output_layer = Dense(2, activation='linear')(flatten)
时间: 2023-12-31 15:03:22 浏览: 28
这行代码定义了模型的输出层,其中 Dense 表示全连接层,2 表示输出层的神经元数量,activation 参数为 linear 表示输出层的激活函数为线性函数,即不使用任何激活函数。这里的输出层神经元数量为 2,说明该模型是一个回归模型,输出一个 2 维的向量。在模型训练时,会将模型的输出与真实值进行比较,计算损失函数并进行反向传播优化。
相关问题
X_scaled, y_scaled = in_and_out(alluse_data) X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_scaled, test_size=0.2, random_state=42) X_test_scaled, y_test_scaled = in_and_out(alluse_data) input_layer = tf.keras.layers.Input(shape=(9,)) reshaped_input = tf.keras.layers.Reshape((9, 1))(input_layer) conv1 = Conv1D(32, 3, activation='relu')(reshaped_input) bi_nlstm = Bidirectional(NLSTM(64, return_sequences=True))(conv1) attention = Attention()([bi_nlstm, bi_nlstm]) flatten = Flatten()(attention) output_layer = Dense(2, activation='linear')(flatten) model = Model(inputs=input_layer, outputs=output_layer) model.compile(optimizer='adam', loss='mse')
这段代码看起来像是一个基于 TensorFlow 的深度学习模型,用于进行回归任务。首先,通过函数 in_and_out() 对所有数据进行标准化处理,然后将数据集分为训练集和测试集。接着,通过 tf.keras.layers 构建了一个深度神经网络模型。模型的输入层为一个 9 维的向量,经过一个 Reshape 层将其转换为 9 行 1 列的矩阵。接着,经过一个卷积层 Conv1D,一个双向的 LSTM 层 Bidirectional(NLSTM),一个 Attention 层,一个 Flatten 层,最后输出一个 2 维的向量。模型的损失函数为 mse,优化器为 adam。
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 如何加入CBAM-ResNet模块,加在哪里
要加入CBAM-ResNet模块,可以参考以下步骤:
1. 导入CBAM模块
```python
from cbam import cbam_block
```
2. 在每个ResNet块中加入CBAM模块,可以在每个ResNet块的末尾添加CBAM块,如下所示:
```python
def resnet_block(input_layer, filters, conv_num=3, activation='relu'):
# ...
shortcut = input_layer
for i in range(conv_num - 1):
x = layers.Conv2D(filters, (3, 3), padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.Activation(activation)(x)
x = layers.Conv2D(filters, (3, 3), padding='same')(x)
x = layers.BatchNormalization()(x)
# 添加CBAM模块
x = cbam_block(x)
output_layer = layers.add([x, shortcut])
output_layer = layers.Activation(activation)(output_layer)
return output_layer
```
3. 修改模型构建部分,加入CBAM-ResNet模块,如下所示:
```python
def CBAM_ResNet(input_shape, num_classes, filters):
inputs = layers.Input(shape=input_shape)
x = layers.Conv2D(filters, (3, 3), padding='same')(inputs)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
# 添加CBAM模块
x = cbam_block(x)
x = resnet_block(x, filters)
x = resnet_block(x, filters)
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(num_classes, activation='softmax')(x)
model = models.Model(inputs, x)
return model
```
这样就可以在CBAM-ResNet模块中加入CBAM模块了。