"""
Created on Sun Sep 24 09:09:10 2017
@author: Jay
"""
import numpy as np
import matplotlib.pyplot as plt
from testCases import *
import sklearn
import sklearn.datasets
import sklearn.linear_model
from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets
np.random.seed(1)
X, Y = load_planar_dataset()
'''
clf=sklearn.linear_model.LogisticRegressionCV();
clf.fit(X.T,Y.T);
plot_decision_boundary(lambda x:clf.predict(x),X,Y)
plt.title('Logistic Regression')
LR_predictions=clf.predict(X.T)
print('Accuracy of logistic regression: %d' %float((np.dot(Y,LR_predictions)+\
np.dot(1-Y,1-LR_predictions))/float(Y.size)*100)+'%')
'''
def layer_sizes(X,Y):
n_x=X.shape[0]
n_h=4
n_y=Y.shape[0]
return(n_x,n_h,n_y)
def initialize_parameters(n_x, n_h, n_y):
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))
assert (W1.shape == (n_h, n_x))
assert (b1.shape == (n_h, 1))
assert (W2.shape == (n_y, n_h))
assert (b2.shape == (n_y, 1))
parameters={'W1':W1,
'b1':b1,
'W2':W2,
'b2':b2}
return parameters
def forward_propagation(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)
assert(A2.shape==(1,X.shape[1]))
cache={'Z1':Z1,
'A1':A1,
'Z2':Z2,
'A2':A2}
return A2,cache
'''X_assess,parameters=forward_propagation_test_case()
A2,cache=forward_propagation(X_assess,parameters)
print(np.mean(cache['Z1']) ,np.mean(cache['A1']),np.mean(cache['Z2']),np.mean(cache['A2']))'''
def compute_cost(A2,Y,parameters):
m=Y.shape[1]
W1=parameters['W1']
W2=parameters['W2']
cost =-(float(np.dot(np.log(A2),Y.T))+np.dot(np.log(1.-A2),(1.-Y).T))/m
cost = np.squeeze(cost)
return cost
def backward_propagation(parameters,cache,X,Y):
m=X.shape[1]
W1=parameters['W1']
W2=parameters['W2']
A1=cache['A1']
A2=cache['A2']
dZ2=A2-Y
dW2=np.dot(dZ2,A1.T)/m
db2=np.sum(dZ2,axis=1,keepdims=True)/m
dZ1=np.dot(W2.T,dZ2)*(1-A1**2)
dW1=np.dot(dZ1,X.T)/m
db1=np.sum(dZ1,axis=1,keepdims=True)/m
grads={'dW1':dW1,
'db1':db1,
'dW2':dW2,
'db2':db2}
return grads
parameters, cache, X_assess, Y_assess = backward_propagation_test_case()
'''
grads = backward_propagation(parameters, cache, X_assess, Y_assess)
print ("dW1 = "+ str(grads["dW1"]))
print ("db1 = "+ str(grads["db1"]))
print ("dW2 = "+ str(grads["dW2"]))
print ("db2 = "+ str(grads["db2"]))
'''
def update_parameters(parameters, grads, learning_rate = 1.2):
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 - learning_rate * dW1
b1 = b1 - learning_rate * db1
W2 = W2 - learning_rate * dW2
b2 = b2 - learning_rate * db2
parameters = {"W1": W1,
"b1": b1,
"W2": W2,
"b2": b2}
return parameters
def nn_model(X,Y,n_h,num_iterations=10000,print_cost=False):
np.random.seed(3)
n_x=layer_sizes(X,Y)[0]
n_y=layer_sizes(X,Y)[2]
parameters=initialize_parameters(n_x,n_h,n_y)
W1 = parameters['W1']
b1 = parameters['b1']
W2 = parameters['W2']
b2 = parameters['b2']
for i in range(0, num_iterations):
A2, cache = forward_propagation(X, parameters)
cost = compute_cost(A2, Y, parameters)
grads = backward_propagation(parameters, cache, X, Y)
parameters = update_parameters(parameters, grads)
if i % 1000 == 0:
print ("Cost after iteration %i: %f" %(i, cost))
return parameters
'''
X_assess, Y_assess = nn_model_test_case()
parameters = nn_model(X_assess, Y_assess, 4, num_iterations=10000, print_cost=False)
print("W1 = " + str(parameters["W1"]))
print("b1 = " + str(parameters["b1"]))
print("W2 = " + str(parameters["W2"]))
print("b2 = " + str(parameters["b2"]))
'''
def predict(parameters,X):
A2,cache=forward_propagation(X,parameters)
predictions=np.array([0 if i<=0.5 else 1 for i in np.squeeze(A2)])
return predictions
'''
parameters, X_assess = predict_test_case()
predictions = predict(parameters, X_assess)
print("predictions mean = " + str(np.mean(predictions)))
'''
'''
parameters = nn_model(X, Y, n_h = 4, num_iterations = 20000, print_cost=True)
plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)
plt.title("Decision Boundary for hidden layer size " + str(4))
predictions = predict(parameters, X)
print ('Accuracy: %d' % float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100) + '%')
plt.figure(figsize=(16, 32))
hidden_layer_sizes = [1, 2, 3, 4, 5, 20, 50]
for i, n_h in enumerate(hidden_layer_sizes): #枚举
plt.subplot(5, 2, i+1)
plt.title('Hidden Layer of size %d' % n_h)
parameters = nn_model(X, Y, n_h, num_iterations = 5000)
plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)
predictions = predict(parameters, X)
accuracy = float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100)
print ("Accuracy for {} hidden units: {} %".format(n_h, accuracy))
'''
noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets()
datasets = {"noisy_circles": noisy_circles,
"noisy_moons": noisy_moons,
"blobs": blobs,
"gaussian_quantiles": gaussian_quantiles}
for i,j in datasets.items():
dataset = "j"
X, Y = j
X,Y=X.T,Y.reshape(1,Y.shape[0])
if dataset=='blobs':
Y=Y%2
parameters=nn_model(X,Y,5,num_iterations=10000)
predictions=predict(parameters,X)
accuracy=float((np.dot(Y,predictions.T)+np.dot(1.-Y,1.-predictions.T))/Y.size*100)
print('accuracy for {} is:{}%'.format(i,accuracy))
print('***************************')
