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Updates to Assignment

If you were working on the older version:

  • Please click on the "Coursera" icon in the top right to open up the folder directory.
  • Navigate to the folder: Week 3/ Planar data classification with one hidden layer. You can see your prior work in version 6b: "Planar data classification with one hidden layer v6b.ipynb"

List of bug fixes and enhancements

  • Clarifies that the classifier will learn to classify regions as either red or blue.
  • compute_cost function fixes np.squeeze by casting it as a float.
  • compute_cost instructions clarify the purpose of np.squeeze.
  • compute_cost clarifies that "parameters" parameter is not needed, but is kept in the function definition until the auto-grader is also updated.
  • nn_model removes extraction of parameter values, as the entire parameter dictionary is passed to the invoked functions.

Planar data classification with one hidden layer

Welcome to your week 3 programming assignment. It's time to build your first neural network, which will have a hidden layer. You will see a big difference between this model and the one you implemented using logistic regression.

You will learn how to:

  • Implement a 2-class classification neural network with a single hidden layer
  • Use units with a non-linear activation function, such as tanh
  • Compute the cross entropy loss
  • Implement forward and backward propagation

1 - Packages

Let's first import all the packages that you will need during this assignment.

  • numpy is the fundamental package for scientific computing with Python.
  • sklearn provides simple and efficient tools for data mining and data analysis.
  • matplotlib is a library for plotting graphs in Python.
  • testCases provides some test examples to assess the correctness of your functions
  • planar_utils provide various useful functions used in this assignment
# Package imports
import numpy as np
import matplotlib.pyplot as plt
from testCases_v2 import *
import sklearn
import sklearn.datasets
import sklearn.linear_model
from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets

%matplotlib inline

np.random.seed(1) # set a seed so that the results are consistent

2 - Dataset

First, let's get the dataset you will work on. The following code will load a "flower" 2-class dataset into variables X and Y.