I wanna use predictions in real time, but I want to test that the results are the same as in the case of giving all the data as input only once. This is the code:
import numpy as np
from keras.layers import Input, Dense, SimpleRNN
from keras.models import Model
#MODEL 1, for training
x = Input(shape=(None, 1))
h = SimpleRNN(30, return_sequences=True)(x)
out = Dense(1)(h)
model = Model(x, out)
model.compile(loss='mse', optimizer='adam')
X_train = np.random.rand(100, 50, 1)
y_train = np.random.rand(100, 50, 1)
model.fit(X_train, y_train, verbose = False)
#MODEL 1, for predictions in real time
x = Input(batch_shape=(1, None, 1))
h = SimpleRNN(30, stateful=True, return_sequences=True)(x)
out = Dense(1)(h)
predict_model = Model(x, out)
predict_model.set_weights(model.get_weights())
X = np.random.rand(2, 2, 1)
predictions = model.predict(X, verbose = False)
for sim in range(len(predictions)):
for i in range(len(predictions[0])):
pred = predict_model.predict(X[sim:(sim+1), i:(i + 1), :], verbose = False)
print(pred[0][0]) #Predictions in real time
print(predictions[sim][i]) #Predictions with MODEL 1
print()
predict_model.reset_states()
It prints this:
[0.09156141]
[0.09156139]
[-0.38076958]
[-0.38076955]
[0.12214336]
[0.12214339]
[-0.52013564]
[-0.5201356]
The results must be exactly the same because both have the same weights. What is happening?
