How to interpret get_weights for Keras GRU?

Issue

I am unable to interpret the results of get_weights from a GRU layer. Here’s my code –

#Modified from - https://machinelearningmastery.com/understanding-simple-recurrent-neural-networks-in-keras/
from pandas import read_csv
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, SimpleRNN, GRU
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
import math
import matplotlib.pyplot as plt

model = Sequential()
model.add(GRU(units = 2, input_shape = (3,1), activation = 'linear'))
model.add(Dense(units = 1, activation = 'linear'))
model.compile(loss = 'mean_squared_error', optimizer = 'adam')

initial_weights = model.layers[0].get_weights()
print("Shape = ",initial_weights)

I am familiar with GRU concepts. In addition, I understand how the get_weights work for Keras Simple RNN layer, where the first array represents the input weights, the second the activation weights and the third the bias. However, I am lost with output of GRU, which is given below –

Shape =  [array([[-0.64266175, -0.0870676 , -0.25356603, -0.03685969,  0.22260845,
        -0.04923642]], dtype=float32), array([[ 0.01929092, -0.4932567 ,  0.3723044 , -0.6559699 , -0.33790302,
         0.27062896],
       [-0.4214194 ,  0.46456426,  0.27233726, -0.00461334, -0.6533575 ,
        -0.32483965]], dtype=float32), array([[0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0.]], dtype=float32)]

I am assuming it has something to do with GRU gates.

Update:7/4 – This page says that keras GRU has 3 gates, update, reset and output. However, based on this, GRU shouldn’t have the output gate.

Solution

Best way I know would be to track the add_weight() calls in the build() function of the GRUCell.

Let’s take an example model,

model = tf.keras.models.Sequential(
    [
     tf.keras.layers.GRU(32, input_shape=(5, 10), name='gru'),
     tf.keras.layers.Dense(10)
    ]
)

How we’ll print some metadata about what’s returned by weights = model.get_layer('gru').get_weights(). Which gives,

Number of arrays in weights: 3
Shape of each array in weights: [(10, 96), (32, 96), (2, 96)]

Let’s go back to what weights defined by the GRUCell. We got,

self.kernel = self.add_weight(
    shape=(input_dim, self.units * 3),
    ...
)
self.recurrent_kernel = self.add_weight(
    shape=(self.units, self.units * 3),
    ...
)

    ...
    bias_shape = (2, 3 * self.units)
    self.bias = self.add_weight(
        shape=bias_shape,
        ...
    )

This is what you’re seeing as weights (in that order). Here’s why they are shaped like this. GRU computations are outlined here.

GRU computations

The first matrix in weights (of shape [10, 96]) is a concatenation of Wz|Wr|Wh (in that order). Each of these is a [10, 32] sized tensor. Concatenation gives a [10, 32*3=96] sized tensor.

Similarly, the second matrix is a concatenation of Uz|Ur|Uh. Each of these is a [32, 32] sized tensor which becomes [32, 96] after concatenation.
You can see how they break this combined weight matrix to each of z, r and h components here.

Finally the bias. It contains 2 biases i.e. [2, 96] sized tensor; input_bias and recurrent_bias. Again, biases from all gates/weights are combined to a single tensor. Typically, only the input_bias is used. But if you have reset_after (decides how the reset gate is applied) set to True, then the recurrent_bias gets used. It’s an implementation detail.

Answered By – thushv89

This Answer collected from stackoverflow, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0

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