model.save and load giving different result

I am trying to save a simple LSTM model for text classification. The input of the model is padded vectorized sentences.

model = Sequential()
model.add(LSTM(40, input_shape=(16, 32)))
model.add(Dense(20))
model.add(Dense(8, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

For saving I’m using the following snippet:

for i in range(50):
    from sklearn.cross_validation import train_test_split

    data_train, data_test, labels_train, labels_test = train_test_split(feature_set, dummy_y, test_size=0.1, random_state=i)
    accuracy = 0.0
    try:
        with open('/app/accuracy', 'r') as file:
            for line in file:
                accuracy = float(line)
    except Exception:
        print("error")
    model.fit(data_train, labels_train, nb_epoch=50)
    loss, acc = model.evaluate(feature_set, dummy_y)
    if acc > accuracy:
        model.save_weights("model.h5", overwrite=True)
        model.save('my_model.h5', overwrite=True)
        print("Saved model to disk.\nAccuracy:")
        print(acc)
        with open('/app/accuracy', 'w') as file:
            file.write('%f' % acc)

But whenever I’m trying to load the same model

from keras.models import load_model
model = load_model('my_model.h5')

I’m getting random accuracy like an untrained model. same result even when trying to load weights separately. If I set the weights

lstmweights=model.get_weights()
model2.set_weights(lstmweights)

like above. It is working if model and model2 are run under same session (same notebook session). If I serialize lstmweights and try to load it from different place, again I’m getting result like untrained model. It seems saving only the weights are not enough. So why model.save is not working. Any known point?

[HarshaVardhanP]

I crosschecked all these functions - they seem to be working properly. model.save(), load_model(), model.save_weights() and model.load_weights()

[pras135]

Use model.model.save() instead of model.save()