# ---
# jupyter:
#   jupytext:
#     text_representation:
#       extension: .py
#       format_name: light
#       format_version: '1.5'
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab.research.google.com/github/andrewhylee/Laesa/blob/master/ImageRecognizer.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

# + id="eBA2J1btZ0cV" colab_type="code" outputId="72691cc0-912a-4fd8-ffb5-d9f6335f705d" colab={"base_uri": "https://localhost:8080/", "height": 122}
from google.colab import drive
drive.mount('/content/gdrive')

# + id="-a82FKy68qZf" colab_type="code" colab={}
# !wget -cq https://s3.amazonaws.com/content.udacity-data.com/courses/nd188/flower_data.zip
# !unzip -qq flower_data.zip

# + id="8wkFmRp_-atl" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 51} outputId="f53a2852-4757-42ac-9680-393c332e478d"
# %matplotlib inline
# %config InlineBackend.figure_format = 'retina'
import time
import json
import copy
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import PIL

# + id="UIyWfvpnh4RZ" colab_type="code" colab={}
from PIL import Image
from collections import OrderedDict
import torch
from torch import nn, optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
import torchvision
from torchvision import datasets, models, transforms
from torch.utils.data.sampler import SubsetRandomSampler
import torch.nn as nn
import torch.nn.functional as F

# + id="sG5gwt0Ui8Qi" colab_type="code" colab={}


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