吴恩达-神经网络-week1-hw3
作者：互联网
# Ref：https://blog.csdn.net/u013733326/article/details/79702148
from testCases import *
from planar_utils import plot_decision_boundary, \
sigmoid, load_planar_dataset, load_extra_datasets
import sklearn
import sklearn.datasets
import sklearn.linear_model
import numpy as np
import matplotlib.pyplot as plt
import traceback
np.random.seed(1)
X, Y = load_planar_dataset()
plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral)
# plt.scatter(X[0, :], X[1, :], c=np.squeeze(Y), s=40, cmap=plt.cm.Spectral)
# --------------------dimension------------------------------------------
shape_X = X.shape
shape_Y = Y.shape
m = shape_Y[1] # the number of training set
print("X的维度: " + str(shape_X))
print("Y的维度: " + str(shape_Y))
print("数据集的数据个数： " + str(m) + "个")
# ------------------Logistics Reg----------------------------------------
print('======================Logistics Regression===============================')
LR_model = sklearn.linear_model.LogisticRegressionCV()
LR_model.fit(X.T, Y.T)
plt.figure()
plot_decision_boundary(lambda x: LR_model.predict(x), X, Y)
plt.title("Logistic Regression")
LR_predictions = LR_model.predict(X.T)
accur = (np.dot(Y, LR_predictions) + np.dot(1 - Y, 1 - LR_predictions)) / float(Y.size) * 100
print("逻辑回归准确性：%d" % float(accur)
+ '% ' + '(正确标记的数据点所占的百分比)') # 47%
# -------------------NN----------------------------------------------------
def layers_size(X, Y, h_layers=4):
"""
:param X: Input features
:param Y: Label
:param h_layers: # ( the hidden layers )
:return: the number of input layer, the hidden of input layer, the number of output layer
"""
n_x = X.shape[0] # input
n_h = h_layers # hidden layer
n_y = Y.shape[0] # output
return (n_x, n_h, n_y)
def initial_parameter(n_x, n_h, n_y):
"""
W[i] 的维度是 （n[i], n[i-1])
b[i] 的维度是 (n[i], 1)
:param n_x: the number of input layer
:param n_h: the number of hidden layer
:param n_y: the number of output layer
:return: 初始化的参数
"""
np.random.seed(2)
W1 = np.random.randn(n_h, n_x) * 0.02
b1 = np.zeros(shape=(n_h, 1))
W2 = np.random.randn(n_y, n_h) * 0.02
b2 = np.zeros(shape=(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))
paras = {
'W1': W1,
'b1': b1,
'W2': W2,
'b2': b2,
}
return paras
def forward_propagation(X, paras):
"""
two layers nn model, 计算前向传播， tanh 和 SIGMOID 激活函数
:param X:
:param paras:
:return:
"""
W1 = paras['W1']
b1 = paras['b1']
W2 = paras['W2']
b2 = paras['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)
def compute_cost(A2, Y):
"""
this function compute the cost,
:param A2: the activation of second layer
:param Y: label: 0(not cat); 1(cat)
:return: the cost of 2 layers nn model
"""
try:
m = Y.shape[1]
log_prob = np.multiply(np.log(A2), Y) + np.multiply((1 - Y), np.log(1 - A2))
cost = - np.sum(log_prob) / m
cost = float(np.squeeze(cost))
assert (isinstance(cost, float))
return cost
except:
print(traceback.print_exc())
def back_propagation(paras, cache, X, Y):
"""
the backward propagation of two layers nn model
:param paras: the parameters of nn model
:param cache: the activations of nn model
:param X: input features
:param Y: label
:return: gradient: dW2, db2, dW1, db1
"""
m = X.shape[1]
W1 = paras['W1']
W2 = paras['W2']
A1 = cache['A1']
A2 = cache['A2']
dZ2 = A2 - Y
dw2 = (1 / m) * np.dot(dZ2, A1.T)
db2 = (1 / m) * np.sum(dZ2, axis=1, keepdims=True)
dZ1 = np.multiply(np.dot(W2.T, dZ2), 1 - np.power(A1, 2))
dw1 = (1 / m) * np.dot(dZ1, X.T)
db1 = (1 / m) * np.sum(dZ1, axis=1, keepdims=True)
grads = {
'dW1': dw1,
'db1': db1,
'dW2': dw2,
'db2': db2
}
return grads
def update_params(params, grads, alpha=0.01):
"""
update the parameters of nn model according to the gradient of backward propagation
:param params: the parameters of the last iteration in the path of backward propagation
:param grads: the gradient of backward propagation
:param alpha: learning rate, default is 0.01
:return: the updated parameters
"""
W1, W2 = params['W1'], params['W2']
b1, b2 = params['b1'], params['b2']
dw1, dw2 = grads['dW1'], grads['dW2']
db1, db2 = grads['db1'], 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 nn_model(X, Y, n_h=4, num_iterations=10000, print_cost=False):
"""
two layers of nn model,
:param X: input features
:param Y: output, the label
:param n_h: the number of the hidden layer
:param iterations: the number of the iterations
:param print_cost: default is False
:return: the parameters of the nn model which have been trained
"""
np.random.seed(3)
n_x = layers_size(X, Y)[0]
n_y = layers_size(X, Y)[2]
params = initial_parameter(n_x, n_h, n_y)
for i in range(num_iterations):
A2, cache = forward_propagation(X, params)
cost = compute_cost(A2, Y)
grads = back_propagation(params, cache, X, Y)
params = update_params(params, grads, alpha=0.5)
if i % 1000 == 0 and print_cost:
print(f'第{i}次循环成本为：' + str(cost))
return params
def predict_y(X, params):
"""
:param X: input features
:param params:
:return: 0 or 1
"""
A2, cache = forward_propagation(X, params)
predict_y = np.round(A2)
return predict_y
print('======================Two layers nn model===============================')
parameters = nn_model(X, Y, n_h=4, num_iterations=10000, print_cost=True)
prediction_y = predict_y(X, parameters)
accu = float((np.dot(Y, prediction_y.T) + np.dot(1 - Y, 1 - prediction_y.T)) / float(Y.size) * 100)
# print('准确率: %.1f' % accu + "%")
print(f'Hidden layer: 的准确率为{accu}')
# figure
plt.figure()
plot_decision_boundary(lambda x: predict_y(x.T, parameters), X, Y)
plt.title('Decision Boundary for hidden layer size ' + str(4))
# Multi hidden layers
plt.figure(figsize=(16, 32))
hidde_layers = [1, 2, 4, 6, 10, 20, 50]
for i, n_h in enumerate(hidde_layers):
params = nn_model(X, Y, n_h, num_iterations=2000, print_cost=False)
prediction_y = predict_y(X, params)
plt.subplot(5, 2, i + 1)
plot_decision_boundary(lambda x: predict_y(x.T, parameters), X, Y)
plt.title(f'Decision Boundary for hidden layer size {n_h}')
accu = float((np.dot(Y, prediction_y.T) + np.dot(1 - Y, 1 - prediction_y.T)) / float(Y.size) * 100)
print(f'Hidden layer: {n_h}的准确率为{accu}')
plt.savefig('hidden layer.png')
if __name__ == "__main__":
pass
标签：吴恩达,print,param,np,shape,params,week1,model,hw3 来源： https://www.cnblogs.com/RankFan/p/15338318.html