ysyun

#! /usr/bin/env zsh

# Ref: https://github.com/creationix/nvm#calling-nvm-use-automatically-in-a-directory-with-a-nvmrc-file
# place this after nvm initialization!

autoload -Uz add-zsh-hook

# Function: load-nvmrc
load-nvmrc() {
    local _CUR_NODE_VER="$(nvm version)"

ysyun

# Lab 12 RNN
import tensorflow as tf
import numpy as np
tf.set_random_seed(777)  # reproducibility

idx2char = ['h', 'i', 'e', 'l', 'o']
# Teach hello: hihell -> ihello
x_data = [[0, 1, 0, 2, 3, 3]]   # hihell
x_one_hot = [[[1, 0, 0, 0, 0],   # h 0
              [0, 1, 0, 0, 0],   # i 1

ysyun

python -m bin.infer \
  --tasks "
    - class: DecodeText
      params:
        delimiter: '' " \
  --model_dir vizmodel \
  --model_params "
    inference.beam_search.beam_width: 2" \
  --input_pipeline "
    class: ParallelTextInputPipeline

ysyun

model: AttentionSeq2Seq
model_params:
  attention.class: seq2seq.decoders.attention.AttentionLayerBahdanau
  attention.params:
    num_units: 512
  bridge.class: seq2seq.models.bridges.ZeroBridge
  embedding.dim: 512
  encoder.class: seq2seq.encoders.BidirectionalRNNEncoder
  encoder.params:
    rnn_cell:

ysyun

/*
$ cat test.csv
1,2,3,,5,6,7,8,9
0,9,8,7,6,5,4,3,2
2,3,4,5,6,7,8,9,0
5,4,3,2,16,7,8,9,3
*/

import tensorflow as tf

ysyun

def run_inference():
    # tf.reset_default_graph()
    with graph.as_default():
        saver = tf.train.Saver()
        checkpoint_path = loaded_checkpoint_path
        if not checkpoint_path:
            checkpoint_path = tf.train.latest_checkpoint(model_dir_input)

        def session_init_op(_scaffold, sess):
            saver.restore(sess, checkpoint_path)

ysyun

def run_inference():
    # tf.reset_default_graph()
    with graph.as_default():
        saver = tf.train.Saver()
        checkpoint_path = loaded_checkpoint_path
        if not checkpoint_path:
            checkpoint_path = tf.train.latest_checkpoint(model_dir_input)

        def session_init_op(_scaffold, sess):
            saver.restore(sess, checkpoint_path)

ysyun

# export DATA_DIR=project-directory
export DATA_DIR=.
python -m bin.train \
  --config_paths="
      $DATA_DIR/example_configs/nmt_large.yml,
      $DATA_DIR/example_configs/train_seq2seq.yml,
      $DATA_DIR/example_configs/text_metrics_bpe.yml" \
  --model_params "
      vocab_source: $DATA_DIR/sourcedata/vocab.source
      vocab_target: $DATA_DIR/sourcedata/vocab.target"

ysyun

{
    "model_class": "AttentionSeq2Seq",
    "model_params": {
        "encoder.params": {
            "rnn_cell": {
                "dropout_input_keep_prob": 0.5,
                "num_layers": 2,
                "cell_params": {
                    "num_units": 512
                },

ysyun

Lifecycle:                      |    Update: 
Mounting and Unmounting         |    New Props or State
--------------------------------+-----------------------------------
                                |
getDefaultProps()               |    componentWillReceiveProps()*
                                |
getInitialState()               |    shouldComponentUpdate()
                                |
componentWillMount()            |    componentWillUpdate()
                                |