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LSTM training multiclass with Keras
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# X_train contains word indices (single int between 0 and max_words) | |
# Y_train0 contains class indices (single int between 0 and nb_classes) | |
X_train = sequence.pad_sequences(X_train, maxlen=maxlen, padding='post') | |
X_test = sequence.pad_sequences(X_test, maxlen=maxlen, padding='post') | |
Y_train = np.zeros((batchSize,globvars.nb_classes))#,dtype=np.float32) | |
for t in range(batchSize): | |
Y_train[t][Y_train0[t]]=1 | |
Y_test = np.zeros((len(Y_test0),globvars.nb_classes))#,dtype=np.float32) | |
for t in range(len(Y_test)): | |
Y_test[t][Y_test0[t]]=1 | |
model = Sequential() | |
model.add(Embedding(globvars.max_words, globvars.embedsize, input_length=maxlen)) | |
model.add(LSTM(globvars.hidden,return_sequences=False)) | |
model.add(Dropout(0.5)) | |
model.add(Dense(globvars.nb_classes)) | |
model.add(Activation('softmax')) | |
model.compile(loss='categorical_crossentropy', optimizer='adam') | |
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=3) | |
score, acc = model.evaluate(X_test, Y_test, | |
batch_size=batch_size, | |
show_accuracy=True) |
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