# How to interpret the model weights extracted from tensorflow2 keras LSTM model?

I fitted a tensorflow.keras.layers.LSTM model and extracted the model weights via get_weights(). However, I find it hard to interpret the weights array.

To be specific, I set the model by

model = Sequential()


and fit the model with the data of the following shape

train_X.shape, train_y.shape, test_X.shape, test_y.shape
## ((23, 10, 10), (23,), (6, 10, 10), (6,))


The code for model fitting:

model.fit(train_X, train_y, epochs=50, batch_size=4,
validation_data=(test_X, test_y), verbose=2, shuffle=True)


The shape of model weights:

[w.shape for w in model.get_weights()]
## [(10, 512), (128, 512), (512,), (128, 1), (1,)]


The math formula of LSTM:

As you can see from the formula, there are eight weight matrices and four bias vectors. However, I don't know how to match them to the weights array.

When you print

print(model.layers[0].trainable_weights)


you should see three tensors: lstm_1/kernel, lstm_1/recurrent_kernel, lstm_1/bias:0 One of the dimensions of each tensor should be a product of

4 * number_of_units where number_of_units is your number of neurons.

Try:

units = int(int(model.layers[0].trainable_weights[0].shape[1])/4)
print("No units: ", units)


That is because each tensor contains weights for four LSTM units (in that order):

i (input), f (forget), c (cell state) and o (output) Therefore in order to extract weights you can simply use slice operator:

W = model.layers[0].get_weights()[0]
U = model.layers[0].get_weights()[1]
b = model.layers[0].get_weights()[2]

W_i = W[:, :units]
W_f = W[:, units: units * 2]
W_c = W[:, units * 2: units * 3]
W_o = W[:, units * 3:]

U_i = U[:, :units]
U_f = U[:, units: units * 2]
U_c = U[:, units * 2: units * 3]
U_o = U[:, units * 3:]

b_i = b[:units]
b_f = b[units: units * 2]
b_c = b[units * 2: units * 3]
b_o = b[units * 3:]