vinhkhuc

# Source: https://course.spacy.io/

# =========================== Chapter 1 =========================== #
# Import the English language class
import spacy
from spacy.lang.en import English
from spacy.matcher import Matcher

# Create the nlp object
nlp = English()

vinhkhuc

https://projector.tensorflow.org/?config=https://gist.githubusercontent.com/vinhkhuc/5ca429d4c38900d9eb24a8c245593c75/raw/2f9fe9fb9ae824d030ff7dd6eb941e6dfef6544a/qc-question-embeddings-projector-config.json

vinhkhuc

{
  "embeddings": [
    {
      "tensorName": "QC Question Embeddings Using TF Sentence Embedding Encoder Large 3",
      "tensorShape": [
        5451,
        300
      ],
      "tensorPath": "https://gist.githubusercontent.com/vinhkhuc/3806dc8ef988d3c10ecfa6310de1943f/raw/738c1d07563612df2662a0aedc931a89cb64458b/qc-question-embeddings.tsv",
      "metadataPath": "https://gist.githubusercontent.com/vinhkhuc/1f9fc9ad322c152ebe607e9bb5d7da55/raw/1518504769d8989f017caa171313991ae43f7e75/qc-embed-metadata.tsv"

vinhkhuc

vinhkhuc

vinhkhuc

# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
# where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
# Similarly, for h * W_2 + b_2
import tensorflow as tf
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

vinhkhuc

"""
Vanilla Char-RNN using TensorFlow by Vinh Khuc (@knvinh).
Adapted from Karpathy's min-char-rnn.py
https://gist.github.com/karpathy/d4dee566867f8291f086
Requires tensorflow>=1.0
BSD License
"""
import random
import numpy as np
import tensorflow as tf

vinhkhuc

# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
# Requires: numpy, sklearn, theano

# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1' 
# where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's. 
# Similarly, for h * W_2 + b_2
import theano
from theano import tensor as T
import numpy as np
from sklearn import datasets

vinhkhuc

from theano import tensor as T, function
x  = T.dscalar('x')
y  = x ** 2
dy = T.grad(cost=y, wrt=x) # Preparing symbolic gradient
df  = function(inputs=[x], outputs=dy)
print(df(4)) # Output: 8

vinhkhuc

"""
Code to replicate Ron Kohavi's cross-validation experiment on the Iris data set.
"""
from sklearn import datasets, svm
from sklearn.cross_validation import cross_val_score, KFold, LeavePOut
import matplotlib.pyplot as plt

output_file = "cross-validation-experiment-iris.png"

iris = datasets.load_iris()