L2_fm = 1.0 dropoutRate = .8 n_epochs = 5 #### start = datetime.now() # initialize a FM learner learner = FM_FTRL_machine(fm_dim, fm_initDev, L1, L2, L1_fm, L2_fm, D, alpha, beta, alpha_fm=alpha_fm, beta_fm=beta_fm, dropoutRate=dropoutRate) print("Start Training:") for e in range(n_epochs): # if it is the first epoch, then don't use L1_fm or L2_fm if e == 0: learner.L1_fm = 0. learner.L2_fm = 0. else: learner.L1_fm = L1_fm
fm_dim, fm_initDev = 12, .02 alpha, beta = .1, 1 alpha_fm, beta_fm = .01, 1. D = 2 ** 20 L1, L2 = 1.0, .1 L1_fm, L2_fm = 1.0, .1 n_epochs = 1 start = datetime.now() learner = FM_FTRL_machine(fm_dim, fm_initDev, L1, L2, L1_fm, L2_fm, D, alpha, beta, alpha_fm = alpha_fm, beta_fm = beta_fm) print("Start Training:") for e in range(n_epochs): learner.L1_fm = 0 if e == 0 else L1_fm learner.L2_fm = 0 if e == 0 else L2_fm progressiveLoss, progressiveCount = 0., 0. for t, x, y in data(trainingFile, D): p = learner.predict(x) loss = logLoss(p, y) learner.update(x, p, y) progressiveLoss += loss
p_D = 22 D = 2 ** p_D L1 = 1.0 L2 = .1 L1_fm = 2.0 L2_fm = 3.0 n_epochs = 5 #### start = datetime.now() # initialize a FM learner learner = FM_FTRL_machine(fm_dim, fm_initDev, L1, L2, L1_fm, L2_fm, D, alpha, beta, alpha_fm = alpha_fm, beta_fm = beta_fm) print("Start Training:") for e in range(n_epochs): # if it is the first epoch, then don't use L1_fm or L2_fm if e == 0: learner.L1_fm = 0. learner.L2_fm = 0. else: learner.L1_fm = L1_fm learner.L2_fm = L2_fm cvLoss = 0. cvCount = 0. progressiveLoss = 0.
L1 = 1.0 L2 = .1 L1_fm = 2.0 L2_fm = 1.0 dropoutRate = .8 n_epochs = 5 #### start = datetime.now() # initialize a FM learner learner = FM_FTRL_machine(fm_dim, fm_initDev, L1, L2, L1_fm, L2_fm, D, alpha, beta, alpha_fm = alpha_fm, beta_fm = beta_fm, dropoutRate = dropoutRate) print("Start Training:") for e in range(n_epochs): # if it is the first epoch, then don't use L1_fm or L2_fm if e == 0: learner.L1_fm = 0. learner.L2_fm = 0. else: learner.L1_fm = L1_fm learner.L2_fm = L2_fm cvLoss = 0. cvCount = 0. progressiveLoss = 0.
D = 2**20 L1, L2 = 1.0, .1 L1_fm, L2_fm = 1.0, .1 n_epochs = 1 start = datetime.now() learner = FM_FTRL_machine(fm_dim, fm_initDev, L1, L2, L1_fm, L2_fm, D, alpha, beta, alpha_fm=alpha_fm, beta_fm=beta_fm) print("Start Training:") for e in range(n_epochs): learner.L1_fm = 0 if e == 0 else L1_fm learner.L2_fm = 0 if e == 0 else L2_fm progressiveLoss, progressiveCount = 0., 0. for t, x, y in data(trainingFile, D):