optimizer = optimizers.Adam(lr=1e-4)
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
#####________________-
#removeing names infromt of nodes
vals = [
'Top0', 'Top1', 'Top2', 'Top3', 'Middle0', 'Middle1', 'Middle2', 'Base0',
'Base1'
]
for i in ['0', '1', '2', '3']:
print(i)
for val in vals:
perumes_old[i] = perumes_old[i].apply(lambda x: x.replace(val, ''))
#_______________________________________#
def clean_text(x):
x = x.lower()
x = re.sub('[^A-Za-z0-9]+', ' ', x)
return x
cust_df['text'] = cust_df['text'].apply(lambda x: clean_text(x))
train_X = cust_df['text'].fillna("##").values
tokenizer = Tokenizer(num_words=max_features)
