.2)(flat)
# outputs
outputs = layers.Dense(1, activation='linear', name='spot')(flat_do)
#Takes input u, v, os
model = keras.models.Model(inputs=[inputs1, inputs2, inputs3], outputs=[outputs])
model.compile(
loss={'spot':'mse'},
optimizer=opt,
metrics={'spot':tf.keras.metrics.MeanAbsoluteError()},
)
return model
```
import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator # 设置训练集和验证集的路径 train_dir = 'path/to/train/directory' validation_dir = 'path/to/validation/directory' # 定义数据生成器 train_datagen = ImageDataGenerator(rescale=1./255) validation_datagen = ImageDataGenerator(rescale=1./255) train_generator = train_datagen.flow_from_directory( train_dir, target_size=(150, 150), batch_size=32, class_mode='categorical') validation_generator = validation_datagen.flow_from_directory( validation_dir, target_size=(150, 150), batch_size=32, class_mode='categorical') # 构建卷积神经网络模型 model = tf.keras.models.Sequential([ tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(150, 150, 3)), tf.keras.layers.MaxPooling2D(2, 2), tf.keras.layers.Conv2D(64, (3,3), activation='relu'), tf.keras.layers.MaxPooling2D(2,2), tf.keras.layers.Conv2D(128, (3,3), activation='relu'), tf.keras.layers.MaxPooling2D(2,2), tf.keras.layers.Conv2D(128, (3,3), activation='relu'), tf.keras.layers.MaxPooling2D(2,2), tf.keras.layers.Flatten(), tf.keras.layers.Dense(512, activation='relu'), tf.keras.layers.Dense(5, activation='softmax') ]) # 编译模型 model.compile(loss='categorical_crossentropy', optimizer=tf.keras.optimizers.RMSprop(lr=1e-4), metrics=['acc']) # 训练模型 history = model.fit( train_generator, steps_per_epoch=train_generator.samples/train_generator.batch_size, epochs=30, validation_data=validation_generator, validation_steps=validation_generator.samples/validation_generator.batch_size, verbose=2) # 保存模型 model.save('flower_classification.h5')给这个代码添加SeNet
好的,我来为你添加SeNet。
首先,导入必要的库:
```python
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model
```
然后,定义数据生成器:
```python
train_datagen = ImageDataGenerator(rescale=1./255)
validation_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(150, 150),
batch_size=32,
class_mode='categorical')
validation_generator = validation_datagen.flow_from_directory(
validation_dir,
target_size=(150, 150),
batch_size=32,
class_mode='categorical')
```
接下来,我们定义SeNet模型:
```python
def SENet(input_shape, classes):
input_tensor = Input(shape=input_shape)
x = Conv2D(64, (3,3), padding='same', activation='relu')(input_tensor)
x = Conv2D(64, (3,3), padding='same', activation='relu')(x)
x = Conv2D(128, (3,3), padding='same', activation='relu')(x)
x = MaxPooling2D(pool_size=(2,2))(x)
x = Conv2D(256, (3,3), padding='same', activation='relu')(x)
x = Conv2D(256, (3,3), padding='same', activation='relu')(x)
x = Conv2D(512, (3,3), padding='same', activation='relu')(x)
x = GlobalAveragePooling2D()(x)
x = Dense(512, activation='relu')(x)
x = Dense(classes, activation='softmax')(x)
model = Model(inputs=input_tensor, outputs=x)
return model
```
然后,我们为SeNet模型编译优化器和损失函数:
```python
model = SENet(input_shape=(150,150,3), classes=5)
model.compile(loss='categorical_crossentropy',
optimizer=tf.keras.optimizers.RMSprop(lr=1e-4),
metrics=['acc'])
```
最后,我们训练模型,保存模型:
```python
history = model.fit(train_generator,
steps_per_epoch=train_generator.samples/train_generator.batch_size,
epochs=30,
validation_data=validation_generator,
validation_steps=validation_generator.samples/validation_generator.batch_size,
verbose=2)
model.save('flower_classification_with_se.h5')
```
这样,我们就成功地为这个代码添加了SeNet。
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