Please, I am stuck, I can not understand the number of parameters of a simple RNN, here the example and the model summary. the example is simple:
x = np.linspace(0,50,501) y= np.sin(x) df= pd.DataFrame(data=y, index=x, columns=['Sinus']) Then I would to build a simple RNNs to predict this sine wave,
test_percent = 0.1 test_point= np.round(len(df)*test_percent) test_ind = int(len(df)-test_point) train = df.iloc[:test_ind] test = df.iloc[test_ind:] from sklearn.preprocessing import MinMaxScaler scaled_train = scaler.transform(train) scaled_test = scaler.transform(test) from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator generator = TimeseriesGenerator(scaled_train, scaled_train, length=50, batch_size=1) from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, SimpleRNN n_features=1 model= Sequential() model.add(SimpleRNN(units=50, input_shape=(50,1))) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') model.summary() 
I do not understand the number 2600 in the model summary! because according to this intuition of RNNS 
in my case the dimension of U is (50,1), so 50 weights, then for V, also 50 weights, and for W, also 50 weights, for me, only 150 parameters, why this number in the summary: 2600 parameters?
