ceshine

# Reference: http://pytorch.org/docs/master/_modules/torch/optim/sgd.html#SGD

class SGD(Optimizer):

    def __init__(self, params, lr=required, momentum=0, dampening=0,
                 weight_decay=0, nesterov=False):
        # ...

    def __setstate__(self, state):
        # ...

ceshine

""" Tested with Python 3.6 """
import re

import pandas as pd
import spacy
import joblib
from tqdm import tqdm

nlp = spacy.load('en')

ceshine

import pandas as pd
import joblib
from sklearn.model_selection import train_test_split


LABELS = ["toxic", "severe_toxic", "obscene",
          "threat", "insult", "identity_hate"]
EMPTY_ID = len(LABELS)

ceshine

import re
import logging

import numpy as np
import pandas as pd
import spacy
import torch
from torchtext import data

NLP = spacy.load('en')

ceshine

"""Improved dataset loader for Toxic Comment dataset from Kaggle
Tested against:
* Python 3.6
* Numpy 1.14.0
* Pandas 0.22.0
* PyTorch 0.4.0a0+f83ca63 (should be very close to 0.3.0)
* torchtext 0.2.1
* spacy 2.0.5
* joblib 0.11
"""

ceshine

library(checkpoint)
checkpoint("2018-02-25")
library(ggplot2)

# number of people
N <- 1000
# probability of event interception
P_E <- 0.075
# probability of lucky event
P_L <- 0.5

ceshine

import tensorflow as tf

class CausalConv1D(tf.layers.Conv1D):
    def __init__(self, filters,
               kernel_size,
               strides=1,
               dilation_rate=1,
               activation=None,
               use_bias=True,
               kernel_initializer=None,

ceshine

class TemporalBlock(tf.layers.Layer):
    def __init__(self, n_outputs, kernel_size, strides, dilation_rate, dropout=0.2, 
                 trainable=True, name=None, dtype=None, 
                 activity_regularizer=None, **kwargs):
        super(TemporalBlock, self).__init__(
            trainable=trainable, dtype=dtype,
            activity_regularizer=activity_regularizer,
            name=name, **kwargs
        )        
        self.dropout = dropout

ceshine

class TemporalConvNet(tf.layers.Layer):
    def __init__(self, num_channels, kernel_size=2, dropout=0.2,
                 trainable=True, name=None, dtype=None, 
                 activity_regularizer=None, **kwargs):
        super(TemporalConvNet, self).__init__(
            trainable=trainable, dtype=dtype,
            activity_regularizer=activity_regularizer,
            name=name, **kwargs
        )
        self.layers = []

ceshine

tf.reset_default_graph()
graph = tf.Graph()
with graph.as_default():
    tf.set_random_seed(10)
    # tf Graph input
    X = tf.placeholder("float", [None, timesteps, num_input])
    Y = tf.placeholder("float", [None, num_classes])
    is_training = tf.placeholder("bool")
    
    # Define weights