#!/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 sys
import ktrain
from ktrain import vision as vis


# Download the **Dogs vs. Cats** dataset from [here](https://www.kaggle.com/c/dogs-vs-cats) and set DATADIR to the extracted folder.

# In[2]:


DATADIR = 'data/dogscats'
(train_data, val_data, preproc) = vis.images_from_folder(
                                              datadir=DATADIR,
                                              data_aug = vis.get_data_aug(horizontal_flip=True),
                                              train_test_names=['train', 'valid'], 
                                              target_size=(224,224), color_mode='rgb')
model = vis.image_classifier('pretrained_resnet50', train_data, val_data, freeze_layers=15)


# In[3]:


learner = ktrain.get_learner(model=model, train_data=train_data, val_data=val_data, 
                             workers=8, use_multiprocessing=False, batch_size=64)


# In[4]:


learner.fit_onecycle(1e-4, 3)


# In[5]:


learner.fit_onecycle(1e-4, 3)


# In[6]:


learner.fit_onecycle(1e-4, 1)


# In[7]:


learner.fit_onecycle(1e-4/5, 1)