一维efficientnetV2代码

一维 EfficientNetV2 的代码可以参考以下示例：

import tensorflow as tf
from tensorflow.keras import layers

def round_filters(filters, multiplier):
    """计算通道数的倍数"""
    divisor = 8
    new_filters = int(filters * multiplier + divisor / 2) // divisor * divisor
    new_filters = max(divisor, new_filters)
    if new_filters < 0.9 * filters:
        new_filters += divisor
    return int(new_filters)

def round_repeats(repeats, multiplier):
    """计算重复数的倍数"""
    if not multiplier:
        return repeats
    return int(math.ceil(multiplier * repeats))

def SEBlock(input_filters, se_ratio):
    """Squeeze-and-Excitation 模块"""
    num_reduced_filters = max(1, int(input_filters * se_ratio))
    filters = input_filters

    se = layers.GlobalAveragePooling1D()(input)
    se = layers.Reshape((1, filters))(se)
    se = layers.Conv1D(num_reduced_filters, 1, activation='relu', padding='same')(se)
    se = layers.Conv1D(filters, 1, activation='sigmoid', padding='same')(se)
    return layers.multiply([input, se])

def MBConvBlock(input_filters, output_filters, kernel_size, strides, expand_ratio, se_ratio, id_skip, drop_rate):
    """MBConvBlock 模块"""
    has_se = (se_ratio is not None) and (0 < se_ratio <= 1)
    filters = input_filters * expand_ratio
    # 扩张卷积
    x = layers.Conv1D(filters, 1, padding='same', use_bias=False)(input)
    x = layers.BatchNormalization()(x)
    x = layers.Activation(tf.nn.swish)(x)
    # 深度卷积
    x = layers.DepthwiseConv1D(kernel_size, strides=strides, padding='same', use_bias=False)(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation(tf.nn.swish)(x)
    # Squeeze-and-Excitation
    if has_se:
        x = SEBlock(input_filters, se_ratio)(x)
    # 线性卷积
    x = layers.Conv1D(output_filters, 1, padding='same', use_bias=False)(x)
    x = layers.BatchNormalization()(x)
    # 残差连接
    if id_skip and strides == 1 and input_filters == output_filters:
        if drop_rate and drop_rate > 0:
            x = layers.Dropout(drop_rate)(x)
        x = layers.add([x, input])
    return x

def EfficientNetV2(input_shape, num_classes, width_multiplier=1.0, depth_multiplier=1.0, dropout_rate=0.2):
    """EfficientNetV2 模型"""
    inputs = layers.Input(shape=input_shape)
    # 第一个卷积层
    x = layers.Conv1D(round_filters(32, width_multiplier), 3, strides=2, padding='same', use_bias=False)(inputs)
    x = layers.BatchNormalization()(x)
    x = layers.Activation(tf.nn.swish)(x)
    # MBConvBlock 模块序列
    b = 0
    blocks = [
        # t, c, n, s, se
        [1, 16, round_repeats(1, depth_multiplier), 1, None],
        [4, 24, round_repeats(2, depth_multiplier), 2, None],
        [4, 32, round_repeats(3, depth_multiplier), 2, None],
        [4, 64, round_repeats(4, depth_multiplier), 2, None],
        [4, 96, round_repeats(3, depth_multiplier), 1, 0.25],
        [4, 160, round_repeats(3, depth_multiplier), 2, 0.25],
        [4, 320, round_repeats(1, depth_multiplier), 1, 0.25],
    ]
    for (t, c, n, s, se) in blocks:
        for i in range(n):
            b += 1
            strides = s if i == 0 else 1
            x = MBConvBlock(x, round_filters(c, width_multiplier), kernel_size=3, strides=strides, expand_ratio=t, se_ratio=se, id_skip=True, drop_rate=dropout_rate)
    # 最后的卷积层
    x = layers.Conv1D(round_filters(1280, width_multiplier), 1, padding='same', use_bias=False)(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation(tf.nn.swish)(x)
    # 全局平均池化和全连接层
    x = layers.GlobalAveragePooling1D()(x)
    x = layers.Dropout(dropout_rate)(x)
    outputs = layers.Dense(num_classes, activation='softmax')(x)
    return tf.keras.Model(inputs, outputs, name='EfficientNetV2')

这是一段简单的 EfficientNetV2 代码，其中包含了常用的一些模块，如 Squeeze-and-Excitation 模块和 MBConvBlock 模块。你可以根据自己的需要进行修改和扩展。