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Cancer_Multi_Activation.py
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Cancer_Multi_Activation.py
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#Importing libraries
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
#Part-1 Data PreProcessing Phase
dataset = pd.read_csv('data.csv')
del dataset['Unnamed: 32']
X = dataset.iloc[:,2:]
Y = dataset.iloc[:,1]
#Label Encoding For M/B to 1/0 Respectively
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
encoder = LabelEncoder()
Y = encoder.fit_transform(Y)
Y = Y.reshape(Y.shape[0],1)
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)
#Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
#Part-2 Model Creation Phase
#Neural Network Initialization
def sigmoid(z):
return (1/(1+np.exp(-z)))
def relu(Z):
return np.maximum(0,Z)
def layers(X,Y):
n_x = X.shape[0]
n_y = Y.shape[0]
return n_x,n_y
def initialize(n_x,n_y,n_h):
np.random.seed(2)
W1 = np.random.randn(n_h,n_x)*0.01
b1 = np.zeros((n_h,1))
W2 = np.random.randn(n_y,n_h)*0.01
b2 = np.zeros((n_y,1))
parameters = {
"W1":W1,
"b1":b1,
"W2":W2,
"b2":b2 }
return parameters
def forward_prop(X,parameters):
W1 = parameters['W1']
b1 = parameters['b1']
W2 = parameters['W2']
b2 = parameters['b2']
Z1 = np.dot(W1,X) + b1
A1 = np.tanh(Z1)
Z2 = np.dot(W2,A1) + b2
A2 = sigmoid(Z2)
cache = {
"Z1":Z1,
"Z2":Z2,
"A1":A1,
"A2":A2 }
return A2,cache
def compute_cost(A2,Y,parameters):
m = Y.shape[1]
W1 = parameters["W1"]
W2 = parameters["W2"]
logprobs = np.multiply(np.log(A2), Y) + np.multiply( (1 - Y),np.log(1 - A2))
cost = -np.sum(logprobs)/m
cost = float(np.squeeze(cost))
return cost
def relu_backward(dA,Z):
dZ = np.array(dA, copy=True) # just converting dz to a correct object.
dZ[Z <= 0] = 0 # When z <= 0, you should set dz to 0 as well.
return dZ
def sigmoid_backward(A,dA):
dZ = dA * A * (1-A)
return dZ
def tanh_backward(A,dA):
dZ = dA *(1-np.power(A,2))
return dZ
def back_prop(parameters,cache,X,Y):
m = Y.shape[1]
W1 = parameters['W1']
W2 =parameters['W2']
A1 = cache['A1']
A2 = cache['A2']
Z1 = cache['Z1']
dA2 = - (np.divide(Y, A2) - np.divide(1 - Y, 1 - A2))
dZ2 = sigmoid_backward(A2,dA2)
dW2 = (1/m)*np.dot(dZ2,A1.T)
db2 = (1/m)*np.sum(dZ2,axis=1,keepdims=True)
dA1 = np.dot(W2.T,dZ2)
dZ1 = tanh_backward(A1, dA1)
dW1 = (1/m)*np.dot(dZ1,X.T)
db1 = (1/m)*np.sum(dZ1,axis=1,keepdims=True)
grads = {
"dW1":dW1,
"dW2":dW2,
"db1":db1,
"db2":db2,
}
return grads
def update_params(parameters ,grads,alpha):
W1 = parameters['W1']
b1 = parameters['b1']
W2 = parameters['W2']
b2 = parameters['b2']
dW1 = grads['dW1']
db1 = grads['db1']
dW2 = grads['dW2']
db2 = grads['db2']
W1 = W1 - alpha*dW1
W2 = W2 - alpha*dW2
b1 = b1 - alpha*db1
b2 = b2 - alpha*db2
parameters = {"W1": W1,
"b1": b1,
"W2": W2,
"b2": b2}
return parameters
def predict(parameters,X):
A2,cache = forward_prop(X,parameters)
prediction = np.round(A2)
return prediction
def model(X,y,n_h,num_iters,alpha,print_cost):
np.random.seed(3)
n_x = layers(X,y)[0]
n_y = layers(X, y)[1]
parameters = initialize(n_x,n_y,n_h)
W1 = parameters['W1']
b1 = parameters['b1']
W2 = parameters['W2']
b2 = parameters['b2']
costs = []
grads = {}
for i in range(0,num_iters):
A2,cache = forward_prop(X, parameters)
cost = compute_cost(A2,y, parameters)
grads = back_prop(parameters, cache, X,y)
if(i>20000):
alpha1 = (20000/i)*alpha
parameters = update_params(parameters, grads, alpha1)
else:
parameters = update_params(parameters, grads, alpha)
if i % 100 == 0:
costs.append(cost)
print("Cost after iteration "+ str(i) +"\tcost=>"+ str(cost))
if print_cost and i % 1000 == 0:
if i <= 20000:
print("Learning rate after iteration "+ str(i) +"\talpha=>"+ str(alpha))
else:
print("Learning rate after iteration "+ str(i) +"\talpha=>"+ str(alpha1))
plt.plot(costs)
plt.ylabel('cost')
plt.xlabel('iterations (per hundreds)')
plt.title("Learning rate =" + str(alpha))
plt.show()
return parameters
parameters = model(X_train.T, y_train.T, n_h=20, num_iters=5000, alpha=0.0075, print_cost=True)
#Predicitng On Test Set
y_pred = predict(parameters,X_test.T)
y_pred = y_pred.reshape(y_pred.shape[1],1)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)