# coding: utf-8

# In[1]:

from theano.sandbox import cuda
cuda.use('gpu3')


# In[2]:

get_ipython().magic(u'matplotlib inline')
import utils; reload(utils)
from utils import *
from __future__ import division, print_function


# In[3]:

#path = "data/fish/sample/"
path = "data/fish/"
batch_size=64


# In[4]:

batches = get_batches(path+'train', batch_size=batch_size)
val_batches = get_batches(path+'valid', batch_size=batch_size*2, shuffle=False)


# ## Setup dirs

# In[ ]:

get_ipython().magic(u'cd data/fish')

get_ipython().magic(u'cd train')

get_ipython().magic(u'mkdir ../valid')


# In[ ]:

g = glob('*')
for d in g: os.mkdir('../valid/'+d)

g = glob('*/*.jpg')
shuf = np.random.permutation(g)
for i in range(500): os.rename(shuf[i], '../valid/' + shuf[i])


# In[ ]:

get_ipython().magic(u'mkdir ../sample')
get_ipython().magic(u'mkdir ../sample/train')
get_ipython().magic(u'mkdir ../sample/valid')


# In[ ]:

from shutil import copyfile

g = glob('*')
for d in g:
    os.mkdir('../sample/train/'+d)
    os.mkdir('../sample/valid/'+d)


# In[ ]:

g = glob('*/*.jpg')
shuf = np.random.permutation(g)
for i in range(400): copyfile(shuf[i], '../sample/train/' + shuf[i])

get_ipython().magic(u'cd ../valid')

g = glob('*/*.jpg')
shuf = np.random.permutation(g)
for i in range(200): copyfile(shuf[i], '../sample/valid/' + shuf[i])

get_ipython().magic(u'cd ..')


# In[ ]:

get_ipython().magic(u'mkdir results')

get_ipython().magic(u'mkdir sample/results')

get_ipython().magic(u'cd ../..')


# ## Initial model

# In[5]:

from vgg16 import Vgg16
model = vgg_ft(8)