sequential model give a different result at every run

Question

I have a python script for building a keras sequential model. Everytime i am getting different results without any changes in script. kindly have a look on script. where i am wrong please help.

thedata = pandas.read_csv("C:/User/Downloads/LSTM/data.csv", sep=', ', delimiter=',', header='infer', names=None)

np.random.seed(1337)

x = thedata['Review']
y = thedata['Polarity_Numeral']
x = x.iloc[:].values
y = y.iloc[:].values

tk = Tokenizer(num_words=40000, lower=True, split=" ")
tk.fit_on_texts(x)
x = tk.texts_to_sequences(x)    
max_len = 120
x = pad_sequences(x, maxlen=max_len)
max_features = 40000
testx = x[51000:52588]
print (testx)
testy = y[51000:52588]
x = x[0:50999]
y = y[0:50999]


model = Sequential()
model.add(Embedding(max_features, 128, input_length=max_len))
model.add(SpatialDropout1D(0.3))
model.add(GaussianNoise(0.2))
model.add(LSTM(128 , dropout_W=0.3, dropout_U=0.3, return_sequences=False))
model.add(Dense(1, W_regularizer=l2(0.2)))
model.add(Activation('sigmoid'))
model.summary()
adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.00)
model.compile(loss='binary_crossentropy', optimizer=adam,metrics = ['accuracy'] )
model_history = model.fit(x, y=y, batch_size=64, epochs=1, verbose=1,validation_split = 0.2)
model.save('C:/User/Downloads/model.h5')
model.save_weights('C:/User/Downloads/weight_model.h5')

predictions = model.predict(testx)
print (predictions)

On first time run, i am getting i.e 57% On Second time run .. 53% On third .. 55% Everytime it is changing randomly. Thanks for the help!

Answer 1

This code is for tensorflow backend

This is because the weights are initialised using random numbers and hence you will get different results every time. This is expected behaviour. To have reproducible result you need to set the random seed as:

import tensorflow as tf
import random as rn

os.environ['PYTHONHASHSEED'] = '0'

# Setting the seed for numpy-generated random numbers
np.random.seed(37)

# Setting the seed for python random numbers
rn.seed(1254)

# Setting the graph-level random seed.
tf.set_random_seed(89)

from keras import backend as K

session_conf = tf.ConfigProto(
      intra_op_parallelism_threads=1,
      inter_op_parallelism_threads=1)

#Force Tensorflow to use a single thread
sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)

K.set_session(sess)

# Rest of the code follows from here on ...