resnet一维数据代码
时间: 2023-10-05 15:09:43 浏览: 78
对于一维数据,可以使用一维卷积神经网络(1D Convolutional Neural Network)来构建ResNet。以下是一个示例代码:
```python
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv1D, BatchNormalization, Activation, Add
def resnet_block(inputs, filters, kernel_size, strides):
x = Conv1D(filters=filters, kernel_size=kernel_size, strides=strides, padding='same')(inputs)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv1D(filters=filters, kernel_size=kernel_size, strides=1, padding='same')(x)
x = BatchNormalization()(x)
x = Add()([x, inputs])
x = Activation('relu')(x)
return x
def build_resnet(input_shape):
inputs = Input(shape=input_shape)
x = Conv1D(filters=64, kernel_size=7, strides=2, padding='same')(inputs)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = resnet_block(x, filters=64, kernel_size=3, strides=2)
x = resnet_block(x, filters=64, kernel_size=3, strides=1)
x = resnet_block(x, filters=64, kernel_size=3, strides=1)
x = tf.keras.layers.GlobalAveragePooling1D()(x)
outputs = tf.keras.layers.Dense(units=num_classes, activation='softmax')(x)
model = Model(inputs=inputs, outputs=outputs)
return model
# 定义输入维度和类别数量
input_shape = (100, 1) # 输入数据的形状,例如 (时间步长, 特征数)
num_classes = 10 # 类别数量
# 构建模型
model = build_resnet(input_shape)
model.summary()
```
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