deeppavlov/core/models/tf_model.py
def print_number_of_parameters():
        """
        Print number of *trainable* parameters in the network
        """
        log.info('Number of parameters: ')
        variables = tf.trainable_variables()
        blocks = defaultdict(int)
        for var in variables:
            # Get the top level scope name of variable
            block_name = var.name.split('/')[0]
            number_of_parameters = np.prod(var.get_shape().as_list())
            blocks[block_name] += number_of_parameters
        for block_name, cnt in blocks.items():
            log.info("{} - {}.".format(block_name, cnt))
        total_num_parameters = np.sum(list(blocks.values()))
        log.info('Total number of parameters equal {}'.format(total_num_parameters))
def print_number_of_parameters():
        """
        Print number of *trainable* parameters in the network
        """
        log.info('Number of parameters: ')
        variables = tf.trainable_variables()
        blocks = defaultdict(int)
        for var in variables:
            # Get the top level scope name of variable
            block_name = var.name.split('/')[0]
            number_of_parameters = np.prod(var.get_shape().as_list())
            blocks[block_name] += number_of_parameters
        for block_name, cnt in blocks.items():
            log.info("{} - {}.".format(block_name, cnt))
        total_num_parameters = np.sum(list(blocks.values()))
        log.info('Total number of parameters equal {}'.format(total_num_parameters))
def print_number_of_parameters ( ) : log . info ( 'Number of parameters: ' ) variables = tf . trainable_variables ( ) blocks = defaultdict ( int ) for var in variables : # Get the top level scope name of variable block_name = var . name . split ( '/' ) [ 0 ] number_of_parameters = np . prod ( var . get_shape ( ) . as_list ( ) ) blocks [ block_name ] += number_of_parameters for block_name , cnt in blocks . items ( ) : log . info ( "{} - {}." . format ( block_name , cnt ) ) total_num_parameters = np . sum ( list ( blocks . values ( ) ) ) log . info ( 'Total number of parameters equal {}' . format ( total_num_parameters ) )
Print number of *trainable* parameters in the network
Print number of * trainable * parameters in the network