def __init__(
self,
output_act_func='softmax',
hidden_act_func='relu',
loss_func='cross_entropy',
use_for='classification',
lr=1e-3,
epochs=100,
img_shape=[28, 28],
layer_tp=['C', 'C', 'P', 'C', 'P'],
channels=[1, 6, 6, 64,
10], # 输入1张图 -> 卷积池化成6张图 -> 卷积池化成6张图 -> 全连接层 -> 分类层
fsize=[[4, 4], [3, 3]],
ksize=[[2, 2], [2, 2]],
batch_size=32,
dropout=0):
Model.__init__(self, 'CNN')
self.hidden_act_func = hidden_act_func
self.output_act_func = out_act_check(output_act_func, loss_func)
self.loss_func = loss_func
self.use_for = use_for
self.lr = lr
self.epochs = epochs
self.channels = channels
self.layer_tp = layer_tp
self.img_shape = img_shape
self.fsize = fsize
self.ksize = ksize
self.batch_size = batch_size
self.dropout = dropout
with tf.name_scope('CNN'):
self.build_model()
def __init__(self,
output_act_func='softmax',
loss_func='cross_entropy',
use_for='classification',
bp_algorithm='adam',
lr=1e-3,
momentum=0.9,
epochs=100,
width=5,
struct=[784, 100 ,10],
batch_size=32,
dropout=0,
variants=0):
Model.__init__(self,'LSTM')
self.loss_func=loss_func
self.output_act_func = out_act_check(output_act_func,loss_func)
self.use_for=use_for
self.bp_algorithm=bp_algorithm
self.lr=lr
self.momentum=momentum
self.epochs=epochs
self.width=width
self.struct = struct
self.batch_size = batch_size
self.dropout=dropout
self.variants=variants
if output_act_func=='gauss':
self.loss_func='mse'
self.output_act=act_func(self.output_act_func)
self.build_model()
def __init__(
self,
name='AE-1',
loss_func='mse', # decoder：[sigmoid] with ‘cross_entropy’ | [affine] with ‘mse’
ae_type='ae', # ae | dae | sae
act_type=['sigmoid', 'affine'],
noise_type='gs', # Gaussian noise (gs) | Masking noise (mn)
beta=0.5, # DAE：噪声损失系数 | SAE：稀疏损失系数
p=0.5, # DAE：样本该维作为噪声的概率 | SAE稀疏性参数：期望的隐层平均活跃度（在训练批次上取平均）
struct=[784, 100],
out_size=10,
ae_epochs=10,
batch_size=32,
lr=1e-3):
Model.__init__(self, name)
self.name = name
self.loss_func = loss_func
self.ae_type = ae_type
self.noise_type = noise_type
self.beta = beta
self.alpha = 1 - self.beta
self.p = p
self.n_x = struct[0]
self.n_h = struct[1]
self.out_size = out_size
self.epochs = ae_epochs
self.batch_size = batch_size
self.lr = lr
self.momentum = 0.5
self.en_func = act_type[0]
self.de_func = out_act_check(act_type[1], loss_func)
# sae: kl 要求 h 必须是0~1之间的数
if ae_type == 'sae' and (act_type[0]
not in ['softmax', 'sigmoid', 'gauss']):
self.en_func = 'sigmoid'
with tf.name_scope(self.name):
self.build_model()
def __init__(
self,
output_func='softmax',
hidden_func='affine', # encoder：[sigmoid] | [affine]
use_for='classification',
loss_func='mse', # decoder：[sigmoid] with ‘cross_entropy’ | [affine] with ‘mse’
struct=[784, 100, 100, 10],
lr=1e-4,
epochs=60,
batch_size=32,
dropout=0,
ae_type='dae', # ae | dae | sae
act_type=['sigmoid', 'affine'],
noise_type='mn', # Gaussian noise (gs) | Masking noise (mn)
beta=0.25, # 惩罚因子权重（KL项 | 非噪声样本项）
p=0.5, # DAE：样本该维作为噪声的概率 / SAE稀疏性参数：期望的隐层平均活跃度（在训练批次上取平均）
ae_lr=1e-3,
ae_epochs=20,
pre_train=True):
Model.__init__(self, 'sup_sAE')
self.hidden_func = hidden_func
self.output_act_func = out_act_check(output_func, loss_func)
self.use_for = use_for
self.loss_func = loss_func
self.struct = struct
self.lr = lr
self.epochs = epochs
self.dropout = dropout
self.pre_train = pre_train
self.ae_type = ae_type
self.act_type = act_type
self.noise_type = noise_type
self.beta = beta
self.p = p
self.ae_epochs = ae_epochs
self.ae_lr = ae_lr
self.batch_size = batch_size
self.build_model()
def __init__(
self,
hidden_act_func='relu',
output_act_func='softmax',
loss_func='mse', # gauss 激活函数会自动转换为 mse 损失函数
struct=[784, 100, 100, 10],
lr=1e-4,
momentum=0.5,
use_for='classification',
bp_algorithm='adam',
epochs=100,
batch_size=32,
dropout=0.3,
units_type=['gauss', 'bin'],
rbm_lr=1e-3,
rbm_epochs=30,
cd_k=1,
pre_train=True):
Model.__init__(self, 'DBN')
self.loss_func = loss_func
self.hidden_act_func = hidden_act_func
self.output_act_func = out_act_check(output_act_func, loss_func)
self.use_for = use_for
self.bp_algorithm = bp_algorithm
self.lr = lr
self.momentum = momentum
self.epochs = epochs
self.struct = struct
self.batch_size = batch_size
self.dropout = dropout
self.pre_train = pre_train
self.dbm_struct = struct[:-1]
self.units_type = units_type
self.cd_k = cd_k
self.rbm_lr = rbm_lr
self.rbm_epochs = rbm_epochs
self.build_model()