product-titles-classifiy-text-cnn.py

from keras.models import Sequential
from keras.layers import Conv1D, GlobalMaxPooling1D, Flatten
from keras.layers import Dense, Input, LSTM, Embedding, Dropout, Activation


model = Sequential()
model.add(embedding_layer)
model.add(Dropout(0.2))
model.add(Conv1D(300, 3, padding='valid',activation='relu',strides=2))
model.add(Conv1D(150, 3, padding='valid',activation='relu',strides=2))
model.add(Conv1D(75, 3, padding='valid',activation='relu',strides=2))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(150,activation='sigmoid'))
model.add(Dropout(0.2))
model.add(Dense(3,activation='sigmoid'))

model.compile(loss='categorical_crossentropy',optimizer='rmsprop',metrics=['acc'])

model.summary()