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// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

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# ================== Loading & Exploring JSON ================== #
# Load JSON: json_data
with open("a_movie.json") as json_file:
    json_data = json.load(json_file)

# Print each key-value pair in json_data
for k in json_data.keys():
    print(k + ': ', json_data[k])
    
# ================== Query API & decode JSON data ================== #

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input: "image"
input_dim: 1
input_dim: 3
input_dim: 1 # This value will be defined at runtime
input_dim: 1 # This value will be defined at runtime
layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "image"
  top: "conv1_1"

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>>> 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']

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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"

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

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

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

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

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#! /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