dpressel

import tensorflow as tf
import sys
import time
import os

assert len(sys.argv) == 2, f"Usage: {sys.argv[0]} <directory>"
dirname = sys.argv[1]
print(dirname)
md_file = os.path.join(dirname, 'md.yml')
input_files = tf.io.gfile.glob(os.path.join(dirname, '*.tfrecord'))

dpressel

from baseline.reader import register_reader, SeqLabelReader
from baseline.vectorizers import register_vectorizer, create_vectorizer, Vectorizer
from collections import Counter
from baseline.data import DataFeed
from baseline.embeddings import register_embeddings
from baseline.tf.embeddings import TensorFlowEmbeddings
from baseline.utils import read_json
import numpy as np
import tensorflow as tf

dpressel

import torch
import torch.nn as nn

class Highway(nn.Module):

    def __init__(self, input_size):
        super(Highway, self).__init__()
        self.proj = nn.Linear(input_size, input_size)
        self.transform = nn.Linear(input_size, input_size)
        self.transform.bias.data.fill_(-2.0)

dpressel

package org.sgdtk.struct;

import java.util.ArrayList;
import java.util.List;

/**
 * Extracts a an array of character shingle features from a sequence
 *
 * @author dpressel
 */

dpressel

# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the BSD+Patents license found in the
# LICENSE file in the root directory of this source tree.

# -*- makefile -*-
# tested on CentOS 7, Ubuntu 16 and Ubuntu 14, see below to adjust flags to distribution.

dpressel

from baseline.tf.tfy import *
import json
import os
from google.protobuf import text_format
from tensorflow.python.platform import gfile
from baseline.model import Tagger
from tensorflow.contrib.layers import fully_connected, xavier_initializer


class RNNWordBoWCharTaggerModel(Tagger):

dpressel

from baseline.tf.tfy import *
import json
import os
from google.protobuf import text_format
from tensorflow.python.platform import gfile
from baseline.model import Tagger
from tensorflow.contrib.layers import fully_connected, xavier_initializer


class RNNWordTaggerModel(Tagger):

dpressel

import torch
import torch.nn as nn
from baseline.pytorch.torchy import classify_bt, append2seq
from baseline.model import Classifier


class NBowMaxModel(nn.Module, Classifier):

    def save(self, outname):
        print('saving %s' % outname)

dpressel

import torch
import torch.nn as nn
from baseline.pytorch.torchy import classify_bt, append2seq
from baseline.model import Classifier


class NBowModel(nn.Module, Classifier):

    def save(self, outname):
        print('saving %s' % outname)

dpressel

from baseline import classifier_repl
# You can use any of the implementations interchangeably!
from baseline.keras.classify import ConvModel
import argparse

"""
Example of loading a previously trained classifier (in Keras) and interacting
with it using a shell.  Your shell inputs (for the time being) should be
in the form the model tokenizes its training data.
"""