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Cancer_Multi_Layer.py
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Cancer_Multi_Layer.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):
A = (1/(1+np.exp(-z)))
cache = (z)
return A,cache
def tanh_(z):
A = np.tanh(z)
cache = (z)
return A,cache
def relu(Z):
A = np.maximum(0,Z)
cache = Z
return A, cache
def relu_backward(dA, cache):
Z = cache
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(dA, cache):
Z = cache
s = 1/(1+np.exp(-Z))
dZ = dA * s * (1-s)
return dZ
def tanh_backward(A,dA):
dZ = dA *(1-np.power(A,2))
return dZ
def layers(X,Y):
n_x = X.shape[0]
n_y = Y.shape[0]
return n_x,n_y
def initialize(layer_dims):
np.random.seed(2)
parameters = { }
L = len(layer_dims)
for i in range(1,L):
parameters["W"+str(i)] = np.random.randn(layer_dims[i],layer_dims[i-1])*0.01
parameters["b"+str(i)] = np.zeros((layer_dims[i],1))
return parameters
def linear_forward(A,W,b):
Z = np.dot(W,A)+b
cache = (A,W,b)
return Z,cache
def linear_activation_forward(A_prev,W,b,activation):
if activation == "sigmoid":
Z,linear_cache = linear_forward(A_prev, W, b)
A,activation_cache = sigmoid(Z)
elif activation == "relu":
Z,linear_cache = linear_forward(A_prev, W, b)
A,activation_cache = relu(Z)
cache = (linear_cache,activation_cache)
return A,cache
def L_model_forward(X,parameters):
caches = []
A = X
L= len(parameters) // 2
for l in range(1,L):
A_prev = A
A, cache =linear_activation_forward(A_prev, parameters['W'+str(l)],parameters['b'+str(l)] , activation="relu")
caches.append(cache)
AL ,cache = linear_activation_forward(A, parameters['W'+str(L)],parameters['b'+str(L)] , activation="sigmoid")
caches.append(cache)
return AL,caches
def compute_cost(AL,Y):
m = Y.shape[1]
logprobs = np.multiply(np.log(AL), Y) + np.multiply( (1 - Y),np.log(1 - AL))
cost = -np.sum(logprobs)/m
cost = np.squeeze(cost)
return cost
def linear_backward(dZ,cache):
A_prev, W, b = cache
m = A_prev.shape[1]
dW = (1/m)*np.dot(dZ,A_prev.T)
db = (1/m)*(np.sum(dZ,axis=1,keepdims=True))
dA_prev = np.dot(W.T,dZ)
return dA_prev, dW, db
def linear_activation_backward(dA,cache,activation):
linear_cache, activation_cache = cache
if activation == "relu":
dZ = relu_backward(dA,activation_cache)
dA_prev, dW, db = linear_backward(dZ,linear_cache)
elif activation == "sigmoid":
dZ = sigmoid_backward(dA,activation_cache)
dA_prev, dW, db = linear_backward(dZ,linear_cache)
return dA_prev, dW, db
def L_model_backward(AL,Y,caches):
grads = {}
L = len(caches) # the number of layers
m = AL.shape[1]
Y = Y.reshape(AL.shape) # after this line, Y is the same shape as AL
# Initializing the backpropagation
dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))
# Lth layer (SIGMOID -> LINEAR) gradients. Inputs: "dAL, current_cache". Outputs: "grads["dAL-1"], grads["dWL"], grads["dbL"]
current_cache = caches[L-1]
grads["dA" + str(L-1)], grads["dW" + str(L)], grads["db" + str(L)] =linear_activation_backward(dAL,current_cache,"sigmoid")
# Loop from l=L-2 to l=0
for l in reversed(range(L-1)):
# lth layer: (RELU -> LINEAR) gradients.
# Inputs: "grads["dA" + str(l + 1)], current_cache". Outputs: "grads["dA" + str(l)] , grads["dW" + str(l + 1)] , grads["db" + str(l + 1)]
current_cache = caches[l]
dA_prev_temp, dW_temp, db_temp = linear_activation_backward(grads["dA"+str(l+1)],current_cache,"relu")
grads["dA" + str(l)] = dA_prev_temp
grads["dW" + str(l + 1)] = dW_temp
grads["db" + str(l + 1)] = db_temp
return grads
def update_params(parameters ,grads,learning_rate):
L = len(parameters) //2
for l in range(L):
parameters["W" + str(l+1)] = parameters["W"+ str(l+1)] -learning_rate*grads["dW"+ str(l+1)]
parameters["b" + str(l+1)] = parameters["b"+ str(l+1)] -learning_rate*grads["db"+ str(l+1)]
return parameters
def predict(X, y, parameters):
m = X.shape[1]
n = len(parameters) // 2 # number of layers in the neural network
p = np.zeros((1,m))
# Forward propagation
probas, caches = L_model_forward(X, parameters)
# convert probas to 0/1 predictions
for i in range(0, probas.shape[1]):
if probas[0,i] > 0.5:
p[0,i] = 1
else:
p[0,i] = 0
print("Accuracy: " + str(np.sum((p == y)/m)))
return p
def L_layer_model(X, Y, layers_dims, learning_rate = 0.0075, num_iterations = 3000, print_cost=False):#lr was 0.009
np.random.seed(1)
costs = [] # keep track of cost
parameters = initialize(layers_dims)
for i in range(0, num_iterations):
# Forward propagation: [LINEAR -> RELU]*(L-1) -> LINEAR -> SIGMOID.
AL, caches =L_model_forward(X,parameters)
# Compute cost.
cost = compute_cost(AL,Y)
# Backward propagation.
grads = L_model_backward(AL,Y,caches)
# Update parameters.
parameters = update_params(parameters,grads,learning_rate)
# Print the cost every 100 training example
if print_cost and i % 100 == 0:
print ("Cost after iteration %i: %f" %(i, cost))
if print_cost and i % 100 == 0:
costs.append(cost)
# plot the cost
plt.plot(np.squeeze(costs))
plt.ylabel('cost')
plt.xlabel('iterations (per hundreds)')
plt.title("Learning rate =" + str(learning_rate))
plt.show()
return parameters
layers_dims = [X_train.shape[1], 23, 8, 1]
parameters = L_layer_model(X_train.T, y_train.T, layers_dims, num_iterations = 9000, print_cost = True)
#Predicting On X_test dataset
pred_train = predict(X_test.T,y_test.T,parameters)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, pred_train.T)