def create_adjustors(self): initial_momentum = .5 final_momentum = .99 start = 1 saturate = self.max_epochs self.momentum_adjustor = learning_rule.MomentumAdjustor( final_momentum, start, saturate) self.momentum_rule = learning_rule.Momentum(initial_momentum, nesterov_momentum=True) if self.lr_monitor_decay: self.learning_rate_adjustor = MonitorBasedLRAdjuster( high_trigger=1., shrink_amt=0.9, low_trigger=.95, grow_amt=1.1, channel_name='train_objective') elif self.lr_lin_decay: self.learning_rate_adjustor = LinearDecayOverEpoch( start, saturate, self.lr_lin_decay)
ann = mlp.MLP(layers, nvis=ds_train.nr_inputs) ##################################### #Define Training ##################################### #L1 Weight Decay L1_cost = PL.costs.cost.SumOfCosts([PL.costs.cost.MethodCost(method='cost_from_X'), PL.costs.mlp.L1WeightDecay(coeffs=[0.1, 0.01])]) # momentum initial_momentum = .5 final_momentum = .99 start = 1 saturate = 20 momentum_adjustor = learning_rule.MomentumAdjustor(final_momentum, start, saturate) momentum_rule = learning_rule.Momentum(initial_momentum) # learning rate start = .1 saturate = 20 decay_factor = .00001 learning_rate_adjustor = sgd.LinearDecayOverEpoch(start, saturate, decay_factor) # termination criterion that stops after 50 epochs without # any increase in misclassification on the validation set termination_criterion = MonitorBased(channel_name='objective', N=20, prop_decrease=0.0) # create Stochastic Gradient Descent trainer trainer = sgd.SGD(learning_rate=.001, batch_size=10, monitoring_dataset=ds_valid,
def main(): base_name = sys.argv[1] #文件名前缀 n_epoch = int(sys.argv[2]) # epoch次数 n_hidden = int(sys.argv[3]) # 隐含层节点数 include_rate = float(sys.argv[4]) # 包含率(1-dropout) in_size = 943 # 输入层节点数目 out_size = 4760 #输出层节点数 b_size = 200 #batch的大小 l_rate = 5e-4 #学习速率 l_rate_min = 1e-5 #学习速率最小值 decay_factor = 0.9 # lr_scale = 3.0 # momentum = 0.5 #摄动因子 init_vals = np.sqrt(6.0/(np.array([in_size, n_hidden])+np.array([n_hidden, out_size]))) print 'loading data...' #读取数据Train,Validation,Test X_tr = np.load('bgedv2_X_tr_float64.npy') Y_tr = np.load('bgedv2_Y_tr_0-4760_float64.npy') Y_tr_target = np.array(Y_tr) X_va = np.load('bgedv2_X_va_float64.npy') Y_va = np.load('bgedv2_Y_va_0-4760_float64.npy') Y_va_target = np.array(Y_va) X_te = np.load('bgedv2_X_te_float64.npy') Y_te = np.load('bgedv2_Y_te_0-4760_float64.npy') Y_te_target = np.array(Y_te) X_1000G = np.load('1000G_X_float64.npy') Y_1000G = np.load('1000G_Y_0-4760_float64.npy') Y_1000G_target = np.array(Y_1000G) X_GTEx = np.load('GTEx_X_float64.npy') Y_GTEx = np.load('GTEx_Y_0-4760_float64.npy') Y_GTEx_target = np.array(Y_GTEx) #随机化 random.seed(0) #随机抽取5000样本进行训练 monitor_idx_tr = random.sample(range(88807), 5000) #将数据X,Y整合成DensenMatrix类型 data_tr = p2_dt_dd.DenseDesignMatrix(X=X_tr.astype('float32'), y=Y_tr.astype('float32')) #取出X中对应5000样本进行训练 X_tr_monitor, Y_tr_monitor_target = X_tr[monitor_idx_tr, :], Y_tr_target[monitor_idx_tr, :] #设置多层感知机的隐含层计算方式 h1_layer = p2_md_mlp.Tanh(layer_name='h1', dim=n_hidden, irange=init_vals[0], W_lr_scale=1.0, b_lr_scale=1.0) #设置多层感知机的输出层计算方式 o_layer = p2_md_mlp.Linear(layer_name='y', dim=out_size, irange=0.0001, W_lr_scale=lr_scale, b_lr_scale=1.0) #设置好模型 model = p2_md_mlp.MLP(nvis=in_size, layers=[h1_layer, o_layer], seed=1) #设置dropout比例 dropout_cost = p2_ct_mlp_dropout.Dropout(input_include_probs={'h1':1.0, 'y':include_rate}, input_scales={'h1':1.0, 'y':np.float32(1.0/include_rate)}) #设置训练算法(batch大小,学习速率,学习规则,终止条件,dropout比例) algorithm = p2_alg_sgd.SGD(batch_size=b_size, learning_rate=l_rate, learning_rule = p2_alg_lr.Momentum(momentum), termination_criterion=p2_termcri.EpochCounter(max_epochs=1000), cost=dropout_cost) #设置训练类(数据集,训练模型,训练算法) train = pylearn2.train.Train(dataset=data_tr, model=model, algorithm=algorithm) train.setup() x = T.matrix() y = model.fprop(x) #训练好的模型对X的预测值 f = theano.function([x], y) MAE_va_old = 10.0 MAE_va_best = 10.0 MAE_tr_old = 10.0 MAE_te_old = 10.0 MAE_1000G_old = 10.0 MAE_1000G_best = 10.0 MAE_GTEx_old = 10.0 outlog = open(base_name + '.log', 'w') log_str = '\t'.join(map(str, ['epoch', 'MAE_va', 'MAE_va_change', 'MAE_te', 'MAE_te_change', 'MAE_1000G', 'MAE_1000G_change', 'MAE_GTEx', 'MAE_GTEx_change', 'MAE_tr', 'MAE_tr_change', 'learing_rate', 'time(sec)'])) print log_str outlog.write(log_str + '\n') sys.stdout.flush() #刷新缓冲区 for epoch in range(0, n_epoch): t_old = time.time() #开始时间 train.algorithm.train(train.dataset)#训练 #计算不同数据集预测值 Y_va_hat = f(X_va.astype('float32')).astype('float64') Y_te_hat = f(X_te.astype('float32')).astype('float64') Y_tr_hat_monitor = f(X_tr_monitor.astype('float32')).astype('float64') Y_1000G_hat = f(X_1000G.astype('float32')).astype('float64') Y_GTEx_hat = f(X_GTEx.astype('float32')).astype('float64') #计算预测值与真实值的MAE MAE_va = np.abs(Y_va_target - Y_va_hat).mean() MAE_te = np.abs(Y_te_target - Y_te_hat).mean() MAE_tr = np.abs(Y_tr_monitor_target - Y_tr_hat_monitor).mean() MAE_1000G = np.abs(Y_1000G_target - Y_1000G_hat).mean() MAE_GTEx = np.abs(Y_GTEx_target - Y_GTEx_hat).mean() #计算迭代误差 MAE_va_change = (MAE_va - MAE_va_old)/MAE_va_old MAE_te_change = (MAE_te - MAE_te_old)/MAE_te_old MAE_tr_change = (MAE_tr - MAE_tr_old)/MAE_tr_old MAE_1000G_change = (MAE_1000G - MAE_1000G_old)/MAE_1000G_old MAE_GTEx_change = (MAE_GTEx - MAE_GTEx_old)/MAE_GTEx_old #更新MAE MAE_va_old = MAE_va MAE_te_old = MAE_te MAE_tr_old = MAE_tr MAE_1000G_old = MAE_1000G MAE_GTEx_old = MAE_GTEx t_new = time.time() #终止时间 l_rate = train.algorithm.learning_rate.get_value() log_str = '\t'.join(map(str, [epoch+1, '%.6f'%MAE_va, '%.6f'%MAE_va_change, '%.6f'%MAE_te, '%.6f'%MAE_te_change, '%.6f'%MAE_1000G, '%.6f'%MAE_1000G_change, '%.6f'%MAE_GTEx, '%.6f'%MAE_GTEx_change, '%.6f'%MAE_tr, '%.6f'%MAE_tr_change, '%.5f'%l_rate, int(t_new-t_old)])) print log_str outlog.write(log_str + '\n') sys.stdout.flush() if MAE_tr_change > 0: #如果误差增大,减小学习速率 l_rate = l_rate*decay_factor if l_rate < l_rate_min: #学习速率最小为l_rate_min l_rate = l_rate_min train.algorithm.learning_rate.set_value(np.float32(l_rate)) #更改训练类的学习速率参数 #更新Validation误差值 if MAE_va < MAE_va_best: MAE_va_best = MAE_va outmodel = open(base_name + '_bestva_model.pkl', 'wb') pkl.dump(model, outmodel) outmodel.close() np.save(base_name + '_bestva_Y_te_hat.npy', Y_te_hat) np.save(base_name + '_bestva_Y_va_hat.npy', Y_va_hat) #更新1000G误差值 if MAE_1000G < MAE_1000G_best: MAE_1000G_best = MAE_1000G outmodel = open(base_name + '_best1000G_model.pkl', 'wb') pkl.dump(model, outmodel) outmodel.close() np.save(base_name + '_best1000G_Y_1000G_hat.npy', Y_1000G_hat) np.save(base_name + '_best1000G_Y_GTEx_hat.npy', Y_GTEx_hat) print 'MAE_va_best : %.6f' % (MAE_va_best) print 'MAE_1000G_best : %.6f' % (MAE_1000G_best) outlog.write('MAE_va_best : %.6f' % (MAE_va_best) + '\n') outlog.write('MAE_1000G_best : %.6f' % (MAE_1000G_best) + '\n') outlog.close()
l2 = RectifiedLinear(layer_name='l2', irange=ir, dim=dim, max_col_norm=1.) l3 = RectifiedLinear(layer_name='l3', irange=ir, dim=dim, max_col_norm=1.) output = Softmax(layer_name='y', n_classes=9, irange=ir, max_col_norm=mcn_out) mdl = MLP([l1, l2, l3, output], nvis=X2.shape[1]) trainer = sgd.SGD(learning_rate=lr, batch_size=bs, learning_rule=learning_rule.Momentum(mm), cost=Dropout(default_input_include_prob=ip, default_input_scale=1 / ip), termination_criterion=EpochCounter(epochs), seed=seed) decay = sgd.LinearDecayOverEpoch(start=2, saturate=20, decay_factor=.1) experiment = Train(dataset=training, model=mdl, algorithm=trainer, extensions=[decay]) experiment.main_loop() epochs_current = epochs for s in range(n_add): trainer = sgd.SGD(learning_rate=lr * .1, batch_size=bs, learning_rule=learning_rule.Momentum(mm),
def main(): base_name = sys.argv[1] n_epoch = int(sys.argv[2]) n_hidden = int(sys.argv[3]) include_rate = float(sys.argv[4]) in_size = 943 out_size = 4760 b_size = 200 l_rate = 3e-4 l_rate_min = 1e-5 decay_factor = 0.9 lr_scale = 3.0 momentum = 0.5 init_vals = np.sqrt(6.0/(np.array([in_size, n_hidden, n_hidden, n_hidden])+np.array([n_hidden, n_hidden, n_hidden, out_size]))) print 'loading data...' X_tr = np.load('bgedv2_X_tr_float64.npy') Y_tr = np.load('bgedv2_Y_tr_4760-9520_float64.npy') Y_tr_target = np.array(Y_tr) X_va = np.load('bgedv2_X_va_float64.npy') Y_va = np.load('bgedv2_Y_va_4760-9520_float64.npy') Y_va_target = np.array(Y_va) X_te = np.load('bgedv2_X_te_float64.npy') Y_te = np.load('bgedv2_Y_te_4760-9520_float64.npy') Y_te_target = np.array(Y_te) X_1000G = np.load('1000G_X_float64.npy') Y_1000G = np.load('1000G_Y_4760-9520_float64.npy') Y_1000G_target = np.array(Y_1000G) X_GTEx = np.load('GTEx_X_float64.npy') Y_GTEx = np.load('GTEx_Y_4760-9520_float64.npy') Y_GTEx_target = np.array(Y_GTEx) random.seed(0) monitor_idx_tr = random.sample(range(88807), 5000) data_tr = p2_dt_dd.DenseDesignMatrix(X=X_tr.astype('float32'), y=Y_tr.astype('float32')) X_tr_monitor, Y_tr_monitor_target = X_tr[monitor_idx_tr, :], Y_tr_target[monitor_idx_tr, :] h1_layer = p2_md_mlp.Tanh(layer_name='h1', dim=n_hidden, irange=init_vals[0], W_lr_scale=1.0, b_lr_scale=1.0) h2_layer = p2_md_mlp.Tanh(layer_name='h2', dim=n_hidden, irange=init_vals[1], W_lr_scale=lr_scale, b_lr_scale=1.0) h3_layer = p2_md_mlp.Tanh(layer_name='h3', dim=n_hidden, irange=init_vals[2], W_lr_scale=lr_scale, b_lr_scale=1.0) o_layer = p2_md_mlp.Linear(layer_name='y', dim=out_size, irange=0.0001, W_lr_scale=lr_scale, b_lr_scale=1.0) model = p2_md_mlp.MLP(nvis=in_size, layers=[h1_layer, h2_layer, h3_layer, o_layer], seed=1) dropout_cost = p2_ct_mlp_dropout.Dropout(input_include_probs={'h1':1.0, 'h2':include_rate, 'h3':include_rate, 'y':include_rate}, input_scales={'h1':1.0, 'h2':np.float32(1.0/include_rate), 'h3':np.float32(1.0/include_rate), 'y':np.float32(1.0/include_rate)}) algorithm = p2_alg_sgd.SGD(batch_size=b_size, learning_rate=l_rate, learning_rule = p2_alg_lr.Momentum(momentum), termination_criterion=p2_termcri.EpochCounter(max_epochs=1000), cost=dropout_cost) train = pylearn2.train.Train(dataset=data_tr, model=model, algorithm=algorithm) train.setup() x = T.matrix() y = model.fprop(x) f = theano.function([x], y) MAE_va_old = 10.0 MAE_va_best = 10.0 MAE_tr_old = 10.0 MAE_te_old = 10.0 MAE_1000G_old = 10.0 MAE_1000G_best = 10.0 MAE_GTEx_old = 10.0 outlog = open(base_name + '.log', 'w') log_str = '\t'.join(map(str, ['epoch', 'MAE_va', 'MAE_va_change', 'MAE_te', 'MAE_te_change', 'MAE_1000G', 'MAE_1000G_change', 'MAE_GTEx', 'MAE_GTEx_change', 'MAE_tr', 'MAE_tr_change', 'learing_rate', 'time(sec)'])) print log_str outlog.write(log_str + '\n') sys.stdout.flush() for epoch in range(0, n_epoch): t_old = time.time() train.algorithm.train(train.dataset) Y_va_hat = f(X_va.astype('float32')).astype('float64') Y_te_hat = f(X_te.astype('float32')).astype('float64') Y_tr_hat_monitor = f(X_tr_monitor.astype('float32')).astype('float64') Y_1000G_hat = f(X_1000G.astype('float32')).astype('float64') Y_GTEx_hat = f(X_GTEx.astype('float32')).astype('float64') MAE_va = np.abs(Y_va_target - Y_va_hat).mean() MAE_te = np.abs(Y_te_target - Y_te_hat).mean() MAE_tr = np.abs(Y_tr_monitor_target - Y_tr_hat_monitor).mean() MAE_1000G = np.abs(Y_1000G_target - Y_1000G_hat).mean() MAE_GTEx = np.abs(Y_GTEx_target - Y_GTEx_hat).mean() MAE_va_change = (MAE_va - MAE_va_old)/MAE_va_old MAE_te_change = (MAE_te - MAE_te_old)/MAE_te_old MAE_tr_change = (MAE_tr - MAE_tr_old)/MAE_tr_old MAE_1000G_change = (MAE_1000G - MAE_1000G_old)/MAE_1000G_old MAE_GTEx_change = (MAE_GTEx - MAE_GTEx_old)/MAE_GTEx_old MAE_va_old = MAE_va MAE_te_old = MAE_te MAE_tr_old = MAE_tr MAE_1000G_old = MAE_1000G MAE_GTEx_old = MAE_GTEx t_new = time.time() l_rate = train.algorithm.learning_rate.get_value() log_str = '\t'.join(map(str, [epoch+1, '%.6f'%MAE_va, '%.6f'%MAE_va_change, '%.6f'%MAE_te, '%.6f'%MAE_te_change, '%.6f'%MAE_1000G, '%.6f'%MAE_1000G_change, '%.6f'%MAE_GTEx, '%.6f'%MAE_GTEx_change, '%.6f'%MAE_tr, '%.6f'%MAE_tr_change, '%.5f'%l_rate, int(t_new-t_old)])) print log_str outlog.write(log_str + '\n') sys.stdout.flush() if MAE_tr_change > 0: l_rate = l_rate*decay_factor if l_rate < l_rate_min: l_rate = l_rate_min train.algorithm.learning_rate.set_value(np.float32(l_rate)) if MAE_va < MAE_va_best: MAE_va_best = MAE_va outmodel = open(base_name + '_bestva_model.pkl', 'wb') pkl.dump(model, outmodel) outmodel.close() np.save(base_name + '_bestva_Y_te_hat.npy', Y_te_hat) np.save(base_name + '_bestva_Y_va_hat.npy', Y_va_hat) if MAE_1000G < MAE_1000G_best: MAE_1000G_best = MAE_1000G outmodel = open(base_name + '_best1000G_model.pkl', 'wb') pkl.dump(model, outmodel) outmodel.close() np.save(base_name + '_best1000G_Y_1000G_hat.npy', Y_1000G_hat) np.save(base_name + '_best1000G_Y_GTEx_hat.npy', Y_GTEx_hat) print 'MAE_va_best : %.6f' % (MAE_va_best) print 'MAE_1000G_best : %.6f' % (MAE_1000G_best) outlog.write('MAE_va_best : %.6f' % (MAE_va_best) + '\n') outlog.write('MAE_1000G_best : %.6f' % (MAE_1000G_best) + '\n') outlog.close()
def main(): base_name = sys.argv[ 1] # 获取第一个参数 sys.argv[ ]记录(获取)命令行参数 sys(system) argv(argument variable)参数变量,该变量为list列表 n_epoch = int(sys.argv[2]) #获取第二个参数 n_hidden = int(sys.argv[3]) #获取第三个参数作为隐层神经元个数 include_rate = float(sys.argv[4]) in_size = 1001 #输入层神经元个数(标记基因个数) out_size = 1 #输出层神经元个数 b_size = 200 #偏差值 l_rate = 5e-4 #学习速率 l_rate_min = 1e-5 #学习速率最小值 decay_factor = 0.9 #衰减因数 lr_scale = 3.0 momentum = 0.5 init_vals = np.sqrt(6.0 / (np.array([in_size, n_hidden]) + np.array([n_hidden, out_size]))) #初始值,返回平方根 print 'loading data...' #显示载入数据 X_tr = np.load( 'geno_X_tr_float64.npy') # tr(traing)以numpy专用二进制类型保存训练数据集的数据 Y_tr = np.load('pheno_Y_tr_0-4760_float64.npy') Y_tr_pheno = np.array(Y_tr) X_va = np.load( 'geno_X_va_float64.npy') #验证集(模型选择,在学习到不同复杂度的模型中,选择对验证集有最小预测误差的模型) Y_va = np.load('pheno_Y_va_0-4760_float64.npy') Y_va_target = np.array(Y_va) X_te = np.load('geno_te_float64.npy') #测试集(对学习方法的评估) Y_te = np.load('pheno_Y_te_0-4760_float64.npy') Y_te_target = np.array(Y_te) random.seed(0) #设置生成随机数用的整数起始值。调用任何其他random模块函数之前调用这个函数 monitor_idx_tr = random.sample(range(88807), 5000) #监测训练 #将训练数据集类型设为32位浮点型,The DenseDesignMatrix class and related code Functionality for representing data that can be described as a dense matrix (rather than a sparse matrix) with each row containing an example and each column corresponding to a different feature. data_tr = p2_dt_dd.DenseDesignMatrix(X=X_tr.astype('float32'), y=Y_tr.astype('float32')) X_tr_monitor, Y_tr_monitor_target = X_tr[monitor_idx_tr, :], Y_tr_target[ monitor_idx_tr, :] #一个隐层,用Tanh()作激活函数; 输出层用线性函数作激活函数 h1_layer = p2_md_mlp.Tanh(layer_name='h1', dim=n_hidden, irange=init_vals[0], W_lr_scale=1.0, b_lr_scale=1.0) o_layer = p2_md_mlp.Linear(layer_name='y', dim=out_size, irange=0.0001, W_lr_scale=lr_scale, b_lr_scale=1.0) #Multilayer Perceptron;nvis(Number of “visible units” input units) layers(a list of layer objects,最后1层指定MLP的输出空间) model = p2_md_mlp.MLP(nvis=in_size, layers=[h1_layer, o_layer], seed=1) dropout_cost = p2_ct_mlp_dropout.Dropout(input_include_probs={ 'h1': 1.0, 'y': include_rate }, input_scales={ 'h1': 1.0, 'y': np.float32(1.0 / include_rate) }) #随机梯度下降法 algorithm = p2_alg_sgd.SGD( batch_size=b_size, learning_rate=l_rate, learning_rule=p2_alg_lr.Momentum(momentum), termination_criterion=p2_termcri.EpochCounter(max_epochs=1000), cost=dropout_cost) #训练 根据前面的定义 :dataset为一个密集型矩阵,model为MLP多层神经网络,algorithm为SGD train = pylearn2.train.Train(dataset=data_tr, model=model, algorithm=algorithm) train.setup() x = T.matrix() #定义为一个二维数组 #fprop(state_below) does the forward prop transformation y = model.fprop(x) f = theano.function([x], y) #定义一个function函数,输入为x,输出为y MAE_va_old = 10.0 #平均绝对误差 MAE_va_best = 10.0 MAE_tr_old = 10.0 #训练误差 MAE_te_old = 10.0 MAE_1000G_old = 10.0 MAE_1000G_best = 10.0 MAE_GTEx_old = 10.0 #base_name = sys.argv[1] # 获取第一个参数 sys.argv[ ]记录(获取)命令行参数 outlog = open(base_name + '.log', 'w') log_str = '\t'.join( map(str, [ 'epoch', 'MAE_va', 'MAE_va_change', 'MAE_te', 'MAE_te_change', 'MAE_tr', 'MAE_tr_change', 'learing_rate', 'time(sec)' ])) print log_str #输出运行日志 outlog.write(log_str + '\n') #Python的标准输出缓冲(这意味着它收集“写入”标准出来之前,将其写入到终端的数据)。调用sys.stdout.flush()强制其“缓冲 sys.stdout.flush() for epoch in range(0, n_epoch): t_old = time.time() train.algorithm.train(train.dataset) Y_va_hat = f(X_va.astype('float32')).astype('float64') Y_te_hat = f(X_te.astype('float32')).astype('float64') Y_tr_hat_monitor = f(X_tr_monitor.astype('float32')).astype('float64') #计算平均绝对误差 MAE_va = np.abs(Y_va_target - Y_va_hat).mean() MAE_te = np.abs(Y_te_target - Y_te_hat).mean() MAE_tr = np.abs(Y_tr_monitor_target - Y_tr_hat_monitor).mean() #误差变换率 MAE_va_change = (MAE_va - MAE_va_old) / MAE_va_old MAE_te_change = (MAE_te - MAE_te_old) / MAE_te_old MAE_tr_change = (MAE_tr - MAE_tr_old) / MAE_tr_old #将old误差值更新为当前误差值 MAE_va_old = MAE_va MAE_te_old = MAE_te MAE_tr_old = MAE_tr #返回当前的时间戳(1970纪元后经过的浮点秒数) t_new = time.time() l_rate = train.algorithm.learning_rate.get_value() log_str = '\t'.join( map(str, [ epoch + 1, '%.6f' % MAE_va, '%.6f' % MAE_va_change, '%.6f' % MAE_te, '%.6f' % MAE_te_change, '%.6f' % MAE_tr, '%.6f' % MAE_tr_change, '%.5f' % l_rate, int(t_new - t_old) ])) print log_str outlog.write(log_str + '\n') sys.stdout.flush() if MAE_tr_change > 0: #训练误差变换率大于0时,学习速率乘上一个衰减因子 l_rate = l_rate * decay_factor if l_rate < l_rate_min: #学习速率小于最小速率时,更新为最小速率 l_rate = l_rate_min train.algorithm.learning_rate.set_value(np.float32(l_rate)) if MAE_va < MAE_va_best: MAE_va_best = MAE_va outmodel = open(base_name + '_bestva_model.pkl', 'wb') pkl.dump(model, outmodel) outmodel.close() np.save(base_name + '_bestva_Y_te_hat.npy', Y_te_hat) np.save(base_name + '_bestva_Y_va_hat.npy', Y_va_hat) print 'MAE_va_best : %.6f' % (MAE_va_best) outlog.write('MAE_va_best : %.6f' % (MAE_va_best) + '\n') outlog.close()
def main(): training_data, validation_data, test_data, std_scale = load_training_data() kaggle_test_features = load_test_data(std_scale) ############### # pylearn2 ML hl1 = mlp.Sigmoid(layer_name='hl1', dim=200, irange=.1, init_bias=1.) hl2 = mlp.Sigmoid(layer_name='hl2', dim=100, irange=.1, init_bias=1.) # create Softmax output layer output_layer = mlp.Softmax(9, 'output', irange=.1) # create Stochastic Gradient Descent trainer that runs for 400 epochs trainer = sgd.SGD(learning_rate=.05, batch_size=300, learning_rule=learning_rule.Momentum(.5), termination_criterion=MonitorBased( channel_name='valid_objective', prop_decrease=0., N=10), monitoring_dataset={ 'valid': validation_data, 'train': training_data }) layers = [hl1, hl2, output_layer] # create neural net model = mlp.MLP(layers, nvis=93) watcher = best_params.MonitorBasedSaveBest( channel_name='valid_objective', save_path='pylearn2_results/pylearn2_test.pkl') velocity = learning_rule.MomentumAdjustor(final_momentum=.6, start=1, saturate=250) decay = sgd.LinearDecayOverEpoch(start=1, saturate=250, decay_factor=.01) ###################### experiment = Train(dataset=training_data, model=model, algorithm=trainer, extensions=[watcher, velocity, decay]) experiment.main_loop() #load best model and test ################ model = serial.load('pylearn2_results/pylearn2_test.pkl') # get an prediction of the accuracy from the test_data test_results = model.fprop(theano.shared(test_data[0], name='test_data')).eval() print test_results.shape loss = multiclass_log_loss(test_data[1], test_results) print 'Test multiclass log loss:', loss out_file = 'pylearn2_results/' + str(loss) + 'ann' #exp.save(out_file + '.pkl') #save the kaggle results results = model.fprop( theano.shared(kaggle_test_features, name='kaggle_test_data')).eval() save_results(out_file + '.csv', kaggle_test_features, results)
output = mlp.Softmax(n_classes=2, layer_name='y', irange=.01) #output = mlp.HingeLoss(layer_name='y',n_classes=2,irange=.05) #layers = [l5, l6, output] layers = [l1, l2, l3, l4, l5, output] ann = mlp.MLP(layers, nvis=X[0].reshape(-1).shape[0]) lr = 0.1 epochs = 400 trainer = sgd.SGD( learning_rate=lr, batch_size=100, learning_rule=learning_rule.Momentum(.05), # Remember, default dropout is .5 #cost=Dropout(input_include_probs={'l1': .5}, # input_scales={'l1': 1.}), termination_criterion=EpochCounter(epochs), monitoring_dataset={ 'train': ds, 'valid': ds_test }) watcher = best_params.MonitorBasedSaveBest(channel_name='valid_roc_auc', save_path='saved_clf.pkl') velocity = learning_rule.MomentumAdjustor(final_momentum=.9, start=1, saturate=250)
save_path = 'valid_best_fold%d.pkl' % fold print save_path images_train = images[train_index] y_train = y[train_index] images_train, y_train = shuffle(images_train, y_train, random_state=7) X_train = DenseDesignMatrix(X=images_train, y=y_train,view_converter=view_converter) images_test = images[test_index] y_test = y[test_index] X_test = DenseDesignMatrix(X=images_test, y=y_test,view_converter=view_converter) if retrain: print "training on", X_train.X.shape, 'testing on', X_test.X.shape trainer = sgd.SGD(learning_rate=learn_rate, batch_size=batch_size, learning_rule=learning_rule.Momentum(momentum_start), cost=Dropout( input_include_probs={'l1':1., 'l2':1., 'l3':1., 'l4':1., 'l5':1., 'l6':1.}, input_scales={'l1':1., 'l2':1., 'l3':1., 'l4':1., 'l5':1., 'l6':1.} ), termination_criterion=EpochCounter(max_epochs=max_epochs), monitoring_dataset={'train':X_train, 'valid':X_test}, ) input_space = Conv2DSpace(shape=(central_window_shape, central_window_shape), axes = axes, num_channels = 1) ann = mlp.MLP(layers, input_space=input_space)
random.seed(0) #设置生成随机数用的整数起始值。调用任何其他random模块函数之前调用这个函数 monitor_idx_tr = random.sample(range(88807), 5000) #监测训练 #将训练数据集类型设为32位浮点型,The DenseDesignMatrix class and related code Functionality for representing data that can be described as a dense matrix (rather than a sparse matrix) with each row containing an example and each column corresponding to a different feature. data_tr = p2_dt_dd.DenseDesignMatrix(X=X_tr.astype('float32'), y=Y_tr.astype('float32')) X_tr_monitor, Y_tr_monitor_target = X_tr[monitor_idx_tr, :], Y_tr_target[monitor_idx_tr, :] #一个隐层,用Tanh()作激活函数; 输出层用线性函数作激活函数 h1_layer = p2_md_mlp.Tanh(layer_name='h1', dim=n_hidden, irange=init_vals[0], W_lr_scale=1.0, b_lr_scale=1.0) o_layer = p2_md_mlp.Linear(layer_name='y', dim=out_size, irange=0.0001, W_lr_scale=lr_scale, b_lr_scale=1.0) #Multilayer Perceptron;nvis(Number of “visible units” input units) layers(a list of layer objects,最后1层指定MLP的输出空间) model = p2_md_mlp.MLP(nvis=in_size, layers=[h1_layer, o_layer], seed=1) dropout_cost = p2_ct_mlp_dropout.Dropout(input_include_probs={'h1':1.0, 'y':include_rate}, input_scales={'h1':1.0, 'y':np.float32(1.0/include_rate)}) #随机梯度下降法 algorithm = p2_alg_sgd.SGD(batch_size=b_size, learning_rate=l_rate, learning_rule = p2_alg_lr.Momentum(momentum), termination_criterion=p2_termcri.EpochCounter(max_epochs=1000), cost=dropout_cost) #训练 根据前面的定义 :dataset为一个密集型矩阵,model为MLP多层神经网络,algorithm为SGD train = pylearn2.train.Train(dataset=data_tr, model=model, algorithm=algorithm) train.setup() x = T.matrix() #定义为一个二维数组 #fprop(state_below) does the forward prop transformation y = model.fprop(x) f = theano.function([x], y) #定义一个function函数,输入为x,输出为y MAE_va_old = 10.0 #平均绝对误差 MAE_va_best = 10.0 MAE_tr_old = 10.0 #训练误差 MAE_te_old = 10.0
def main( x ): l1_dim = x[0] l2_dim = x[1] learning_rate = x[2] momentum = x[3] l1_dropout = x[4] decay_factor = x[5] min_lr = 1e-7 # train = np.loadtxt( train_file, delimiter = ',' ) x_train = train[:,0:-1] y_train = train[:,-1] y_train.shape = ( y_train.shape[0], 1 ) # validation = np.loadtxt( validation_file, delimiter = ',' ) x_valid = validation[:,0:-1] y_valid = validation[:,-1] y_valid.shape = ( y_valid.shape[0], 1 ) # #input_space = VectorSpace( dim = x.shape[1] ) full = DenseDesignMatrix( X = x_train, y = y_train ) valid = DenseDesignMatrix( X = x_valid, y = y_valid ) l1 = mlp.RectifiedLinear( layer_name='l1', irange=.001, dim = l1_dim, # "Rather than using weight decay, we constrain the norms of the weight vectors" max_col_norm=1. ) l2 = mlp.RectifiedLinear( layer_name='l2', irange=.001, dim = l2_dim, max_col_norm=1. ) output = mlp.Linear( dim = 1, layer_name='y', irange=.0001 ) layers = [l1, l2, output] nvis = x_train.shape[1] mdl = mlp.MLP( layers, nvis = nvis ) # input_space = input_space #lr = .001 #epochs = 100 decay = sgd.ExponentialDecay( decay_factor = decay_factor, min_lr = min_lr ) trainer = sgd.SGD( learning_rate = learning_rate, batch_size=128, learning_rule=learning_rule.Momentum( momentum ), update_callbacks = [ decay ], # Remember, default dropout is .5 cost = Dropout( input_include_probs = {'l1': l1_dropout}, input_scales={'l1': 1.}), #termination_criterion = EpochCounter(epochs), termination_criterion = MonitorBased( channel_name = "valid_objective", prop_decrease = 0.001, # 0.1% of objective N = 10 ), # valid_objective is MSE monitoring_dataset = { 'train': full, 'valid': valid } ) watcher = best_params.MonitorBasedSaveBest( channel_name = 'valid_objective', save_path = output_model_file ) experiment = Train( dataset = full, model = mdl, algorithm = trainer, extensions = [ watcher ] ) experiment.main_loop() ### error = get_error_from_model( output_model_file ) print "*** error: {} ***".format( error ) return error