# Once trained, is it normal that LSTM-based neural nets output different values even though the input is the same?

I'm trying my hand at recurrent neural nets having successfully implemented a few simple feed-forward networks. I'm using tflearn and the following example code that performs a simple sentiment analysis using IMDB film reviews (see https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py):

from __future__ import division, print_function, absolute_import

import tflearn
from tflearn.datasets import imdb

train, test, _ = imdb.load_data(path='imdb.pkl', n_words=10000,
valid_portion=0.1)
trainX, trainY = train
testX, testY = test

# Data preprocessing
# Converting labels to binary vectors
trainY = to_categorical(trainY, nb_classes=2)
testY = to_categorical(testY, nb_classes=2)

# Network building
net = tflearn.input_data([None, 100])
net = tflearn.embedding(net, input_dim=10000, output_dim=128)
net = tflearn.lstm(net, 128, dropout=0.8)
net = tflearn.fully_connected(net, 2, activation='softmax')
loss='categorical_crossentropy')

# Training
model = tflearn.DNN(net, tensorboard_verbose=0)
model.fit(trainX, trainY, validation_set=(testX, testY), show_metric=True,
batch_size=32)

# Predict
model.predict(testX)


I noticed that if I call model.predict(testX) more than once, the output is not the same despite the input vectors being identical. Is this expected? If so, why?

Sorry in advance for the novice-level question

• Are you simply repeatedly calling the model.predict() function after the net is fitted or rerun the whole python script? Jul 9, 2016 at 22:39
• Repeatedly calling the predict function, not re-running the entire script. In other words, I only call model.fit() once.
– maxp
Jul 10, 2016 at 9:11