ktl014

# AlexNet
name: "AlexNet"
layer {
  name: "train-data"
  type: "Data"
  top: "data"
  top: "label"
  transform_param {
    mirror: false
    crop_size: 227

ktl014

from sklearn.kernel_approximation import RBFSampler
from sklearn.decomposition import PCA

kernel_svm = svm.SVC(gamma=.2)
linear_svm = svm.LinearSVC()
feature_map_fourier = RBFSampler(gamma=.2, random_state=SEED)
feature_map_nystroem = Nystroem(gamma=.2, random_state=SEED)
fourier_approx_svm = pipeline.Pipeline([("feature_map", feature_map_fourier),
                                        ("svm", svm.LinearSVC())])

ktl014

# Importing data from csv file
csv_filename = './tweets.csv'
dataset = tf.contrib.data.make_csv_dataset(csv_filename, batch_size=32)
dataset = dataset.shuffle(buffer_size=100)
iter = dataset.make_one_shot_iterator()
next = iter.get_next()
features, labels = next['text'], next['sentiment']

with tf.Session() as sess:
	sess.run([features, labels])

ktl014

#! /usr/bin/env python
from __future__ import absolute_import, division, print_function

import os
import matplotlib.pyplot as plt

import tensorflow as tf
import keras
import tensorflow.contrib.eager as tfe

ktl014

from urllib.request import urlopen
html = urlopen("http://www.google.com/")
print(html)

ktl014

name: "ResNet-50"
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {

ktl014

name: "AlexNet"
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
#  transform_param {

ktl014

name: "VGG_ILSVRC_16_layers"
layer {
  name: "data"
  type: "Data"
  include {
    phase: TRAIN
  }
 transform_param {
    crop_size: 224
    mean_value: 104

ktl014

name: "VGG_ILSVRC_19_layers"
input: "data"
input_dim: 10
input_dim: 3
input_dim: 224
input_dim: 224
layers {
  bottom: "data"
  top: "conv1_1"
  name: "conv1_1"

ktl014

>>> class Library(object):
...     def __init__(self):                                                     
...             self.books = { 'title' : object, 'title2' : object, 'title3' : object, }
...     def __getitem__(self, i):
...             return self.books[i]
...     def __iter__(self):
...             return self.books.itervalues()
... 
>>> library = Library()
>>> library['title']