Typesetting math: 0%
    
   
   
  
                                 Logout                       Control Panel             Logistic Regression with a Neural Network mindset v5     Last Checkpoint: 08/12/2018     (autosaved)                  
   
     
 
     



  

    
CodeMarkdownRaw NBConvertHeading-
CellToolbar
Submit Assignment
  











    

        
 
# Logistic Regression with a Neural Network mindset
​
Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize  cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning.
​
**Instructions:**
- Do not use loops (for/while) in your code, unless the instructions explicitly ask you to do so.
​
**You will learn to:**
- Build the general architecture of a learning algorithm, including:
    - Initializing parameters
    - Calculating the cost function and its gradient
    - Using an optimization algorithm (gradient descent) 
- Gather all three functions above into a main model function, in the right order.
Logistic Regression with a Neural Network mindset¶
Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize  cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning.
Instructions:

Do not use loops (for/while) in your code, unless the instructions explicitly ask you to do so.

You will learn to:

Build the general architecture of a learning algorithm, including:

Initializing parameters
Calculating the cost function and its gradient
Using an optimization algorithm (gradient descent) 


Gather all three functions above into a main model function, in the right order.

 
## 1 - Packages ##
​
First, let's run the cell below to import all the packages that you will need during this assignment. 
- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.
- [h5py](http://www.h5py.org) is a common package to interact with a dataset that is stored on an H5 file.
- [matplotlib](http://matplotlib.org) is a famous library to plot graphs in Python.
- [PIL](http://www.pythonware.com/products/pil/) and [scipy](https://www.scipy.org/) are used here to test your model with your own picture at the end.
1 - Packages¶
First, let's run the cell below to import all the packages that you will need during this assignment. 

numpy is the fundamental package for scientific computing with Python.
h5py is a common package to interact with a dataset that is stored on an H5 file.
matplotlib is a famous library to plot graphs in Python.
PIL and scipy are used here to test your model with your own picture at the end.

In [5]:
 
import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
from lr_utils import load_dataset
​
%matplotlib inline
/opt/conda/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
/opt/conda/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
. . .
 
​
## 2 - Overview of the Problem set ##
​
**Problem Statement**: You are given a dataset ("data.h5") containing:
    - a training set of m_train images labeled as cat (y=1) or non-cat (y=0)
    - a test set of m_test images labeled as cat or non-cat
    - each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB). Thus, each image is square (height = num_px) and (width = num_px).
​
You will build a simple image-recognition algorithm that can correctly classify pictures as cat or non-cat.
​
Let's get more familiar with the dataset. Load the data by running the following code.
2 - Overview of the Problem set¶
Problem Statement: You are given a dataset ("data.h5") containing:
- a training set of m_train images labeled as cat (y=1) or non-cat (y=0)
- a test set of m_test images labeled as cat or non-cat
- each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB). Thus, each image is square (height = num_px) and (width = num_px).
You will build a simple image-recognition algorithm that can correctly classify pictures as cat or non-cat.
Let's get more familiar with the dataset. Load the data by running the following code.
In [7]:
 
# Loading the data (cat/non-cat)
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()
. . .
 
We added "_orig" at the end of image datasets (train and test) because we are going to preprocess them. After preprocessing, we will end up with train_set_x and test_set_x (the labels train_set_y and test_set_y don't need any preprocessing).
​
Each line of your train_set_x_orig and test_set_x_orig is an array representing an image. You can visualize an example by running the following code. Feel free also to change the `index` value and re-run to see other images. 
We added "_orig" at the end of image datasets (train and test) because we are going to preprocess them. After preprocessing, we will end up with train_set_x and test_set_x (the labels train_set_y and test_set_y don't need any preprocessing).
Each line of your train_set_x_orig and test_set_x_orig is an array representing an image. You can visualize an example by running the following code. Feel free also to change the index value and re-run to see other images. 
In [6]:
 
# Example of a picture
index = 25
plt.imshow(train_set_x_orig[index])
print ("y = " + str(train_set_y[:, index]) + ", it's a '" + classes[np.squeeze(train_set_y[:, index])].decode("utf-8") +  "' picture.")
y = [1], it's a 'cat' picture.
. . .
 
Many software bugs in deep learning come from having matrix/vector dimensions that don't fit. If you can keep your matrix/vector dimensions straight you will go a long way toward eliminating many bugs. 
​
**Exercise:** Find the values for:
    - m_train (number of training examples)
    - m_test (number of test examples)
    - num_px (= height = width of a training image)
Remember that `train_set_x_orig` is a numpy-array of shape (m_train, num_px, num_px, 3). For instance, you can access `m_train` by writing `train_set_x_orig.shape[0]`.
Many software bugs in deep learning come from having matrix/vector dimensions that don't fit. If you can keep your matrix/vector dimensions straight you will go a long way toward eliminating many bugs. 
Exercise: Find the values for:
- m_train (number of training examples)
- m_test (number of test examples)
- num_px (= height = width of a training image)
Remember that train_set_x_orig is a numpy-array of shape (m_train, num_px, num_px, 3). For instance, you can access m_train by writing train_set_x_orig.shape[0].
In [8]:
&