import dataset, models, loss, optim
import numpy as np
from theano import tensor as T, function, shared
def init_theta():
M, K = 784, 10
theta = [None]*2
theta[0] = shared(np.zeros((M, K), 'float32'), borrow=True)
theta[1] = shared(np.zeros((K,), 'float32'), borrow=True)
return theta
if __name__ == '__main__':
# config
itMax = 195*2
szBatch = 256
lr = 0.1
vaFreq = 20
import tr_va_te
tr_va_te.run(itMax=itMax,
szBatch=szBatch,
lr=lr,
vaFreq=vaFreq,
init_theta=init_theta,
mo_create=models.create_linear)
# hidden layer II
theta2 = (theta[2], theta[3])
if is_tr:
a2 = models.create_dropout(models.create_linear_sigmoid(a1, theta2), trng=trng)
else:
a2 = models.create_dropout(models.create_linear_sigmoid(a1, theta2), trng=None)
# output layer
theta3 = (theta[4], theta[5])
a3 = models.create_linear_sigmoid(a2, theta3)
return x, a3
if __name__ == '__main__':
# config
itMax = 195*2
szBatch = 256
lr = 0.1
vaFreq = 20
import tr_va_te
tr_va_te.run(itMax=itMax,
szBatch=szBatch,
lr=lr,
vaFreq=vaFreq,
pa_init=init_param,
mo_create=create_mlp,
)
