human
I am working on the file "GRU/GRU.ipynb".
The first task is:
GRU  class and functions
Can you write Python program for it?
 What is a the Python program you requested?
assistant
#Here is the Python program you requested.

class RNN_cell(object):

    """
    RNN cell object which takes 3 arguments for initialization.
    input_size = Input Vector size
    hidden_layer_size = Hidden layer size
    target_size = Output vector size

    """

    def __init__(self, input_size, hidden_layer_size, target_size):

        #Initialization of given values
        self.input_size = input_size
        self.hidden_layer_size = hidden_layer_size
        self.target_size = target_size
        
        # Weights for input and hidden tensor
        self.Wx = tf.Variable(tf.zeros([self.input_size,self.hidden_layer_size]))
        self.Wr = tf.Variable(tf.zeros([self.input_size,self.hidden_layer_size]))
        self.Wz = tf.Variable(tf.zeros([self.input_size,self.hidden_layer_size]))
        
        self.br = tf.Variable(tf.truncated_normal([self.hidden_layer_size],mean=1))
        self.bz = tf.Variable(tf.truncated_normal([self.hidden_layer_size],mean=1))
        
        self.Wh = tf.Variable(tf.zeros([self.hidden_layer_size,self.hidden_layer_size]))

        
        #Weights for output layer
        self.Wo = tf.Variable(tf.truncated_normal([self.hidden_layer_size,self.target_size],mean=1,stddev=.01))
        self.bo = tf.Variable(tf.truncated_normal([self.target_size],mean=1,stddev=.01))
        # Placeholder for input vector with shape[batch, seq, embeddings]
        self._inputs = tf.placeholder(tf.float32,
                                      shape=[None, None, self.input_size],
                                      name='inputs')

        # Processing inputs to work with scan function
        self.processed_input = process_batch_input_for_RNN(self._inputs)

        '''
        Initial hidden state's shape is [1,self.hidden_layer_size]
        In First time stamp, we are doing dot product with weights to
        get the shape of [batch_size, self.hidden_layer_size].
        For this dot product tensorflow use broadcasting. But during
        Back propagation a low level error occurs.
        So to solve the problem it was needed to...