为什么我的theano函数不能编译？

Question

我正试着微调一个RBM自动编码器，我只是用随机初始参数和随机数据来测试我的theano函数是否可以成功编译，但它失败了，有人能运行这个代码并告诉我哪里出错了吗？提前谢谢

 import theano
 import theano.tensor as T
 import numpy as np
 import numpy
 rng = numpy.random

 # Assume the input data is a matrix of size(data_num,data_dim)
 # Assume the input data has already been normalized (zero mean and unit variance)
 # this is written for one batch, one layer


 '''
 b1 = T.matrix("b1")
 b2 = T.matrix("b2")
 b3 = T.matrix("b3")
 b4 = T.matrix("b4")
 b5 = T.matrix("b5")
 b6 = T.matrix("b6")

 w1 = T.matrix('w1')
 w2 = T.matrix('w2')
 w3 = T.matrix('w3')
 w4 = w1.T
 w5 = w2.T
 w6 = w3.T
 '''
 data = T.matrix("data")

 training_steps = 1000
 simulate_data_num = 50
 lr = 0.01

 ini_value_w1 = rng.randn(10,8)
 ini_value_w2 = rng.randn(8,6)
 ini_value_w3 = rng.randn(6,4)

 ini_value_b1 = np.tile(rng.randn(8,1),(1,simulate_data_num))
 ini_value_b2 = np.tile(rng.randn(6,1),(1,simulate_data_num))
 ini_value_b3 = np.tile(rng.randn(4,1),(1,simulate_data_num))
 ini_value_b4 = np.tile(rng.randn(6,1),(1,simulate_data_num))
 ini_value_b5 = np.tile(rng.randn(8,1),(1,simulate_data_num))
 ini_value_b6 = np.tile(rng.randn(10,1),(1,simulate_data_num))

 w1 = theano.shared(ini_value_w1, name="w1")
 w2 = theano.shared(ini_value_w2, name="w2")
 w3 = theano.shared(ini_value_w3, name="w3")
 w4 = w3.T
 w5 = w2.T
 w6 = w1.T

 b1 = theano.shared(ini_value_b1, name="b1")
 b2 = theano.shared(ini_value_b2, name="b2")
 b3 = theano.shared(ini_value_b3, name="b3")
 b4 = theano.shared(ini_value_b4, name="b4")
 b5 = theano.shared(ini_value_b5, name="b5")
 b6 = theano.shared(ini_value_b6, name="b6")

 para = [b1, b2, b3, b4, b5, b6, w1, w2, w3]

 step1_result = T.dot(w1.T,data) + b1 > 0
 step2_result = T.dot(w2.T,step1_result) + b2 > 0
 step3_result = T.dot(w3.T,step2_result) + b3 > 0
 step4_result = T.dot(w4.T,step3_result) + b4 > 0
 step5_result = T.dot(w5.T,step4_result) + b5 > 0
 result = T.dot(w6.T,step5_result) + b6

 diff = result - data # Cross-entropy loss function
 cost = (diff ** 2).mean() # no regulization, this will return the mean of the matrix
 gw1, gw2, gw3, gb1, gb2, gb3, gb4, gb5, gb6 = T.grad(cost, para)

 # Compile
 train = theano.function(
           inputs=[data],
           outputs=[cost],
           updates=((w1, w1 - lr*gw1),(w2, w2 - lr*gw2), \
            (w3, w3 - lr*gw3), (b1, b1 - lr*gb1), \
            (b2, b2 - lr*gb2), (b3, b3 - lr*gb3), \
            (b4, b4 - lr*gb4), (b5, b5 - lr*gb5), \
            (b6, b6 - lr*gb6)))

 predict = theano.function(inputs=[data], outputs=result)

 # Train

 simulate_data = rng.randn(10,simulate_data_num)  # 50 data points



 cost_record = []
 for i in range(training_steps):
    print("iteration:",i)
    cost = train(simulate_data)
    cost_record.append(cost)

 print(cost_record)
 print(cost_record[-1])

 '''
 print("Final model:")
 print(w.get_value())
 print(b.get_value())
 print("target values for D:")
 print(D[1])
 print("prediction on D:")
 print(predict(D[0]))
 '''