import argparse
import os
import random
import sys
# import matplotlib.image as mpimg
import numpy as np
import tensorflow as tf
import inference
import train
from utils import evaluation_utils
from utils import misc_utils as utils
from utils import vocab_utils
utils.check_tensorflow_version()
FLAGS = None
def add_arguments(parser):
"""Build ArgumentParser."""
parser.register("type", "bool", lambda v: v.lower() == "true")
# network
parser.add_argument("--num_units",
type=int,
default=32,
help="Network size.")
parser.add_argument("--num_layers",
type=int,
Created by Tudor Paraschivescu for the Cambridge UROP project
"Dialogue systems for language learning"
The hierarchical model with dynamic RNN support."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from chatbot.models.base_model import BaseModel
import utils.misc_utils as utils
from chatbot.models import model_helper
utils.check_tensorflow_version(version="1.3.0")
class HierarchicalModel(BaseModel):
"""
Sequence-to-sequence hierarchical model.
This class implements a multi-layer recurrent neural network as encoder,
a multi-layer recurrent neural network as a context encoder
and a multi-layer recurrent neural network decoder.
"""
def _build_encoder(self, hparams):
"""Build an encoder"""
encoder_num_layers = hparams.num_layers
encoder_num_residual_layers = hparams.num_residual_layers
