Step-by-Step Hidden State Calculation in a Recurrent Neural Network

Mon, 01 Dec 2025 18:32:24 +0000

Consider a simplified Recurrent Neural Network (RNN) with a single input and a single output. The hidden state is updated using the recurrence:

$$ h_t = \text{ReLU}(W_{ih} \cdot x_t + W_{hh} \cdot h_{t-1}) $$

Assume the following:

$ x_t = 3 $ for every time step
$ h_0 = 0 $
$ W_{ih} = 0.4 $
$ W_{hh} = 0.6 $
Activation function: ReLU

Compute the value of the hidden state $ h_4 $ at time $ t = 4 $.

Passing variable length sentences to Tensorflow LSTM

Mon, 11 Feb 2019 05:06:27 +0000

I have a tensorflow LSTM model for predicting the sentiment. I build the model with the maximum sequence length 150. (Maximum number of words) While making predictions, i have written the code as below:

batchSize = 32
maxSeqLength = 150

def getSentenceMatrix(sentence):
    arr = np.zeros([batchSize, maxSeqLength])
    sentenceMatrix = np.zeros([batchSize,maxSeqLength], dtype='int32')
    cleanedSentence = cleanSentences(sentence)
    cleanedSentence = ' '.join(cleanedSentence.split()[:150])
    split = cleanedSentence.split()
    for indexCounter,word in enumerate(split):
        try:
            sentenceMatrix[0,indexCounter] = wordsList.index(word)
        except ValueError:
            sentenceMatrix[0,indexCounter] = 399999 #Vector for unkown words
    return sentenceMatrix

input_text = "example data"
inputMatrix = getSentenceMatrix(input_text)

In the code i'm truncating my input text to 150 words and ignoring remaining data.Due to this my predictions are wrong.

cleanedSentence = ' '.join(cleanedSentence.split()[:150])

I know that if we have lesser length than sequence length we can pad with zero's. What we need to do if we have more length. Can you suggest me the best way to do this. Thanks in advance.

Ask Ghassem - Recent questions tagged rnn

Step-by-Step Hidden State Calculation in a Recurrent Neural Network

Passing variable length sentences to Tensorflow LSTM