add ensemble - you don't count vectorize, you use ensemble
https://github.com/dnishimoto/python-deep-learning/blob/master/UFO%20.ipynb
docs=ufo_df["summary"] #text
LABELS=['Egg', 'Cross','Sphere', 'Triangle','Disk','Oval','Rectangle','Teardrop']
#LABELS=['Triangle']
target=ufo_df[LABELS]
#print([len(d) for d in docs])
encoded_docs=[one_hot(d,vocab_size) for d in docs]
#print([np.max(d) for d in encoded_docs])
padded_docs = pad_sequences(encoded_docs, maxlen=max_length, padding='post')
#print([d for d in padded_docs])
model=Sequential()
model.add(Embedding(vocab_size, 8, input_length=max_length))
model.add(Flatten())
model.add(Dense(8, activation='softmax'))
#model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(padded_docs, target, epochs=50, verbose=0)
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