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<http://stackoverflow.com/questions/41083805/why-my-theano-function-can-not-be-compiled#>
I'm trying to fine-tune a RBM autoencoder and I just use random initial
parameters and random data to test if my theano function can be
successfully compiled, but it fails, can someone run this code and tell me
where it is wrong? Thanks in advance.
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]))
'''
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