def __init__(self,
fit_mode='ne',
learning_rate=0.001,
iter_max=1000,
mini_batch=256,
L2_n=0.0,
early_stop=True):
#校验参数类型和取值
#check_type(变量名，变量类型，要求类型)
#check_limit(变量名，限制条件，正确取值提示)
check_type('fit_mode', type(fit_mode), type(''))
check_type('learning_rate', type(learning_rate), type(0.0))
check_type('iter_max', type(iter_max), type(0))
check_type('mini_batch', type(mini_batch), type(0))
check_type('L2_n', type(L2_n), type(0.0))
check_type('early_stop', type(early_stop), type(True))
fit_mode = fit_mode.lower()
mode_list = ['ne', 'sgd']
check_limit('fit_mode', fit_mode in mode_list, str(mode_list))
check_limit('learning_rate', learning_rate > 0.0, 'value>0.0')
check_limit('iter_max', iter_max > 0, 'value>0')
check_limit('mini_batch', mini_batch >= 0, 'value>=0')
check_limit('L2_n', L2_n >= 0.0, 'value>=0.0')
self.fit_mode = fit_mode
self.learning_rate = learning_rate
self.iter_max = iter_max
self.mini_batch = mini_batch
self.L2_n = L2_n
self.early_stop = early_stop
self.dp_tool = None
def assess(self,y,p_y,mode=None):
'''\n
Function: 使用输入的观测值和预测值进行模型评估
Notes: 注意数据集的数据类型，分类首选类型str，回归首选类型float64，
拟合时数据集采用非首选类型可能会导致此处类型不匹配，建议提前转换
Parameters
----------
y:观测值，Series类型
p_y:预测值，Series类型
mode:模式，str类型，默认使用内部集成单元的属性，
'c'->分类，'r'->回归
----------
Returns
-------
0: 分类->准确率，回归->R方，float类型
-------
'''
#校验参数
if type(mode)==type(None):
mode=self.units[0].tree.mode
check_type('mode',type(mode),type(''))
mode_list=['c','r']
check_limit('mode',mode in mode_list,str(mode_list))
y,continuity_y=self.unit_test.check_input_y_(y,name='y')
p_y,continuity_p_y=self.unit_test.check_input_y_(p_y,name='p_y')
check_index_match(y,p_y,'y','p_y')
#分类模式求准确率，回归模式求R2
if mode=='c':
return stats.accuracy(y,p_y)
elif mode=='r':
return stats.r_sqr(y,p_y)
def selection(self,test_X,test_y,units=None,units_oob_score=None,
use='oob',return_units=False,show_time=False):
'''\n
Function: 在生成好的模型上进行选择，筛选出集成单元的一个子集
Notes: 作用类似于决策树的剪枝，通过一些规则生成可选子集，
再通过在测试集上的表现选择最优的一个，能够得到更简单且泛化能力更强的模型
Parameters
----------
test_X: 测试集特征列，DataFrame类型
test_y: 测试集目标列，Series类型
units: 集成单元，list(DecitionTree)类型
units_oob_score: 集成单元obb评分，list(float)类型
use: 使用的选择方法，str类型，默认'oob'
'rd'->随机选择，'oob'->oob选择
return_units: 是否以返回值形式给到选择后的集成单元，bool类型，默认False
show_time: 是否显示耗时，bool类型，默认值False
----------
Returns
-------
0: 分类->准确率，回归->R方，float类型
-------
'''
start = time.clock()
if units==None:
units=self.units
if units_oob_score==None:
units_oob_score=self.units_oob_score
#输入校验
check_type('units',type(units),type([]))
check_type('element in units',type(units[0]),type(dt.DecisionTree()))
check_type('units_oob_score',type(units_oob_score),type([]))
check_type('element in units_oob_score',type(units_oob_score[0]),[type(0.0),np.float64])
check_type('use',type(use),type(''))
check_type('return_units',type(return_units),type(True))
use_list=['rd','oob']
check_limit('use',use in use_list,str(use_list))
test_X,continuity_X=self.unit_test.check_input_X_(test_X,'test_X')
test_y,continuity_y=self.unit_test.check_input_y_(test_y,'test_y')
check_index_match(test_X,test_y,'test_X','test_y')
features=[]
for unit in units:
features+=unit.tree.features
features=list(set(features))
check_items_match(test_X.columns,features,'test_X','tree','features',mode='right')
#选择
if use=='rd':
subset=self.random_selection_(test_X,test_y,units)
elif use=='oob':
subset=self.oob_selection_(test_X,test_y,units,units_oob_score)
end = time.clock()
if show_time==True:
print('\ntime used for selection:%f'%(end-start))
if return_units==False:
self.units=subset
else:
return subset
def bind_func_(self, activation, softmax, optimizer, mode):
#参数类型校验
check_type('activation', type(activation), [type(''), type(())])
check_type('softmax', type(softmax), type(True))
check_type('optimizer', type(optimizer), type(''))
#参数值校验
activation_list = ['sigm', 'tanh', 'relu']
check_limit('activation', activation in activation_list,
str(activation_list))
optimizer_list = ['sgd', 'magd', 'nagd', 'adam']
check_limit('optimizer', optimizer in optimizer_list,
str(optimizer_list))
#绑定函数
#隐含层激活函数
if activation == 'sigm':
self.activation_ = self.sigmoid_
elif activation == 'tanh':
self.activation_ = self.tanh_
elif activation == 'relu':
self.activation_ = self.relu_
else:
raise ValueError('Unknown activation function')
self.activation = activation
#输出层激活函数和代价函数
if mode == 'c':
if softmax == False:
self.cost = 'ce'
self.output_activation = 'sigm'
self.cost_ = self.cross_ent_
self.output_activation_ = self.sigmoid_
else:
self.cost = 'log'
self.output_activation = 'soft'
self.cost_ = self.log_like_
self.output_activation_ = self.softmax_
elif mode == 'r':
self.cost = 'mse'
self.output_activation = 'none'
self.cost_ = self.mean_sqr_err_
self.output_activation_ = self.identity_
else:
raise ValueError('Unknown mode')
self.softmax = softmax
#优化器
if optimizer == 'sgd':
self.optimizer_ = self.sgd_
elif optimizer == 'magd':
self.optimizer_ = self.momentum_
elif optimizer == 'nagd':
self.optimizer_ = self.nesterov_
elif optimizer == 'adam':
self.optimizer_ = self.adam_
else:
raise ValueError('Unknown optimizer')
self.optimizer = optimizer
def __init__(self,mode='c',units_n=10,units_type='cart',
depth_max=None,split_sample_n=2,leaf_sample_n=1,
features_use='sqrt',features_reuse=True):
#校验参数类型和取值
#check_type(变量名，变量类型，要求类型)
#check_limit(变量名，限制条件，正确取值提示)
check_type('mode',type(mode),type(''))
mode_list=['c','r']
mode=mode.lower()
check_limit('mode',mode in mode_list,str(mode_list))
check_type('units_n',type(units_n),type(0))
check_limit('units_n',units_n>=1,'value>=1')
check_type('units_type',type(units_type),type(''))
type_list=['id3','c4.5','cart']
units_type=units_type.lower()
check_limit('units_type',units_type in type_list,str(type_list))
#保存参数
self.unit_test=dt.DecisionTree(mode=mode,model_type=units_type,depth_max=depth_max,
split_sample_n=split_sample_n,leaf_sample_n=leaf_sample_n,
features_use=features_use,features_reuse=features_reuse)
self.mode=mode
self.units_n=units_n
self.units_type=units_type
self.depth_max=depth_max
self.split_sample_n=split_sample_n
self.leaf_sample_n=leaf_sample_n
self.features_use=features_use
self.features_reuse=features_reuse
def __init__(self,mode='c',units_type='cart',iter_max=10,depth_max=0,
learning_rate=1.0):
#校验参数类型和取值
check_type('mode',type(mode),type(''))
type_list=['r','c']
mode=mode.lower()
check_limit('mode',mode in type_list,str(type_list))
check_type('units_type',type(units_type),type(''))
type_list=['cart']
units_type=units_type.lower()
check_limit('units_type',units_type in type_list,str(type_list))
check_type('iter_max',type(iter_max),type(0))
check_limit('iter_max',iter_max>=1,'value>=1')
check_type('learning_rate',type(learning_rate),type(0.0))
check_limit('learning_rate',learning_rate>0.0,'value>0.0')
check_limit('learning_rate',learning_rate<=1.0,'value<=1.0')
if type(depth_max)==type(0):
if depth_max==0:
if mode=='r':
depth_max=3
elif mode=='c':
depth_max=3
#保存参数
#注：此处depth_max参考了sklearn,尝试过回归也用depth_max=1，效果很糟糕
self.unit_test=dt.DecisionTree(mode='r',model_type='cart',
depth_max=depth_max)
self.mode=mode
self.units_type='cart'
self.units_mode='r'
self.iter_max=iter_max
self.depth_max=depth_max
self.learning_rate=learning_rate
def assess(self,y,p_y,mode=None):
'''\n
Function: 使用输入的观测值和预测值进行模型评估
Notes: 注意数据集的数据类型，分类首选类型str，回归首选类型float64，
拟合时数据集采用非首选类型可能会导致此处类型不匹配，建议提前转换
Parameters
----------
y:观测值，Series类型
p_y:预测值，Series类型
mode:模式，str类型，默认使用内部集成单元的属性，
'c'->分类，'r'->回归
----------
Returns
-------
0: 分类->准确率，回归->R方，float类型
-------
'''
#校验参数
if type(mode)==type(None):
mode=self.mode
check_type('mode',type(mode),type(''))
mode_list=['c','r']
check_limit('mode',mode in mode_list,str(mode_list))
check_index_match(y,p_y,'y','p_y')
#分类模式求准确率，回归模式求R2
if mode=='c':
return stats.accuracy(y.astype('str'),p_y.astype('str'))
elif mode=='r':
r_sqr=stats.r_sqr(y,p_y)
if r_sqr<0:
print('warning: R2 is less than 0, which means bad fitting,'+
'\ntry to reduce the learning rate')
return r_sqr
def predict(self,X,units=None,mode=None,classes=None,units_result=False,
return_proba=False,return_paths=False,show_time=False):
'''\n
Function: 使用输入数据和所有集成单元进行预测，没有输入集成单元时使用内部缓存
Parameters
----------
X: 所有特征列，DataFrame类型
units: 集成单元，list(DecitionTree)类型，默认调用内部缓存
mode: 模式，分类->'c'，回归->'r'，默认'c'
classes: 分类标签列表，list(str)类型
units_result: 是否返回每个单元的分类结果，bool类型，默认False
return_proba: 是否返回分类概率，分类模式下有效，bool类型，默认值False，
分类概率不能直接用于评估
return_paths: 是否返回决策路径，bool类型，默认值False
（路径信息以str类型返回，可转换为list使用）
show_time: 是否显示耗时，bool类型，默认值False
----------
Returns
-------
0: 预测的分类/分类概率，Series/DataFrame类型
1: 各个单元的预测的分类/分类概率，list(Series)/list(DataFrame)类型
2: 所有数据最终抵达的节点和决策路径，list(DataFrame)类型
-------
'''
start = time.clock()
#校验参数
if type(units)==type(None):
units=self.units
if type(mode)==type(None):
mode=self.mode
check_type('mode',type(mode),type(''))
mode_list=['c','r']
check_limit('mode',mode in mode_list,str(mode_list))
if (type(classes)==type(None))&(mode=='c'):
classes=self.classes
check_type('units',type(units),type([]))
if len(units)==0:
raise ValueError('lack of units')
if mode=='r':
check_type('element in units',type(units[0]),type(dt.DecisionTree()))
elif mode=='c':
check_type('element in units',type(units[0][0]),type(dt.DecisionTree()))
check_type('return_proba',type(return_proba),type(True))
check_type('return_paths',type(return_paths),type(True))
check_type('show_time',type(show_time),type(True))
X,continuity_X=self.unit_test.check_input_X_(X)
features=[]
if mode=='c':
for units_ in units:
for unit in units_:
features+=unit.tree.features
elif mode=='r':
for unit in units:
features+=unit.tree.features
features=list(set(features))
check_items_match(X.columns,features,'X','unit','features',mode='right')
#分类模式先求分类概率，回归模式直接求回归值
n=len(X)
if mode=='c':
#定义存放分类结果的DataFrame
p_y=pd.DataFrame(
np.zeros((n,len(classes))),
index=X.index,columns=classes)
elif mode=='r':
#定义存放回归值的Series
p_y=pd.Series(np.zeros(n),index=X.index)
#逐个调用每个单元进行预测,并将结果累加
units_p_y,units_paths=[],[]
for i in range(len(units)):
if show_time==True:
print('\npredicting with unit %d ---'%i)
if mode=='r':
if return_paths==True:
p_y_,paths=units[i].predict(X,return_proba=True,return_paths=True,
show_time=show_time,check_input=False)
units_paths.append(paths)
else:
p_y_=units[i].predict(X,return_proba=True,return_paths=False,
show_time=show_time,check_input=False)
p_y+=p_y_
if units_result==True:
if (mode=='c')&(return_proba==False):
p_y_=units[i].choose_class_(p_y_,classes)
units_p_y.append(p_y_)
#分类模式需要调用子单元对每个类的概率进行预测
elif mode=='c':
classes_p_y,classes_paths=[],[]
for j in range(len(classes)):
if return_paths==True:
p_y_,paths=units[i][j].predict(X,return_proba=True,return_paths=True,
show_time=show_time,check_input=False)
classes_paths.append(paths)
else:
p_y_=units[i][j].predict(X,return_proba=True,return_paths=False,
show_time=show_time,check_input=False)
p_y.iloc[:,j]+=p_y_
if units_result==True:
if (mode=='c')&(return_proba==False):
p_y_=units[i].choose_class_(p_y_,classes)
classes_p_y.append(p_y_)
if return_paths==True:
units_paths.append(classes_paths)
if units_result==True:
units_p_y.append(classes_p_y)
#返回分类概率或唯一分类
if (mode=='c')&(return_proba==False):
p_y=self.unit_test.choose_class_(p_y,classes)
end = time.clock()
if show_time==True:
print('\ntotal time used for predict: %f'%(end-start))
if units_result==True:
if return_paths==True:
return p_y,units_p_y,paths
else:
return p_y,units_p_y
else:
if return_paths==True:
return p_y,paths
else:
return p_y
def __init__(self,
mode='c',
multi_class='ovr',
iter_max=10,
C=10.0,
k_type='lin',
k_args=None,
relax=0.001,
eps=1.0):
check_type('mode', type(mode), type(''))
check_type('multi_class', type(multi_class), type(''))
check_type('iter_max', type(iter_max), type(0))
check_type('C', type(C), type(0.0))
check_type('k_type', type(k_type), type(''))
check_type('relax', type(relax), type(0.0))
check_type('eps', type(eps), type(0.0))
mode_list, mode_list2 = ['c', 'r'], ['ovr', 'tree']
check_limit('mode', mode in mode_list, str(mode_list))
check_limit('multi_class', multi_class in mode_list2, str(mode_list2))
check_limit('iter_max', iter_max > 0, 'value>0')
check_limit('C', C > 0.0, 'value>0.0')
check_limit('relax', relax > 0.0, 'value>0.0')
check_limit('eps', eps > 0.0, 'value>0.0')
type_list = ['lin', 'pol', 'rbf']
check_limit('k_type', k_type in type_list, str(type_list))
if k_type != 'lin':
check_type('k_args', type(k_args), type({}))
if k_type == 'pol':
if ('R' in k_args.keys()) & ('d' in k_args.keys()):
check_type('k_args:R', type(k_args['R']), type(0.0))
check_type('k_args:d', type(k_args['d']), type(0))
check_limit('k_args:d', k_args['d'] > 0, 'value>0')
else:
raise ValueError(
'k_args should provide R and d for k_type: pol')
if k_type == 'rbf':
if 'sigma' in k_args.keys():
check_type('k_args:sigma', type(k_args['sigma']), type(0.0))
check_limit('k_args:sigma', k_args['sigma'] > 0.0, 'value>0.0')
else:
raise ValueError('k_args should provide sigma for k_type: rbf')
self.C = C
self.k_type = k_type
self.k_args = k_args
self.iter_max = iter_max
self.relax = relax
self.mode = mode
self.multi_class = multi_class
self.eps = eps
def __init__(self,
input_shape=(28, 28),
output_shape=(10, ),
hidden_layers=(100, ),
mode='c',
activation='sigm',
cost='mse',
optimizer='sgd',
batch_size=256,
iter_max=100,
learning_rate=0.1,
L2_alpha=0.0001,
dropout_p=0.0,
early_stop=10,
lr_atten_rate=0.9,
lr_atten_max=10,
momentum_p=0.9,
adam_beta1=0.9,
adam_beta2=0.999,
adam_eps=1e-8,
relu_a=0.0):
check_type('input_shape', type(input_shape), type(()))
check_type('output_shape', type(output_shape), type(()))
check_type('hidden_layers', type(hidden_layers), type(()))
check_type('mode', type(mode), type(''))
check_type('batch_size', type(batch_size), type(0))
check_type('iter_max', type(iter_max), type(0))
check_type('learning_rate', type(learning_rate), type(0.0))
check_type('L2_alpha', type(L2_alpha), type(0.0))
check_type('dropout_p', type(dropout_p), type(0.0))
if type(early_stop) == type(True):
if early_stop == True:
early_stop = 20
else:
early_stop = iter_max
check_type('early_stop', type(early_stop), type(0))
check_type('lr_atten_rate', type(lr_atten_rate), type(0.0))
check_type('lr_atten_max', type(lr_atten_max), type(0))
check_type('momentum_p', type(momentum_p), type(0.0))
check_type('adam_beta1', type(adam_beta1), type(0.0))
check_type('adam_beta2', type(adam_beta2), type(0.0))
check_type('adam_eps', type(adam_eps), type(0.0))
check_type('relu_a', type(relu_a), type(0.0))
for item in input_shape:
check_type('item in input_shape', type(item), type(0))
for item in input_shape:
check_type('item in output_shape', type(item), type(0))
for item in input_shape:
check_type('item in hidden_layers', type(item), type(0))
mode_list = ['c', 'r']
check_limit('mode', mode in mode_list, str(mode_list))
check_limit('batch_size', batch_size > 0, 'value>0')
check_limit('iter_max', iter_max > 0, 'value>0')
check_limit('learning_rate', learning_rate > 0.0, 'value>0.0')
check_limit('L2_alpha', L2_alpha >= 0.0, 'value>=0.0')
check_limit('dropout_p', (dropout_p >= 0.) & (dropout_p < 1.),
'0.<=value<1.')
check_limit('early_stop', early_stop > 0, 'value>0')
check_limit('lr_atten_rate',
(lr_atten_rate > 0.) & (lr_atten_rate <= 1.),
'0.<value<=1.')
check_limit('lr_atten_max', lr_atten_max >= 0, 'value>=0')
check_limit('momentum_p', (momentum_p > 0.) & (momentum_p < 1.),
'0<value<1')
check_limit('adam_beta1', (adam_beta1 > 0.) & (adam_beta1 < 1.),
'0<value<1')
check_limit('adam_beta2', (adam_beta2 > 0.) & (adam_beta2 < 1.),
'0<value<1')
check_limit('adam_eps', adam_eps > 0., 'value>0')
check_limit('relu_a', (relu_a >= 0.) & (relu_a <= 1.), '0<=value<=1')
self.mode = mode
if mode == 'r':
print('\nwarning: output_shape has been changed to (1,)')
output_shape = (1, )
self.input_shape = input_shape
self.output_shape = output_shape
self.hidden_layers = hidden_layers
self.input_size = int(np.array(input_shape).prod())
self.output_size = int(np.array(output_shape).prod())
self.layers = (self.input_size, ) + hidden_layers + (
self.output_size, )
self.bind_func_(activation, cost, optimizer)
self.random_init_()
self.batch_size = batch_size
self.iter_max = iter_max
self.learning_rate = learning_rate
self.L2_alpha = L2_alpha
self.dropout_p = dropout_p
self.early_stop = early_stop
self.lr_atten_rate = lr_atten_rate
self.lr_atten_max = lr_atten_max
self.momentum_p = momentum_p
self.adam_beta1 = adam_beta1
self.adam_beta2 = adam_beta2
self.adam_eps = adam_eps
self.relu_a = relu_a
self.time_cost = pd.Series(np.zeros(9),
index=[
'Total', 'input check', 'mini batch',
'forward prop', 'back prop', '--cost',
'--grad', '--delta', 'monitor'
],
name='time_cost')
self.classes = []
self.iter_total = 0
def bind_func_(self, activation, cost, optimizer):
#参数类型校验
check_type('activation', type(activation), [type(''), type(())])
check_type('cost', type(cost), type(''))
check_type('optimizer', type(optimizer), type(''))
#参数值校验
activation_list = ['sigm', 'tanh', 'relu', 'soft', 'none']
layers = self.layers
activation_config = []
if type(activation) == type(''):
check_limit('activation', activation in activation_list,
str(activation_list))
for i in range(len(layers) - 1):
activation_config.append(activation)
else:
for item in activation:
check_limit('item in activation', item in activation_list,
str(activation_list))
#将激活函数设置分配到各个层
if len(activation) == 1:
for i in range(len(layers) - 1):
activation_config.append(activation[0])
elif len(activation) == 2:
for i in range(len(layers) - 2):
activation_config.append(activation[0])
activation_config.append(activation[1])
elif len(activation) == len(layers) - 1:
for i in range(len(layers) - 1):
activation_config.append(activation[i])
else:
raise ValueError('\nconfig error: layers do not match')
if (activation_config[-1] in ('tanh', 'relu', 'none')) & (self.mode
== 'c'):
print(
'\nwarning: you set tanh/relu/none activation of output for classification'
)
if (activation_config[-1] in ('sigm', 'soft')) & (self.mode == 'r'):
print(
'\nwarning: you set sigmoid/softmax activation of output for regression'
)
cost_list = ['mse', 'ce', 'log']
check_limit('cost', cost in cost_list, str(cost_list))
if (cost in ['ce', 'log']) & (self.mode == 'r'):
print(
'\nwarning: you set cross entropy/log like cost for regression'
)
optimizer_list = ['sgd', 'magd', 'nagd', 'adam']
check_limit('optimizer', optimizer in optimizer_list,
str(optimizer_list))
#绑定函数
activations_ = []
for i in range(len(activation_config)):
if activation_config[i] == 'sigm':
activations_.append(self.sigmoid_)
elif activation_config[i] == 'tanh':
activations_.append(self.tanh_)
elif activation_config[i] == 'relu':
activations_.append(self.relu_)
elif activation_config[i] == 'soft':
activations_.append(self.softmax_)
elif activation_config[i] == 'none':
activations_.append(self.identity_)
else:
raise ValueError('Unknown activation function')
self.activation = activation_config
self.activations_ = activations_
if cost == 'mse':
self.cost_ = self.mean_sqr_err_
elif cost == 'ce':
self.cost_ = self.cross_ent_
elif cost == 'log':
self.cost_ = self.log_like_
else:
raise ValueError('Unknown cost function')
self.cost = cost
if optimizer == 'sgd':
self.optimizer_ = self.sgd_
elif optimizer == 'magd':
self.optimizer_ = self.momentum_
elif optimizer == 'nagd':
self.optimizer_ = self.nesterov_
elif optimizer == 'adam':
self.optimizer_ = self.adam_
else:
raise ValueError('Unknown optimizer')
self.optimizer = optimizer
def __init__(self,
mode='c',
hidden_layers=(100, ),
activation='sigm',
softmax=False,
optimizer='nagd',
batch_size=100,
iter_max=100,
learning_rate=0.01,
l2_alpha=0.0001,
dropout_p=0.0,
early_stop=10,
attenuation=0.9,
momentum_p=0.9,
adam_beta1=0.9,
adam_beta2=0.999,
adam_eps=1e-8,
relu_a=0.0,
external_monitor=None):
check_type('hidden_layers', type(hidden_layers), type(()))
check_type('mode', type(mode), type(''))
check_type('batch_size', type(batch_size), type(0))
check_type('iter_max', type(iter_max), type(0))
check_type('learning_rate', type(learning_rate), type(0.0))
check_type('l2_alpha', type(l2_alpha), type(0.0))
check_type('dropout_p', type(dropout_p), type(0.0))
if type(early_stop) == type(True):
if early_stop == True:
early_stop = 20
else:
early_stop = iter_max
check_type('early_stop', type(early_stop), type(0))
check_type('attenuation', type(attenuation), type(0.0))
check_type('momentum_p', type(momentum_p), type(0.0))
check_type('adam_beta1', type(adam_beta1), type(0.0))
check_type('adam_beta2', type(adam_beta2), type(0.0))
check_type('adam_eps', type(adam_eps), type(0.0))
check_type('relu_a', type(relu_a), type(0.0))
mode_list = ['c', 'r']
check_limit('mode', mode in mode_list, str(mode_list))
check_limit('batch_size', batch_size > 0, 'value>0')
check_limit('iter_max', iter_max > 0, 'value>0')
check_limit('learning_rate', learning_rate > 0.0, 'value>0.0')
check_limit('l2_alpha', l2_alpha >= 0.0, 'value>=0.0')
check_limit('dropout_p', (dropout_p >= 0.) & (dropout_p < 1.),
'0.<=value<1.')
check_limit('early_stop', early_stop > 0, 'value>0')
check_limit('attenuation', (attenuation > 0.) & (attenuation <= 1.),
'0.<value<=1.')
check_limit('momentum_p', (momentum_p > 0.) & (momentum_p < 1.),
'0<value<1')
check_limit('adam_beta1', (adam_beta1 > 0.) & (adam_beta1 < 1.),
'0<value<1')
check_limit('adam_beta2', (adam_beta2 > 0.) & (adam_beta2 < 1.),
'0<value<1')
check_limit('adam_eps', adam_eps > 0., 'value>0')
check_limit('relu_a', (relu_a >= 0.) & (relu_a <= 1.), '0<=value<=1')
self.mode = mode
self.hidden_layers = hidden_layers
self.bind_func_(activation, softmax, optimizer, mode)
self.batch_size = batch_size
self.iter_max = iter_max
self.learning_rate = learning_rate
self.l2_alpha = l2_alpha
self.dropout_p = dropout_p
self.early_stop = early_stop
self.attenuation = attenuation
self.momentum_p = momentum_p
self.adam_beta1 = adam_beta1
self.adam_beta2 = adam_beta2
self.adam_eps = adam_eps
self.relu_a = relu_a
self.external_monitor = external_monitor
self.is_fitted = False
self.iter_total = 0
self.time_cost = pd.Series(np.zeros(9),
index=[
'Total', 'input check', 'mini batch',
'forward prop', 'back prop', '--grad',
'--delta', 'update', 'monitor'
],
name='time_cost')