ResNet-DenseNet Architecture with ResU Blocks（Tensorflow版本）

import tensorflow as tf
from tensorflow.keras import layers, models
import math

def Bottleneck(x, growthRate):
    interChannels = 4 * growthRate
    out = layers.BatchNormalization()(x)
    out = tf.nn.relu(out)
    out = layers.Conv1D(interChannels, kernel_size=1, use_bias=False)(out)

    out = layers.BatchNormalization()(out)
    out = tf.nn.relu(out)
    out = layers.Conv1D(growthRate, kernel_size=3, padding='same', use_bias=False)(out)

    out = layers.Concatenate()([x, out])
    return out



def SingleLayer(x, growthRate):
    out = layers.BatchNormalization()(x)
    out = tf.nn.relu(out)
    out = layers.Conv1D(growthRate, kernel_size=3, padding='same', use_bias=False)(out)

    out = layers.Concatenate()([x, out])
    return out


def Transition(x, nOutChannels, down=False):
    out = layers.BatchNormalization()(x)
    out = tf.nn.relu(out)
    out = layers.Conv1D(nOutChannels, kernel_size=1, use_bias=False)(out)

    if down:
        out = layers.AveragePooling1D(pool_size=2)(out)

    return out


def ResidualUBlock(inputs, out_ch, mid_ch, layers_num, downsampling=True):
    K = 9  # Kernel size

    x = layers.Conv1D(out_ch, kernel_size=K, padding="same", use_bias=False)(inputs)
    x = layers.BatchNormalization()(x)
    x = tf.nn.leaky_relu(x)

    encoders_out = []
    for idx in range(layers_num):
        encoder_x = layers.Conv1D(mid_ch, kernel_size=K, strides=2, padding="same", use_bias=False)(x)
        encoder_x = layers.BatchNormalization()(encoder_x)
        encoder_x = tf.nn.leaky_relu(encoder_x)
        # print('encoder_x',e