Tensorflow RNN weight matrices initialization

Question

I\'m using bidirectional_rnn with GRUCell but this is a general question regarding the RNN in Tensorflow.

I couldn\'t find how to initializ

Answer 1

The RNN models will create their variables with get_variable, and you can control the initialization by wrapping the code which creates those variables with a variable_scope and passing a default initializer to it. Unless the RNN specifies one explicitly (looking at the code, it doesn't), uniform_unit_scaling_initializer is used.

You should also be able to share model weights by declaring the second model and passing reuse=True to its variable_scope. As long as the namespaces match up, the new model will get the same variables as the first model.

Answer 2

A simple way to initialize all kernel weights with certain initializer is to leave the initializer in tf.variable_scope(). For example:

with tf.variable_scope('rnn', initializer=tf.variance_scaling_initializer()):
   basic_cell= tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)
   outputs, state= tf.nn.dynamic_rnn(basic_cell, X, dtype=tf.float32)

Answer 3

How to initialize weight matrices for RNN?

I believe people are using random normal initialization for weight matrices for RNN. Check out the example in TensorFlow GitHub Repo. As the notebook is a bit long, they have a simple LSTM model where they use tf.truncated_normal to initialize weights and tf.zeros to initialize biases (although I have tried using tf.ones to initialize biases before, seem to also work). I believe that the standard deviation is a hyperparameter you could tune yourself. Sometimes weights initialization is important to the gradient flow. Although as far as I know, LSTM itself is designed to handle gradient vanishing problem (and gradient clipping is for helping gradient exploding problem), so perhaps you don't need to be super careful with the setup of std_dev in LSTM? I've read papers recommending Xavier initialization (TF API doc for Xavier initializer) in Convolution Neural Network context. I don't know if people use that in RNN, but I imagine you can even try those in RNN if you want to see if it helps.

Now to follow up with @Allen's answer and your follow up question left in the comments.

How to control initialization with variable scope?

Using the simple LSTM model in the TensorFlow GitHub python notebook that I linked to as an example. Specifically, if I want to re-factorize the LSTM part of the code in above picture using variable scope control, I may code something as following...

import tensorflow as tf
def initialize_LSTMcell(vocabulary_size, num_nodes, initializer):
    '''initialize LSTMcell weights and biases, set variables to reuse mode'''
    gates = ['input_gate', 'forget_gate', 'memory_cell', 'output_gate']
    with tf.variable_scope('LSTMcell') as scope:
        for gate in gates:
            with tf.variable_scope(gate) as gate_scope:
                wx = tf.get_variable("wx", [vocabulary_size, num_nodes], initializer)
                wt = tf.get_variable("wt", [num_nodes, num_nodes], initializer)
                bi = tf.get_variable("bi", [1, num_nodes, tf.constant_initializer(0.0)])
                gate_scope.reuse_variables() #this line can probably be omitted, b.z. by setting 'LSTMcell' scope variables to 'reuse' as the next line, it'll turn on the reuse mode for all its child scope variables
        scope.reuse_variables()

def get_scope_variables(scope_name, variable_names):
    '''a helper function to fetch variable based on scope_name and variable_name'''
    vars = {}
    with tf.variable_scope(scope_name, reuse=True):
        for var_name in variable_names
            var = tf.get_variable(var_name)
            vars[var_name] = var
    return vars

def LSTMcell(i, o, state):
    '''a function for performing LSTMcell computation'''
    gates = ['input_gate', 'forget_gate', 'memory_cell', 'output_gate']
    var_names = ['wx', 'wt', 'bi']
    gate_comp = {}
    with tf.variable_scope('LSTMcell', reuse=True):
        for gate in gates:
            vars = get_scope_variables(gate, var_names)
            gate_comp[gate] = tf.matmul(i, vars['wx']) + tf.matmul(o, vars['wt']) + vars['bi']
    state = tf.sigmoid(gate_comp['forget_gate']) * state + tf.sigmoid(gate_comp['input_gate']) * tf.tanh(gate_comp['memory_cell'])
    output = tf.sigmoid(gate_comp['output_gate']) * tf.tanh(state)
    return output, state

The usage of the re-factorized code would be something like following...

initialize_LSTMcell(volcabulary_size, num_nodes, tf.truncated_normal_initializer(mean=-0.1, stddev=.01))
#...Doing some computation...
LSTMcell(input_tensor, output_tensor, state)

Even though the refactorized code may look less straightforward, but using scope variable control ensures scope encapsulation and allows flexible variable controls (in my opinion at least).

In pre-training some LSTMs and using their weights in a subsequent model. How to do that without saving all the states and restoring the entire model.

Assuming you have a pre-trained model froze and loaded in, if you wanna use their frozen 'wx', 'wt' and 'bi', you can simply find their parent scope names and variable names, then fetch the variables using similar structure in get_scope_variables func.

with tf.variable_scope(scope_name, reuse=True):
    var = tf.get_variable(var_name)

Here is a link to understanding variable scope and sharing variables. I hope this is helpful.