#!/usr/bin/env python
# coding: utf-8

# In[1]:


get_ipython().run_line_magic('reload_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().run_line_magic('matplotlib', 'inline')
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID";
os.environ["CUDA_VISIBLE_DEVICES"]="0" 
import ktrain
from ktrain import vision as vis


# In[2]:


get_ipython().system('wget --no-check-certificate      https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip      -O /tmp/cats_and_dogs_filtered.zip')


# In[3]:


import os
import zipfile

local_zip = '/tmp/cats_and_dogs_filtered.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('/tmp')
zip_ref.close()


# In[4]:


(trn, val, preproc) = vis.images_from_folder(
                                              datadir='/tmp/cats_and_dogs_filtered',
                                              data_aug = vis.get_data_aug(horizontal_flip=True),
                                              train_test_names=['train', 'validation'])


# In[5]:


model = vis.image_classifier('pretrained_mobilenet', trn, val, freeze_layers=15)
learner = ktrain.get_learner(model=model, train_data=trn, val_data=val, 
                             workers=8, use_multiprocessing=False, batch_size=64)


# In[6]:


learner.lr_find(max_epochs=3, show_plot=True)


# In[7]:


learner.fit_onecycle(1e-4, 4)


# In[8]:


learner.validate(class_names=preproc.get_classes())


# In[9]:


p = ktrain.get_predictor(learner.model, preproc)


# In[10]:


p.predict_filename('/tmp/cats_and_dogs_filtered/validation/cats/cat.2001.jpg')


# In[11]:


p.predict_filename('/tmp/cats_and_dogs_filtered/validation/cats/cat.2001.jpg', return_proba=True)


# In[12]:


p.preproc.get_classes()


# In[13]:


p.save('/tmp/mycatsdogs')


# In[14]:


p = ktrain.load_predictor('/tmp/mycatsdogs')


# In[15]:


p.predict_filename('/tmp/cats_and_dogs_filtered/validation/dogs/dog.2001.jpg')


# In[16]:


vis.show_image('/tmp/cats_and_dogs_filtered/validation/dogs/dog.2001.jpg')


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