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, )