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

# # Project 2
# ## Image Classification with CNN
# -------------------------------
# 

# ## 1. Direct prediction
#    * Download the dataset birds from http://www-cvr.ai.uiuc.edu/ponce_grp/data/.
#    * Use [Keras](https://keras.io/) and a CNN from Keras [Applications](https://keras.io/applications/) pretrained on ImageNet, to classify the images in the birds dataset. Construct a confusion matrix that relates the bird classes with the 10 most frequent classes from ImageNet predicted by the model.
#    * Discuss the results.
#    
# ## 2. Transfer learning
#    * Use the pre-trained CNN model as a feature extractor. Create a new model that replaces the top part of the pretrained CNN with two layers of 256 and 6 neurons respectively.
#    * Train the model with the training images from the bird dataset. 
#    * Evaluate the performance over the test dataset reporting the results in a confusion matrix. Discuss the results. 
#    
# ## 3. Fine tuning
#    * Repeat the experiment from the last question, but this time allow all the layers to be trained. 
#    * Compare and discuss the results.

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