10_8.py

from keras.layers import *
from keras.models import *
from keras.callbacks import *
import keras.backend as K

K.clear_session()
model = Sequential()
    
#embedding layer
model.add(Embedding(len(unique_x), 100, input_length=32,trainable=True)) 

model.add(Conv1D(64,3, padding='causal',activation='relu'))
model.add(Dropout(0.2))
model.add(MaxPool1D(2))
    
model.add(Conv1D(128,3,activation='relu',dilation_rate=2,padding='causal'))
model.add(Dropout(0.2))
model.add(MaxPool1D(2))

model.add(Conv1D(256,3,activation='relu',dilation_rate=4,padding='causal'))
model.add(Dropout(0.2))
model.add(MaxPool1D(2))
          
#model.add(Conv1D(256,5,activation='relu'))    
model.add(GlobalMaxPool1D())
    
model.add(Dense(256, activation='relu'))
model.add(Dense(len(unique_y), activation='softmax'))
    
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')

model.summary()