构建了三层神经网络来验证正则化和dropout对防止过拟合的作用。
首先看数据集，reg_utils.py包含产生数据集函数，前向传播，计算损失值等，代码如下：import numpy as np
import matplotlib.pyplot as plt
import h5py
import sklearn
import sklearn.datasets
import sklearn.linear_model
import scipy.io
def sigmoid(x):
"""
Compute the sigmoid of x
Arguments:
x -- A scalar or numpy array of any size.
Return:
s -- sigmoid(x)
"""
s = 1/(1+np.exp(-x))
return s
def relu(x):
"""
Compute the relu of x
Arguments:
x -- A scalar or numpy array of any size.
Return:
s -- relu(x)
"""
s = np.maximum(0,x)
return s
def load_planar_dataset(seed):
np.random.seed(seed)
m = 400 # number of examples
N = int(m/2) # number of points per class
D = 2 # dimensionality
X = np.zeros((m,D)) # data matrix where each row is a single example
Y = np.zeros((m,1), dtype='uint8') # labels vector (0 for red, 1 for blue)
a = 4 # maximum ray of the flower
for j in range(2):
ix = range(N*j,N*(j+1))
t = np.linspace(j*3.12,(j+1)*3.12,N) + np.random.randn(N)*0.2 # theta
r = a*np.sin(4*t) + np.random.randn(N)*0.2 # radius
X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
Y[ix] = j
X = X.T
Y = Y.T
return X, Y
def initialize_parameters(layer_dims):
"""
Arguments:
layer_dims -- python array (list) containing the dimensions of each layer in our network
Returns:
parameters -- python dictionary containing your parameters "W1", "b1", ..., "WL", "bL":
W1 -- weight matrix of shape (layer_dims[l], layer_dims[l-1])
b1 -- bias vector of shape (layer_dims[l], 1)
Wl -- weight matrix of shape (layer_dims[l-1], layer_dims[l])
bl -- bias vector of shape (1, layer_dims[l])
Tips:
- For example: the layer_dims for the "Planar Data classification model" would have been [2,2,1].
This means W1's shape was (2,2), b1 was (1,2), W2 was (2,1) and b2 was (1,1). Now you have to generalize it!
- In the for loop, use parameters['W' + str(l)] to access Wl, where l is the iterative integer.
"""
np.random.seed(3)
parameters = {}
L = len(layer_dims) # number of layers in the network
for l in range(1, L):
parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l-1]) / np.sqrt(layer_dims[l-1])
parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))
assert(parameters['W' + str(l)].shape == layer_dims[l], layer_dims[l-1])
assert(parameters['W' + str(l)].shape == layer_dims[l], 1)
return parameters
def forward_propagation(X, parameters):
"""
Implements the forward propagation (and computes the loss) presented in Figure 2.
Arguments:
X -- input dataset, of shape (input size, number of examples)
parameters -- python dictionary containing your parameters "W1", "b1", "W2", "b2", "W3", "b3":
W1 -- weight matrix of shape ()
b1 -- bias vector of shape ()
W2 -- weight matrix of shape ()
b2 -- bias vector of shape ()
W3 -- weight matrix of shape ()
b3 -- bias vector of shape ()
Returns:
loss -- the loss function (vanilla logistic loss)
"""
# retrieve parameters
W1 = parameters["W1"]
b1 = parameters["b1"]
W2 = parameters["W2"]
b2 = parameters["b2"]
W3 = parameters["W3"]
b3 = parameters["b3"]
# LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID
Z1 = np.dot(W1, X) + b1
A1 = relu(Z1)
Z2 = np.dot(W2, A1) + b2
A2 = relu(Z2)
Z3 = np.dot(W3, A2) + b3
A3 = sigmoid(Z3)
cache = (Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3)
return A3, cache
def backward_propagation(X, Y, cache):
"""
Implement the backward propagation presented in figure 2.
Arguments:
X -- input dataset, of shape (input size, number of examples)
Y -- true "label" vector (containing 0 if cat, 1 if non-cat)
cache -- cache output from forward_propagation()
Returns:
gradients -- A dictionary with the gradients with respect to each parameter, activation and pre-activation variables
"""
m = X.shape[1]
(Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3) = cache
dZ3 = A3 - Y
dW3 = 1./m * np.dot(dZ3, A2.T)
db3 = 1./m * np.sum(dZ3, axis=1, keepdims = True)
dA2 = np.dot(W3.T, dZ3)
dZ2 = np.multiply(dA2, np.int64(A2 > 0))
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 = np.multiply(dA1, np.int64(A1 > 0))
dW1 = 1./m * np.dot(dZ1, X.T)
db1 = 1./m * np.sum(dZ1, axis=1, keepdims = True)
gradients = {"dZ3": dZ3, "dW3": dW3, "db3": db3,
"dA2": dA2, "dZ2": dZ2, "dW2": dW2, "db2": db2,
"dA1": dA1, "dZ1": dZ1, "dW1": dW1, "db1": db1}
return gradients
def update_parameters(parameters, grads, learning_rate):
"""
Update parameters using gradient descent
Arguments:
parameters -- python dictionary containing your parameters:
parameters['W' + str(i)] = Wi
parameters['b' + str(i)] = bi
grads -- python dictionary containing your gradients for each parameters:
grads['dW' + str(i)] = dWi
grads['db' + str(i)] = dbi
learning_rate -- the learning rate, scalar.
Returns:
parameters -- python dictionary containing your updated parameters
"""
n = len(parameters) // 2 # number of layers in the neural networks
# Update rule for each parameter
for k in range(n):
parameters["W" + str(k+1)] = parameters["W" + str(k+1)] - learning_rate * grads["dW" + str(k+1)]
parameters["b" + str(k+1)] = parameters["b" + str(k+1)] - learning_rate * grads["db" + str(k+1)]
return parameters
def predict(X, y, parameters):
"""
This function is used to predict the results of a n-layer neural network.
Arguments:
X -- data set of examples you would like to label
parameters -- parameters of the trained model
Returns:
p -- predictions for the given dataset X
"""
m = X.shape[1]
p = np.zeros((1,m), dtype = np.int)
# Forward propagation
a3, caches = forward_propagation(X, parameters)
# convert probas to 0/1 predictions
for i in range(0, a3.shape[1]):
if a3[0,i] > 0.5:
p[0,i] = 1
else:
p[0,i] = 0
# print results
#print ("predictions: " + str(p[0,:]))
#print ("true labels: " + str(y[0,:]))
print("Accuracy: " + str(np.mean((p[0,:] == y[0,:]))))
return p
def compute_cost(a3, Y):
"""
Implement the cost function
Arguments:
a3 -- post-activation, output of forward propagation
Y -- "true" labels vector, same shape as a3
Returns:
cost - value of the cost function
"""
m = Y.shape[1]
logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)
cost = 1./m * np.nansum(logprobs)
return cost
def load_dataset():
train_dataset = h5py.File('datasets/train_catvnoncat.h5', "r")
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('datasets/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
classes = np.array(test_dataset["list_classes"][:]) # the list of classes
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]))
train_set_x_orig = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T
test_set_x_orig = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T
train_set_x = train_set_x_orig/255
test_set_x = test_set_x_orig/255
return train_set_x, train_set_y, test_set_x, test_set_y, classes
def predict_dec(parameters, X):
"""
Used for plotting decision boundary.
Arguments:
parameters -- python dictionary containing your parameters
X -- input data of size (m, K)
Returns
predictions -- vector of predictions of our model (red: 0 / blue: 1)
"""
# Predict using forward propagation and a classification threshold of 0.5
a3, cache = forward_propagation(X, parameters)
predictions = (a3>0.5)
return predictions
def load_planar_dataset(randomness, seed):
np.random.seed(seed)
m = 50
N = int(m/2) # number of points per class
D = 2 # dimensionality
X = np.zeros((m,D)) # data matrix where each row is a single example
Y = np.zeros((m,1), dtype='uint8') # labels vector (0 for red, 1 for blue)
a = 2 # maximum ray of the flower
for j in range(2):
ix = range(N*j,N*(j+1))
if j == 0:
t = np.linspace(j, 4*3.1415*(j+1),N) #+ np.random.randn(N)*randomness # theta
r = 0.3*np.square(t) + np.random.randn(N)*randomness # radius
if j == 1:
t = np.linspace(j, 2*3.1415*(j+1),N) #+ np.random.randn(N)*randomness # theta
r = 0.2*np.square(t) + np.random.randn(N)*randomness # radius
X[ix] = np.c_[r*np.cos(t), r*np.sin(t)]
Y[ix] = j
X = X.T
Y = Y.T
return X, Y
def plot_decision_boundary(model, X, y):
# Set min and max values and give it some padding
x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1
y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1
h = 0.01
# Generate a grid of points with distance h between them
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# Predict the function value for the whole grid
Z = model(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
# Plot the contour and training examples
plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
plt.ylabel('x2')
plt.xlabel('x1')
plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)
plt.show()
def load_2D_dataset():
data = scipy.io.loadmat('datasets/data.mat')
train_X = data['X'].T
train_Y = data['y'].T
test_X = data['Xval'].T
test_Y = data['yval'].T
#plt.scatter(train_X[0, :], train_X[1, :], c=np.squeeze(train_Y), s=40, cmap=plt.cm.Spectral);
return train_X, train_Y, test_X, test_Y调用数据集，代码如下：
import numpy as np
import reg_utils
import matplotlib.pyplot as plt
import testCases
import sklearn
import sklearn.datasets
train_X, train_Y, test_X, test_Y=reg_utils.load_2D_dataset()
print('训练样本={}'.format(train_X.shape))
print('训练样本标签={}'.format(train_Y.shape))
print('测试样本={}'.format(test_X.shape))
plt.show()打印结果：
第一种方法不用正则化和dropout即lambda=0,keep_prob=1,代码如下
import numpy as np
import reg_utils
import matplotlib.pyplot as plt
import testCases
import sklearn
import sklearn.datasets
train_X, train_Y, test_X, test_Y=reg_utils.load_2D_dataset()
print('训练样本={}'.format(train_X.shape))
print('训练样本标签={}'.format(train_Y.shape))
print('测试样本={}'.format(test_X.shape))
# plt.show()
"""
初始化权重 方差为2/n
"""
def initialize_parameters_he(layers_dims):
L=len(layers_dims)
parameters={}
for i in range(1,L):
parameters['W'+str(i)]=np.random.randn(layers_dims[i],layers_dims[i-1])\
*np.sqrt(2.0/layers_dims[i-1])
parameters['b' + str(i)]=np.zeros((layers_dims[i],1))
return parameters
'''
计算损失值：带有L2正则项的损失值
'''
def compute_cost_with_regularization(A3,Y,parameters,lambd):
m=Y.shape[1]
W1 = parameters['W1']
W2 = parameters['W2']
W3 = parameters['W3']
cost_entropy=reg_utils.compute_cost(A3, Y)
#cost_regularize = np.sum(np.sum(np.square(Wl)) for Wl in [W1, W2, W3]) * lambd / (2 * m)
cost_regularize=np.sum(np.sum(np.square(Wl)) for Wl in [W1,W2,W3])* lambd / (2 * m)
cost=cost_entropy+cost_regularize
return cost
"""
前向传播带有dropout
"""
def forward_propagation_with_dropout(X,paremeters,keep_prob):
W1 = paremeters['W1']
b1 = paremeters['b1']
W2 = paremeters['W2']
b2 = paremeters['b2']
W3 = paremeters['W3']
b3 = paremeters['b3']
Z1=np.dot(W1,X)+b1
A1=reg_utils.relu(Z1)
D1=np.random.rand(A1.shape[0],A1.shape[1])#np.random.rand 输出值在0 1之间
D1=(D1<keep_prob) # 返回的是 true false
#去掉A1上的一些神经元 只剩下 要的和0
A1=np.multiply(A1,D1)
#放大回去 确保A1的期望值不变
A1=A1/keep_prob
Z2 = np.dot(W2, A1) + b2
A2 = reg_utils.relu(Z2)
D2 = np.random.rand(A2.shape[0], A2.shape[1])
D2 = (D2 < keep_prob) # 返回的是 true false
A2 = np.multiply(A2, D2)
A2 = A2 / keep_prob
Z3 = np.dot(W3, A2) + b3
A3=reg_utils.sigmoid(Z3)
cache=(Z1,D1,A1,W1,b1,Z2,D2,A2,W2,b2,Z3,A3,W3,b3,)
return A3,cache
'''
后向传播：带有dropout
'''
def back_propagation_with_dropout(X,Y,cache,keep_prob):
m=X.shape[1]
(Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3,)=cache
dZ3 = 1. / m * (A3 - Y)
dW3 = np.dot(dZ3, A2.T)
db3 = np.sum(dZ3, axis=1, keepdims=True)
dA2 = np.dot(W3.T, dZ3)
dA2=np.multiply(dA2,D2)
dA2=dA2/keep_prob
dZ2 = np.multiply(dA2, np.int64(A2 > 0))
dW2 = np.dot(dZ2, A1.T)
db2 = np.sum(dZ2, axis=1, keepdims=True)
dA1 = np.dot(W2.T, dZ2)
dA1 = np.multiply(dA1, D1)
dA1 = dA1 / keep_prob
dZ1 = np.multiply(dA1, np.int64(A1 > 0))
dW1 = np.dot(dZ1, X.T)
db1 = np.sum(dZ1, axis=1, keepdims=True)
gradients={'dZ3':dZ3,'dW3':dW3,'db3':db3,'dA2':dA2,'dZ2':dZ2,
'dW2':dW2,'db2':db2,'dA1':dA1,'dZ1':dZ1,'dW1':dW1,'db1':db1}
return gradients
"""
后向传播带有L2正则项
"""
def back_propagation_with_regularization(X,Y,lambd,cache):
m=X.shape[1]
(Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3) = cache
#X,W1,A1,W2,A2,W3,A3=cache
dZ3=1./m *(A3-Y)
dW3=np.dot(dZ3,A2.T)+W3*(lambd/m)
db3=np.sum(dZ3, axis=1, keepdims = True)
dA2=np.dot(W3.T,dZ3)
dZ2=np.multiply(dA2,np.int64(A2 > 0))
dW2=np.dot(dZ2,A1.T)+W2*(lambd/m)#由此可看处 lambda越大 W的惩罚越大
db2 = np.sum(dZ2, axis=1, keepdims=True)
dA1=np.dot(W2.T,dZ2)
dZ1 = np.multiply(dA1, np.int64(A1 > 0))
dW1 = np.dot(dZ1, X.T) + W1 * (lambd / m)
db1 = np.sum(dZ1, axis=1, keepdims=True)
gradients = {"dZ3": dZ3, "dW3": dW3, "db3": db3, "dA2": dA2,
"dZ2": dZ2, "dW2": dW2, "db2": db2, "dA1": dA1,
"dZ1": dZ1, "dW1": dW1, "db1": db1}
return gradients
"""
构建模型
"""
def model(X,Y,num_iterations,learning_rate,lambd=0,keep_prob=1):
layers_dims=[X.shape[0], 20, 3, 1]
parameters=initialize_parameters_he(layers_dims)
costs=[]
for i in range(num_iterations):
if keep_prob==1:
A3, cache=reg_utils.forward_propagation(X, parameters)
elif keep_prob<1:
A3, cache=forward_propagation_with_dropout(X, parameters, keep_prob)
if lambd==0:
cost = reg_utils.compute_cost(A3, Y)
else:
cost=compute_cost_with_regularization(A3,Y,parameters,lambd)
if lambd==0 and keep_prob==1:
gradients=reg_utils.backward_propagation(X, Y, cache)
elif lambd!=0:
gradients=back_propagation_with_regularization(X, Y, lambd, cache)
elif keep_prob<1:
gradients=back_propagation_with_dropout(X,Y,cache,keep_prob)
parameters=reg_utils.update_parameters(parameters, gradients, learning_rate)
if i%1000==0:
print('after {} iterations cost is {}'.format(i,cost))
costs.append(cost)
plt.plot(costs)
plt.xlabel('num_iterations')
plt.ylabel('costs')
plt.title('learning rate is {}'.format(str(learning_rate)))
plt.show()
return parameters
def test():
#######test compute_cost_with_regularization
# a3, Y_assess, parameters=testCases.compute_cost_with_regularization_test_case()
# cost=compute_cost_with_regularization(a3, Y_assess, parameters,0.1)
# print(cost)
########################
#######back_propagation_with_regularization
# X_assess, Y_assess, cache=testCases.backward_propagation_with_regularization_test_case()
# gradients=back_propagation_with_regularization(X_assess, Y_assess,0.7,cache)
# print('dw1={} dw2={} dw3={}'.format(gradients['dW1'],gradients['dW2'],gradients['dW3']))
###################test forward_propagation_with_dropout
# X_assess, parameters=testCases.forward_propagation_with_dropout_test_case()
# A3, cache=forward_propagation_with_dropout(X_assess, parameters,keep_prob=0.7)
# print('A3={}'.format(A3))
###################test backward_propagation_with_dropout
X_assess, Y_assess, cache=testCases.backward_propagation_with_dropout_test_case()
gradients=back_propagation_with_dropout(X_assess, Y_assess, cache,keep_prob=0.8)
print('dA1={}'.format(gradients['dA1']))
print('dA2={}'.format(gradients['dA2']))
"""
测试模型
"""
def test_model():
parameters = model(train_X, train_Y, num_iterations=30000, learning_rate=0.3, lambd=0,keep_prob=1)
print('on the train sample')
train_prediction=reg_utils.predict(train_X, train_Y,parameters)
print('on the test sample')
test_prediction = reg_utils.predict(test_X, test_Y, parameters)
if __name__=='__main__':
#test()
test_model()
#pass打印结果：可看出过拟合了
lambda=0.7,keep_prob=1打印结果：可看出减少了过拟合
lambda=0.keep_prob=0.86,打印结果：可看出dropout也能减少过拟合。
