[SOLVED] Keras model predicts different results using the same input

Keras model predicts different results using the same input

I built a Keras sequential model on the simple dataset. I am able to train the model, however every time I try to get a prediction on the same input I get different values. Anyone knows why? I read through different Stackoverflow here (Why the exactly identical keras model predict different results for the same input data in the same env, Keras saved model predicting different values on different session, different prediction after load a model in keras), but couldn't find the answer. I tried to set the Tensorflow seed and still getting different results. Here is my code

from pandas import concat
from pandas import DataFrame
# create sequence
length = 10
sequence = [i/float(length) for i in range(length)]
# create X/y pairs
df = DataFrame(sequence)
df = concat([df, df.shift(1)], axis=1)
df.dropna(inplace=True)
print(df)
# convert to LSTM friendly format
values = df.values
X, y = values[:, 0], values[:, 1]
X = X.reshape(len(X), 1, 1)
print(X.shape, y.shape)

output is:

     0    0
1  0.1  0.0
2  0.2  0.1
3  0.3  0.2
4  0.4  0.3
5  0.5  0.4
6  0.6  0.5
7  0.7  0.6
8  0.8  0.7
9  0.9  0.8
(9, 1, 1) (9,)

Then start building the model

#configure network
from tensorflow import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
tf.random.set_seed(1337)

n_batch = len(X)
n_neurons = 10
#design network
model = Sequential()
model.add(LSTM(n_neurons, batch_input_shape=(n_batch, X.shape[1], X.shape[2]), stateful=True))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X,y,epochs=2,batch_size=n_batch,verbose=1,shuffle=False)

Now every time I run the following code to get the prediction I get different results as you can see here

model.predict(X)
********output**************
 array([[0.03817442],
       [0.07164046],
       [0.10493257],
       [0.13797525],
       [0.17069395],
       [0.20301574],
       [0.23486984],
       [0.26618803],
       [0.29690543]], dtype=float32)

model.predict(X)
********output**************
array([[0.04415776],
       [0.08242793],
       [0.12048437],
       [0.15823033],
       [0.19556962],
       [0.2324073 ],
       [0.26865062],
       [0.3042098 ],
       [0.33899906]], dtype=float32)

Solution

The problem is setting stateful=True in your LSTM layer, as this keeps the state between predict calls, so each prediction depends on previous predictions.

So as a solution, set stateful=False.