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ReneeD/
EEGdenoiseNet
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[8adc28]: / code / Novel_CNN / Novel_CNN.py

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import tensorflow as tf

import numpy as np

import os

from tensorflow import keras

from tensorflow.keras import layers,Sequential

from tensorflow.keras.models import *

from tensorflow.keras.layers import *

from tensorflow.keras.optimizers import *

from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler

from tensorflow.keras import backend as keras



def Novel_CNN(input_size = ( 1024, 1)):

    inputs = Input(input_size)

    conv1 = layers.Conv1D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)

    conv1 = layers.Conv1D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)

    pool1 = AveragePooling1D(pool_size= 2)(conv1)



    conv2 = layers.Conv1D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)

    conv2 = layers.Conv1D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)

    pool2 = AveragePooling1D(pool_size= 2)(conv2)



    conv3 = layers.Conv1D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)

    conv3 = layers.Conv1D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)

    pool3 = AveragePooling1D(pool_size= 2)(conv3) #9



    conv4 = layers.Conv1D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)

    conv4 = layers.Conv1D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)

    drop4 = layers.Dropout(0.5)(conv4)

    pool4 = AveragePooling1D(pool_size = 2)(drop4)  #13



    conv5 = layers.Conv1D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)  #14

    conv5 = layers.Conv1D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)

    drop5 = layers.Dropout(0.5)(conv5)

    ###

    pool5 = AveragePooling1D(pool_size = 2)(drop5)



    conv6 = layers.Conv1D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool5)  #18

    conv6 = layers.Conv1D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)

    drop6 = layers.Dropout(0.5)(conv6)



    pool6 = AveragePooling1D(pool_size = 2)(drop6)



    conv7 = layers.Conv1D(2048, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool6)  #22

    conv7 = layers.Conv1D(2048, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)

    drop7 = layers.Dropout(0.5)(conv7)

    #######

    flatten1 = layers.Flatten()(drop7)

    #output1 = layers.Dense(2048)(flatten1)

    output1 = layers.Dense(1024)(flatten1)

    model = Model(inputs = inputs, outputs = output1)



    model.summary()

    return model