kavinvin

{
  "embeddings": [
    {
      "tensorName": "Lip Reading Triplet",
      "tensorShape": [
        1000,
        50
      ],
      "tensorPath": "https://kavinvin.s3-ap-southeast-1.amazonaws.com/triplet_feature_vector-1575173410.tsv",
      "metadataPath": "https://kavinvin.s3-ap-southeast-1.amazonaws.com/meta-1575122339.tsv"

kavinvin

kavinvin

{
  "embeddings": [
    {
      "tensorName": "My tensor",
      "tensorShape": [
        1000,
        50
      ],
      "tensorPath": "https://kavinvin.s3-ap-southeast-1.amazonaws.com/features-1575119175.tsv",
      "metadataPath": "https://gist.githubusercontent.com/kavinvin/ca037a4b3378e404ba94ac4f6505fcff/raw/590b84c433911c4b8673ccfe6c5b3e00eca63023/meta.tsv"

kavinvin

{
  "embeddings": [
    {
      "tensorName": "My tensor",
      "tensorShape": [
        1000,
        50
      ],
      "tensorPath": "https://raw.githubusercontent.com/.../tensors.tsv",
      "metadataPath": "https://raw.githubusercontent.com/.../optional.metadata.tsv",

kavinvin

from sklearn.preprocessing import Imputer
import numpy as np

imputer = Imputer(strategy='mean', axis=0)
imputer.fit(np.array([[4, 400, 30],
                      [5, 300, 25]]))
print(imputer.statistics_)
# [4.5     350     27.5]
              
print(imputer.transform([[4,        800,       np.nan],

kavinvin

import cv2
import numpy as np
from utils.transform import scale_img


def get_files():
    return ('{}.jpg'.format(n) for n in range(1, 20))


def get_images():

kavinvin

### Keybase proof

I hereby claim:

  * I am kavinvin on github.
  * I am kavinvin (https://keybase.io/kavinvin) on keybase.
  * I have a public key ASCBtdANrwMgRyAk873I14bHuBfRLdCaK2peAI8FArRXAwo

To claim this, I am signing this object: