Val_accuracy (val_acc) very low We have a data set that is converted from signal data to video. We want to classify these images using convolution. We tried many different methods but val acc is consistently low. Training accuracy is 99% and val_acc is 40%. We need your help in this respect. Thank you

weight_decay=0.0005
input_ = Input(shape=(125, 125, 1))
# Block 1
x = Conv2D(64, (3, 3), strides=(1, 1), activation='relu', padding='same',kernel_regularizer='0.001')(input_)
x = BatchNormalization()(x)
x = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='pool1')(x)

# Block 2
x = Conv2D(128, (3, 3), strides=(1, 1), activation='relu', padding='same',kernel_regularizer='0.001')(x)
x = BatchNormalization()(x)
x = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='pool2')(x)

# Block 3
x = Conv2D(256, (3, 3), strides=(1, 1), activation='relu', padding='same',kernel_regularizer='0.001')(x)
x = BatchNormalization()(x)
x = Conv2D(256, (3, 3), strides=(1, 1), activation='relu', padding='same',kernel_regularizer='0.001')(x)
x = BatchNormalization()(x)
x = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='pool3')(x)

# Block 4
x = Conv2D(512, (3, 3), strides=(1, 1), activation='relu', padding='same',kernel_regularizer='0.001')(x)
x = BatchNormalization()(x)
x = Conv2D(512, (3, 3), strides=(1, 1), activation='relu', padding='same',kernel_regularizer='0.001')(x)
x = BatchNormalization()(x)
x = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='pool4')(x)
x = GlobalMaxPooling2D()(x)

x = Dense(800,kernel_regularizer='0.001')(x)
x = Dropout(0.5)(x)
x = BatchNormalization()(x)
x = Dense(800,kernel_regularizer='0.001')(x)
x = Dropout(0.5)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dense(8)(x)
x = Activation('softmax')(x)
model = Model(inputs = input_, outputs=x)


enter image description here

my kernel and dataset
https://www.kaggle.com/ultrasonraporlama/video-kernel/
Try adding dropout layers to your network, this should work very well to reduce the amount of overfitting of your network.