def check_NNGradients(ilambda=0):
"""
使用一个小型神经网络进行梯度检测, 比较分析梯度与数值梯度的差距
只进行一次传播就行, 只是验算是否正确, 不需要进行梯度下降, 训练时才需要
"""
# 神经网络参数
input_layer_size = 3
hidden_layer_size = 5
num_labels = 3
m = 5
# 随机生成参数
Theta1 = debug_initialize_weights(hidden_layer_size, input_layer_size)
Theta2 = debug_initialize_weights(num_labels, hidden_layer_size)
# 随机生成训练集 m*input_layer_size
X = debug_initialize_weights(m, input_layer_size - 1)
y = np.mod(np.arange(0, m), num_labels).reshape(m, 1)
# 展开参数
nn_params = np.vstack((Theta1.reshape(Theta1.size,
1), Theta2.reshape(Theta2.size, 1)))
my_nn = nn(nn_params, input_layer_size, hidden_layer_size, num_labels, X,
y, ilambda)
# 进行分析梯度计算
cost, grad = my_nn.cost_function()
# 进行数值梯度计算
numgrad = compute_numerical_gradient(my_nn, nn_params)
# 显示分析计算的梯度以及近似计算得到的数值梯度
for num, analyze in zip(numgrad, grad): # zip可以使多个迭代器一起迭代
print('{:.6f} {:.6f}'.format(float(num), float(analyze)))
print('\n上面的两列应该非常的相近.\n' '(左-数值梯度, 右-解析梯度)\n')
# Evaluate the norm of the difference between two solutions.
# If you have a correct implementation, and assuming you used EPSILON = 0.0001
# in compute_numerical_gradient.m, then diff below should be less than 1e-9
diff = np.linalg.norm(numgrad - grad) / np.linalg.norm(numgrad + grad)
print('如果你的反向传播算法是正确的, 相对偏差会非常的小(小于1e-9) \n' '相对偏差: {:.6e}'.format(diff))
def nnOutput(train1, test, element, var, analyse=False):
print "starting to run for subset"
train = train1.copy(deep=True)
if analyse: analyseData = train.copy(deep=True)
rawTransformed = getTargetVar(train)
nnObject = nn(
listOfMatrix=[np.random.rand(len(var), 8),
np.random.rand(8, 3)],
input=rawTransformed[var].as_matrix(),
output=rawTransformed[['high', 'medium', 'low']].as_matrix(),
func=sigmoid,
funcGradient=sigDeriv,
variables=var,
iteration=400)
d = booster(classifier=nnObject,
maxIteration=50,
test=test,
trainCopy=train1)
if analyse: return d.weightSelecter(analyse)
else: return d.nniterate()
def nnOutput(train, test, element, var, analyse=False):
print "starting to run for subset"
if analyse: analyseData = train.copy(deep=True)
rawTransformed = train
secondLayer = 8
d = nn(listOfMatrix=[
np.random.rand(len(var), secondLayer),
np.random.rand(secondLayer, 3)
],
input=rawTransformed[var].as_matrix(),
output=rawTransformed[['high', 'medium', 'low']].as_matrix(),
func=sigmoid,
funcGradient=sigDeriv,
variables=var,
iteration=500)
d.findEstimates()
print d.cost, d.input.shape[0]
if analyse:
analyseData1 = analyseData.copy(deep=True)
temp = d.analyseObservation(dataSet=analyseData1)
return temp
prediction = d.predict(test=test)
return prediction, d.cost, d.input.shape[0]
initn_bias_type = ii.BiasType.ZERO
cost_type = cc.Type.QUADRATIC
traing_type = tt.Type.GRADIENT_D
reg_type = rr.Type.NONE
reg_lambda = 0.01
learng_eta = 0.03
epochs = 5001
batch_size = 0 # len(traing_x)
input_log = True
traing_qtts_log = True
traing_acts_log = False
traing_cost_log = False
evaltn_cost_log = False
n_net = nn(layers, actvtn_types,
initn_weight_type, initn_bias_type,
cost_type,
reg_type, reg_lambda,
traing_type, epochs, batch_size, learng_eta,
groups,
traing_x_list, traing_y_list,
evaltn_x_list, evaltn_y_list,
input_log,
traing_qtts_log, traing_acts_log, traing_cost_log,
evaltn_cost_log)
n_net.training()
n_net.predictions()
q = load_Theta2[-1]
Theta2 = np.vstack((q.reshape(1, q.shape[0]), load_Theta2[:-1]))
# 将参数展开为一个长向量
nn_params = np.vstack((Theta1.reshape(Theta1.size, 1), Theta2.reshape(Theta2.size, 1)))
## ================ Part 3: 计算代价 (feed_forward) ================
#
print('利用神经网络的前向传播计算代价...\n')
# 正则参数设为0, 即代表不进行正则化
ilambda = 0
# 创建神经网络
my_nn = nn(nn_params, input_layer_size, hidden_layer_size,
num_labels, X, y, ilambda)
_,_,_,_,J = my_nn.feed_forward()
print('''从ex4weights中加载的参数计算得到的代价值为: {:.6f}
(这个值大约为: 0.287629)\n'''.format(J))
## =============== Part 4: 实现正则化 ===============
#
print('检查正则代价... \n')
#使用lambda来进行正则化的区分
ilambda = 1
my_nn_rec = nn(nn_params, input_layer_size, hidden_layer_size,
num_labels, X, y, ilambda)
