juanlp

library(shiny)
library(dplyr)
library(lubridate)

# Load libraries and functions needed to create SQLite databases.
library(RSQLite)
library(RSQLite.extfuns)
saveSQLite <- function(data, name){
  path <- dplyr:::db_location(filename=paste0(name, ".sqlite"))

juanlp

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.

It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data

In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/

juanlp

import numpy
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.


    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in

juanlp

from keras.applications.resnet50 import ResNet50, preprocess_input
from keras.preprocessing import image
import numpy as np


resnet = ResNet50(include_top=False)

def extract_features(img_paths, batch_size=64):
    """ This function extracts image features for each image in img_paths using ResNet50 bottleneck layer.
        Returned features is a numpy array with shape (len(img_paths), 2048).

juanlp

# Convert SE-ResNet-50 from Caffe to Keras
# Using the model from https://github.com/shicai/SENet-Caffe

import os
import numpy as np

# The caffe module needs to be on the Python path; we'll add it here explicitly.
import sys
caffe_root = "/path/to/caffe"
sys.path.insert(0, caffe_root + "python")

juanlp

from keras.callbacks import Callback
import keras.backend as K
import numpy as np

class SGDRScheduler(Callback):
    '''Cosine annealing learning rate scheduler with periodic restarts.

    # Usage
        ```python
            schedule = SGDRScheduler(min_lr=1e-5,