Luo Ji recurrent neural network
import numpy as np
import matplotlib.pyplot as plt
import h5py#储存在h5文件中的数据集进行的交互包
import scipy
from PIL import Image
from scipy import ndimage
% matplotlib inline
Then the last chapter, we introduce the cat's picture and convert it into a matrix of n * 1, the division training set and test set of data stored in here
train_dataset = h5py.File('train_catvnoncat.h5', "r")
np.array(train_dataset)
#有3类数据
output:
array([‘list_classes’, ‘train_set_x’, ‘train_set_y’],
dtype=’<U12’)
lable output:
np.array(train_dataset['train_set_y'][:])
output:
array([0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0,
0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0,
0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0,
1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0,
0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1,
0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,
0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0,
0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0])
np.array(train_dataset['train_set_x'][:]).shape#209张图片，每个图片长宽为64 厚度为3
output：
(209, 64, 64, 3)
train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels
test_dataset = h5py.File('test_catvnoncat.h5', "r")
test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels
#索引0 not cat 索引1 cat
classes = np.array(test_dataset["list_classes"][:]) # the list of classes
#把矩阵shape成n*1
train_set_y = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
m_train = train_set_x_orig.shape[0]
m_test = test_set_x_orig.shape[0]
num_px = train_set_x_orig.shape[1]
train_set_x_flatten = train_set_x_orig.reshape(m_train, -1).T
test_set_x_flatten = test_set_x_orig.reshape(m_test, -1).T
train_set_x_flatten.T.shape#这个值是（209，64*64*3）=（209，12288）
Image color channels a total of three red, green, blue, and therefore the pixel value is actually a vector of three ranges from 0 to 255. The following standardized,
train_set_x = train_set_x_flatten/255
test_set_x = test_set_x_flatten/255
#这样我们就有了 train_set_x,train_set_y,test_set_x,test_set_y 这样的训练集和测试集
train_set_x.shape # This is the shape of the input, the corresponding shape of w should be (1 12288) may then w X-+ B This linear equation, note that not X * w + b. Recalling the specific reasons for matrix multiplication
def sigmoid(z):
s = 1.0/(1+np.exp(-z))
return s
#初始化参数
def initialize_with_zeros(dim):
w = np.zeros((dim,1))
b = 0
assert(w.shape == (dim,1))
assert(isinstance(b, float) or isinstance(b, int))
return w, b
#得到损失和梯度
def propagate(w,b,X,Y):
m = X.shape[1]
A = sigmoid(np.dot(w.T,X)+b)
cost = -(1.0/m)*np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))
dw = (1.0/m)*np.dot(X,(A-Y).T)
db = (1.0/m)*np.sum(A-Y)
assert(dw.shape == w.shape)
assert(db.dtype == float)
cost = np.squeeze(cost)
assert(cost.shape == ())
grads = {"dw": dw,
"db": db}
return grads, cost
#梯度下降法
def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):
costs = []
#循环多少次
for i in range(num_iterations):
grads, cost = propagate(w, b, X, Y)
dw = grads["dw"]
db = grads["db"]
#梯度下降得到新的参数
w = w - learning_rate*dw
b = b - learning_rate*db
#每100次添加一下误差值
if i % 100 == 0:
costs.append(cost)
#可以选择是否打印迭代后的误差值（每循环100次）
if print_cost and i % 100 == 0:
print ("Cost after iteration %i: %f" %(i, cost))
params = {"w": w,
"b": b}
grads = {"dw": dw,
"db": db}
return params, grads, costs
def predict(w,b,X):
m = X.shape[1]
Y_prediction = np.zeros((1,m))
w = w.reshape(X.shape[0],1)
#输出的shape是w.shape[0]*X.shape[1]
A = sigmoid(np.dot(w.T,X)+b)
for i in range(A.shape[1]):
if A[0,i]>0.5:
Y_prediction[0,i] = 1
else:
Y_prediction[0,i] = 0
### END CODE HERE ###
assert(Y_prediction.shape == (1, m))
return Y_prediction
#融合一下方法，学习率为0.5，循环2000次
def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False):
w, b = initialize_with_zeros(X_train.shape[0])
#得到参数，梯度，误差值
parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)
w = parameters["w"]
b = parameters["b"]
Y_prediction_test = predict(w, b, X_test)
Y_prediction_train = predict(w, b, X_train)
print("train accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))
print("test accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))
d = {"costs": costs,
"Y_prediction_test": Y_prediction_test,
"Y_prediction_train" : Y_prediction_train,
"w" : w,
"b" : b,
"learning_rate" : learning_rate,
"num_iterations": num_iterations}
return d
d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True)
output:
Cost after iteration 0: 0.693147
Cost after iteration 100: 0.584508
Cost after iteration 200: 0.466949
Cost after iteration 300: 0.376007
Cost after iteration 400: 0.331463
Cost after iteration 500: 0.303273
Cost after iteration 600: 0.279880
Cost after iteration 700: 0.260042
Cost after iteration 800: 0.242941
Cost after iteration 900: 0.228004
Cost after iteration 1000: 0.214820
Cost after iteration 1100: 0.203078
Cost after iteration 1200: 0.192544
Cost after iteration 1300: 0.183033
Cost after iteration 1400: 0.174399
Cost after iteration 1500: 0.166521
Cost after iteration 1600: 0.159305
Cost after iteration 1700: 0.152667
Cost after iteration 1800: 0.146542
Cost after iteration 1900: 0.140872
train accuracy: 99.04306220095694 %
test accuracy: 70.0 %
#我们找一张图片测试一下
index = 10
plt.imshow(test_set_x[:,index].reshape((64,64, 3)))
print ("y = " + str(test_set_y[0,index]) + ", you predicted that it is a \"" + classes[int(np.squeeze(d["Y_prediction_test"][0,index]))].decode("utf-8")+ "\" picture.")
#错误结果
index = 20
plt.imshow(test_set_x[:,index].reshape((64,64, 3)))
print ("y = " + str(test_set_y[0,index]) + ", you predicted that it is a \"" + classes[int(np.squeeze(d["Y_prediction_test"][0,index]))].decode("utf-8")+ "\" picture.")
#正确结果
#绘制成本曲线
costs = np.squeeze(d['costs'])
plt.plot(costs)
plt.ylabel('cost')
plt.xlabel('iterations (per hundreds)')
plt.title("Learning rate =" + str(d["learning_rate"]))
plt.show()
#学习率选择
learning_rates = [0.01, 0.001, 0.0001]
models = {}
for i in learning_rates:
print ("learning rate is: " + str(i))
models[str(i)] = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 1500, learning_rate = i, print_cost = False)
print ('\n' + "-------------------------------------------------------" + '\n')
for i in learning_rates:
plt.plot(np.squeeze(models[str(i)]["costs"]), label= str(models[str(i)]["learning_rate"]))
plt.ylabel('cost')
plt.xlabel('iterations')
legend = plt.legend(loc='upper center', shadow=True)
frame = legend.get_frame()
frame.set_facecolor('0.90')
plt.show()
output:
learning rate is: 0.01
train accuracy: 99.52153110047847 %
test accuracy: 68.0 %
learning rate is: 0.001
train accuracy: 88.99521531100478 %
test accuracy: 64.0 %
learning rate is: 0.0001
train accuracy: 68.42105263157895 %
test accuracy: 36.0 %
Summary: This is a simple shallow neural network can be helpful familiar with the network structure, specifically how to improve the effect will be behind a number of ways and ideas.