Keras : Why does Sequential and Model give different outputs?

Question

I am using Keras for computing a simple sequence classification neural network. I played with the different module and I found that there are two way to create Sequential neural network.

The first way is to use Sequential API. This is the most common way which I found in a lot of tutorial/documentation. Here is the code :

# Sequential Neural Network using Sequential()
model = Sequential()
model.add(Conv1D(filters=32, kernel_size=3, padding='same', activation='relu', input_shape=(27 , 300,)))
model.add(MaxPooling1D(pool_size=2))
model.add(LSTM(100))
model.add(Dense(len(7, activation='softmax'))
model.summary()

The second ways is to build de sequential neural network from "scratch" with the Model API. Here is the code.

# Sequential neural network using Model()   
inputs = Input(shape=(27 , 300))
x = Conv1D(filters=32, kernel_size=3, padding='same', activation='relu')(inputs)
x = MaxPooling1D(pool_size=2)(x)
x = LSTM(100)(x)
predictions = Dense(7, activation='softmax')(x)
model = Model(inputs=inputs, outputs=predictions)
model.summary()

I trained it both with a fixed seed (np.random.seed(1337)), with the same training data and my output are different... Knowing that the only difference in the summary is the first layer of inputs with the Model API.

Is there anyone that knows why this neural network are different ? And if there are not, why did i get different results ?

Thanks

Answer 1

You setup the random seed only in numpy and not in tensorflow (in case it's the backend of keras in your case). Try to add this in your code:

from numpy.random import seed
seed(1337)
from tensorflow import set_random_seed
set_random_seed(1337)

the detailed article about this topic here