错误出现在：...(l2.b, l2.b - learning__rate * gb2)])
TypeError: ('Bad input argument to theano function with name "C:/Users/Administrator/Desktop/...python/theano/Regularization....py:66" at index 1(0-based)', 'Wrong number of dimensions: expected 2, got 1 with shape (200,).')
import theano
from sklearn.datasets import load_boston
import theano.tensor as T
import numpy as np
import matplotlib.pyplot as plt
class Layer(object):#定义神经层
def __init__(self, inputs, in_size, out_size, activation_function=None):
self.W = theano.shared(np.random.normal(0, 1, (in_size, out_size)))
self.b = theano.shared(np.zeros((out_size, )) + 0.1)
self.Wx_plus_b = T.dot(inputs, self.W) + self.b
self.activation_function = activation_function
if activation_function is None:
self.outputs = self.Wx_plus_b
else:
self.outputs = self.activation_function(self.Wx_plus_b)
def minmax_normalization(data):#正则化数据
xs_max = np.max(data, axis=0)
xs_min = np.min(data, axis=0)
xs = (1 - 0) * (data - xs_min) / (xs_max - xs_min) + 0
return xs
N=400
feats=28
lamda=0.1
np.random.seed(100)
x_data = rng.randn(N, feats)
x_data = minmax_normalization(x_data)
y_data = rng.randint(size=N, low=0, high=2)
x_train, y_train = x_data[:200], y_data[:200]
x_test, y_test = x_data[200:], y_data[200:]
x = T.dmatrix("x")
y = T.dmatrix("y")
l1 = Layer(x, 13, 50, T.tanh)
l2 = Layer(l1.outputs, 50, 1, None)
cost = T.mean(T.square(l2.outputs - y)) + lamda * ((l1.W ** 2).sum() + (l2.W ** 2).sum())
gW1, gb1, gW2, gb2 = T.grad(cost, [l1.W, l1.b, l2.W, l2.b])
learning_rate = 0.01
train = theano.function(
inputs=[x, y],
updates=[(l1.W, l1.W - learning_rate * gW1),
(l1.b, l1.b - learning_rate * gb1),
(l2.W, l2.W - learning_rate * gW2),
(l2.b, l2.b - learning_rate * gb2)])
compute_cost = theano.function(inputs=[x, y], outputs=cost)
train_err_list = []
test_err_list = []
learning_time = []
for i in range(1000):
train(x_train, y_train)
if i % 10 == 0:
# record cost
train_err_list.append(compute_cost(x_train, y_train))
test_err_list.append(compute_cost(x_test, y_test))
learning_time.append(i)
plt.plot(learning_time, train_err_list, 'r-')
plt.plot(learning_time, test_err_list, 'b--')
plt.show()
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