E20- Neural Networks in Keras

Use keras framework to solve the below exercises.



In [1]:

    
import numpy as np
import keras 
import pandas as pd
import matplotlib.pyplot as plt









    



Using TensorFlow backend.

20.1 Predicting Student Admissions with Neural Networks

In this notebook, we predict student admissions to graduate schools based on six pieces of data:

GRE Scores (Test)
TOEFL Scores (Test)
University Ranking (1-5)
Statement of Purpose (SOP) and Letter of Recommendation Strength ( out of 5 )
Undergraduate GPA Scores (Grades)
Research Experience ( either 0 or 1 )

Exercise: Design and train a shallow neural network to predict the chance of Admission for each entry. Choose the number of hidden layer and neurons that minimizes the error.



In [2]:

    
# Import dataset

data = pd.read_csv('https://raw.githubusercontent.com/albahnsen/PracticalMachineLearningClass/master/datasets/universityGraduateAdmissions.csv', index_col=0)
data.head()









    Out[2]:







  
    
      
      GRE Score
      TOEFL Score
      University Rating
      SOP
      LOR
      CGPA
      Research
      Chance of Admit
    
    
      Serial No.
      
      
      
      
      
      
      
      
    
  
  
    
      1
      337
      118
      4
      4.5
      4.5
      9.65
      1
      0.92
    
    
      2
      324
      107
      4
      4.0
      4.5
      8.87
      1
      0.76
    
    
      3
      316
      104
      3
      3.0
      3.5
      8.00
      1
      0.72
    
    
      4
      322
      110
      3
      3.5
      2.5
      8.67
      1
      0.80
    
    
      5
      314
      103
      2
      2.0
      3.0
      8.21
      0
      0.65



In [3]:

    
data.columns









    Out[3]:





Index(['GRE Score', 'TOEFL Score', 'University Rating', 'SOP', 'LOR ', 'CGPA',
       'Research', 'Chance of Admit '],
      dtype='object')



In [4]:

    
X = data.drop(data.columns[-1], axis=1)
Y = data[data.columns[-1]]



In [5]:

    
from sklearn.model_selection import train_test_split

xTrain, xTest, yTrain, yTest = train_test_split(X,Y,test_size=0.3, random_state=22)



In [6]:

    
from keras import initializers
from keras import optimizers
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout



In [ ]:

20.2 Decision Boundary -- Moons Dataset

Exercise: Use keras framework to find a decision boundary for point in the make_moons.



In [7]:

    
# Create moons dataset.

from sklearn.datasets.samples_generator import make_moons

x_train, y_train = make_moons(n_samples=1000, noise= 0.2, random_state=3)
plt.figure(figsize=(12, 8))
plt.scatter(x_train[:, 0], x_train[:,1], c=y_train, s=40, cmap=plt.cm.Spectral);

Hint: Use the next function to plt the decision boundary,



In [ ]:

    
model = 'Sequential neural network in keras'

def plot_decision_region(model, X, pred_fun):
    min_x = np.min(X[:, 0])
    max_x = np.max(X[:, 0])
    min_y = np.min(X[:, 1])
    max_y = np.max(X[:, 1])
    min_x = min_x - (max_x - min_x) * 0.05
    max_x = max_x + (max_x - min_x) * 0.05
    min_y = min_y - (max_y - min_y) * 0.05
    max_y = max_y + (max_y - min_y) * 0.05
    x_vals = np.linspace(min_x, max_x, 30)
    y_vals = np.linspace(min_y, max_y, 30)
    XX, YY = np.meshgrid(x_vals, y_vals)
    grid_r, grid_c = XX.shape
    ZZ = np.zeros((grid_r, grid_c))
    for i in range(grid_r):
        for j in range(grid_c):
            '''
            Here 'model' is the neural network you previous trained.
            '''
            ZZ[i, j] = pred_fun(model, XX[i, j], YY[i, j])
    plt.contourf(XX, YY, ZZ, 30, cmap = pl.cm.coolwarm, vmin= 0, vmax=1)
    plt.colorbar()
    plt.xlabel("x")
    plt.ylabel("y")
    
def pred_fun(model,x1, x2):
    '''
    Here 'model' is the neural network you previous trained.
    '''
    xval = np.array([[x1, x2]])
    return model.predict(xval)[0, 0]

plt.figure(figsize = (8,16/3))    
'''
Here 'model' is the neural network you previous trained.
'''
plot_decision_region(model, x_train, pred_fun)
plot_data(x_train, y_train)



In [ ]:

	GRE Score	TOEFL Score	University Rating	SOP	LOR	CGPA	Research	Chance of Admit
Serial No.
1	337	118	4	4.5	4.5	9.65	1	0.92
2	324	107	4	4.0	4.5	8.87	1	0.76
3	316	104	3	3.0	3.5	8.00	1	0.72
4	322	110	3	3.5	2.5	8.67	1	0.80
5	314	103	2	2.0	3.0	8.21	0	0.65