def __init__(self,
layers,
original_shape,
name='convnet',
loss_func='softmax_cross_entropy',
num_epochs=10,
batch_size=10,
opt='sgd',
learning_rate=0.01,
momentum=0.5,
dropout=0.5,
batch_norm=False):
"""Constructor.
:param layers: string used to build the model.
This string is a comma-separate specification of the layers.
Supported values:
conv2d-FX-FY-Z-S: 2d convolution with Z feature maps as output
and FX x FY filters. S is the strides size
maxpool-X: max pooling on the previous layer. X is the size of
the max pooling
full-X: fully connected layer with X units
softmax: softmax layer
For example:
conv2d-5-5-32,maxpool-2,conv2d-5-5-64,maxpool-2,full-128,full-128,softmax
:param original_shape: original shape of the images in the dataset
:param dropout: Dropout parameter
"""
print("correct cnn file")
SupervisedModel.__init__(self, name)
self.loss_func = loss_func
self.learning_rate = learning_rate
self.opt = opt
self.num_epochs = num_epochs
self.batch_size = batch_size
self.momentum = momentum
self.loss = Loss(self.loss_func)
self.trainer = Trainer(opt,
learning_rate=learning_rate,
momentum=momentum)
self.layers = layers
self.original_shape = original_shape
self.dropout = dropout
self.batch_norm = batch_norm
self.W_vars = None
self.B_vars = None
self.accuracy = None
def __init__(self,
num_hidden,
visible_unit_type='bin',
name='rbm',
loss_func='mse',
learning_rate=0.01,
regcoef=5e-4,
regtype='none',
gibbs_sampling_steps=1,
batch_size=10,
num_epochs=10,
stddev=0.1):
"""Constructor.
:param num_hidden: number of hidden units
:param loss_function: type of loss function
:param visible_unit_type: type of the visible units (bin or gauss)
:param gibbs_sampling_steps: optional, default 1
:param stddev: default 0.1. Ignored if visible_unit_type is not 'gauss'
"""
UnsupervisedModel.__init__(self, name)
self.loss_func = loss_func
self.learning_rate = learning_rate
self.num_epochs = num_epochs
self.batch_size = batch_size
self.regtype = regtype
self.regcoef = regcoef
self.loss = Loss(self.loss_func)
self.num_hidden = num_hidden
self.visible_unit_type = visible_unit_type
self.gibbs_sampling_steps = gibbs_sampling_steps
self.stddev = stddev
self.W = None
self.bh_ = None
self.bv_ = None
self.w_upd8 = None
self.bh_upd8 = None
self.bv_upd8 = None
self.cost = None
self.input_data = None
self.hrand = None
self.vrand = None
def __init__(self, name='lr', loss_func='cross_entropy',
learning_rate=0.01, num_epochs=10, batch_size=10):
"""Constructor."""
SupervisedModel.__init__(self, name)
self.loss_func = loss_func
self.learning_rate = learning_rate
self.num_epochs = num_epochs
self.batch_size = batch_size
self.loss = Loss(self.loss_func)
# Computational graph nodes
self.input_data = None
self.input_labels = None
self.W_ = None
self.b_ = None
self.accuracy = None
def __init__(self,
rbm_layers,
name='dbn',
do_pretrain=False,
rbm_num_epochs=[10],
rbm_gibbs_k=[1],
rbm_gauss_visible=False,
rbm_stddev=0.1,
rbm_batch_size=[10],
rbm_learning_rate=[0.01],
finetune_dropout=1,
finetune_loss_func='softmax_cross_entropy',
finetune_act_func=tf.nn.sigmoid,
finetune_opt='sgd',
finetune_learning_rate=0.001,
finetune_num_epochs=10,
finetune_batch_size=20,
momentum=0.5):
"""Constructor.
:param rbm_layers: list containing the hidden units for each layer
:param finetune_loss_func: Loss function for the softmax layer.
string, default ['softmax_cross_entropy', 'mse']
:param finetune_dropout: dropout parameter
:param finetune_learning_rate: learning rate for the finetuning.
float, default 0.001
:param finetune_act_func: activation function for the finetuning phase
:param finetune_opt: optimizer for the finetuning phase
:param finetune_num_epochs: Number of epochs for the finetuning.
int, default 20
:param finetune_batch_size: Size of each mini-batch for the finetuning.
int, default 20
:param do_pretrain: True: uses variables from pretraining,
False: initialize new variables.
"""
SupervisedModel.__init__(self, name)
self.loss_func = finetune_loss_func
self.learning_rate = finetune_learning_rate
self.opt = finetune_opt
self.num_epochs = finetune_num_epochs
self.batch_size = finetune_batch_size
self.momentum = momentum
self.dropout = finetune_dropout
self.loss = Loss(self.loss_func)
self.trainer = Trainer(finetune_opt,
learning_rate=finetune_learning_rate,
momentum=momentum)
self.do_pretrain = do_pretrain
self.layers = rbm_layers
self.finetune_act_func = finetune_act_func
# Model parameters
self.encoding_w_ = [] # list of matrices of encoding weights per layer
self.encoding_b_ = [] # list of arrays of encoding biases per layer
self.softmax_W = None
self.softmax_b = None
rbm_params = {
'num_epochs': rbm_num_epochs,
'gibbs_k': rbm_gibbs_k,
'batch_size': rbm_batch_size,
'learning_rate': rbm_learning_rate
}
for p in rbm_params:
if len(rbm_params[p]) != len(rbm_layers):
# The current parameter is not specified by the user,
# should default it for all the layers
rbm_params[p] = [rbm_params[p][0] for _ in rbm_layers]
self.rbms = []
self.rbm_graphs = []
for l, layer in enumerate(rbm_layers):
rbm_str = 'rbm-' + str(l + 1)
if l == 0 and rbm_gauss_visible:
self.rbms.append(
rbm.RBM(name=self.name + '-' + rbm_str,
num_hidden=layer,
learning_rate=rbm_params['learning_rate'][l],
num_epochs=rbm_params['num_epochs'][l],
batch_size=rbm_params['batch_size'][l],
gibbs_sampling_steps=rbm_params['gibbs_k'][l],
visible_unit_type='gauss',
stddev=rbm_stddev))
else:
self.rbms.append(
rbm.RBM(name=self.name + '-' + rbm_str,
num_hidden=layer,
learning_rate=rbm_params['learning_rate'][l],
num_epochs=rbm_params['num_epochs'][l],
batch_size=rbm_params['batch_size'][l],
gibbs_sampling_steps=rbm_params['gibbs_k'][l]))
self.rbm_graphs.append(tf.Graph())
def __init__(
self, n_components, name='dae', loss_func='mse',
enc_act_func=tf.nn.tanh, dec_act_func=None, num_epochs=10,
batch_size=10, opt='sgd', learning_rate=0.01, momentum=0.9,
corr_type='none', corr_frac=0., regtype='none', regcoef=5e-4):
"""Constructor.
Parameters
----------
n_components : int
Number of hidden units.
name : str, optional (default = "dae")
Model name (used for save/load from disk).
loss_func : str, optional (default = "mse")
Loss function. ['mse', 'cross_entropy']
enc_act_func : tf.nn.[activation]
Activation function for the encoder.
dec_act_func : tf.nn.[activation]
Activation function for the decoder.
num_epochs : int, optional (default = 10)
Number of epochs.
batch_size : int, optional (default = 10)
Size of each mini-batch.
opt : str, optional (default = "sgd")
Which tensorflow optimizer to use.
Possible values: ['sgd', 'momentum', 'adagrad', 'adam']
learning_rate : float, optional (default = 0.01)
Initial learning rate.
momentum : float, optional (default = 0.9)
Momentum parameter (only used if opt = "momentum").
corr_type : str, optional (default = "none")
Type of input corruption.
Can be one of: ["none", "masking", "salt_and_pepper"]
corr_frac : float, optional (default = 0.0)
Fraction of the input to corrupt.
regtype : str, optional (default = "none")
Type of regularization to apply.
Can be one of: ["none", "l1", "l2"].
regcoef : float, optional (default = 5e-4)
Regularization parameter. If 0, no regularization.
Only considered if regtype != "none".
"""
UnsupervisedModel.__init__(self, name)
self.loss_func = loss_func
self.learning_rate = learning_rate
self.opt = opt
self.num_epochs = num_epochs
self.batch_size = batch_size
self.momentum = momentum
self.regtype = regtype
self.regcoef = regcoef
self.loss = Loss(self.loss_func)
self.trainer = Trainer(
opt, learning_rate=learning_rate, momentum=momentum)
self.n_components = n_components
self.enc_act_func = enc_act_func
self.dec_act_func = dec_act_func
self.corr_type = corr_type
self.corr_frac = corr_frac
self.input_data_orig = None
self.input_data = None
self.W_ = None
self.bh_ = None
self.bv_ = None
def __init__(self,
layers,
name='sdae',
enc_act_func=[tf.nn.tanh],
dec_act_func=[None],
loss_func=['cross_entropy'],
num_epochs=[10],
batch_size=[10],
opt=['sgd'],
regcoef=[5e-4],
learning_rate=[0.01],
momentum=0.5,
finetune_dropout=1,
corr_type=['none'],
corr_frac=[0.],
finetune_loss_func='softmax_cross_entropy',
finetune_act_func=tf.nn.relu,
finetune_opt='sgd',
finetune_learning_rate=0.001,
finetune_num_epochs=10,
finetune_batch_size=20,
do_pretrain=False):
"""Constructor.
:param layers: list containing the hidden units for each layer
:param enc_act_func: Activation function for the encoder.
[tf.nn.tanh, tf.nn.sigmoid]
:param dec_act_func: Activation function for the decoder.
[tf.nn.tanh, tf.nn.sigmoid, None]
:param finetune_loss_func: Loss function for the softmax layer.
string, default ['softmax_cross_entropy', 'mse']
:param finetune_dropout: dropout parameter
:param finetune_learning_rate: learning rate for the finetuning.
float, default 0.001
:param finetune_act_func: activation function for the finetuning phase
:param finetune_opt: optimizer for the finetuning phase
:param finetune_num_epochs: Number of epochs for the finetuning.
int, default 20
:param finetune_batch_size: Size of each mini-batch for the finetuning.
int, default 20
:param corr_type: Type of input corruption. string, default 'none'.
["none", "masking", "salt_and_pepper"]
:param corr_frac: Fraction of the input to corrupt. float, default 0.0
:param do_pretrain: True: uses variables from pretraining,
False: initialize new variables.
"""
# WARNING! This must be the first expression in the function or else it
# will send other variables to expanded_args()
# This function takes all the passed parameters that are lists and
# expands them to the number of layers, if the number
# of layers is more than the list of the parameter.
expanded_args = utilities.expand_args(**locals())
SupervisedModel.__init__(self, name)
self.loss_func = finetune_loss_func
self.learning_rate = finetune_learning_rate
self.opt = finetune_opt
self.num_epochs = finetune_num_epochs
self.batch_size = finetune_batch_size
self.momentum = momentum
self.dropout = finetune_dropout
self.loss = Loss(self.loss_func)
self.trainer = Trainer(finetune_opt,
learning_rate=finetune_learning_rate,
momentum=momentum)
self.do_pretrain = do_pretrain
self.layers = layers
self.finetune_act_func = finetune_act_func
# Model parameters
self.encoding_w_ = [] # list of matrices of encoding weights per layer
self.encoding_b_ = [] # list of arrays of encoding biases per layer
self.last_W = None
self.last_b = None
self.autoencoders = []
self.autoencoder_graphs = []
for l, layer in enumerate(layers):
dae_str = 'dae-' + str(l + 1)
self.autoencoders.append(
denoising_autoencoder.DenoisingAutoencoder(
n_components=layer,
name=self.name + '-' + dae_str,
enc_act_func=expanded_args['enc_act_func'][l],
dec_act_func=expanded_args['dec_act_func'][l],
loss_func=expanded_args['loss_func'][l],
opt=expanded_args['opt'][l],
regcoef=expanded_args['regcoef'],
learning_rate=expanded_args['learning_rate'][l],
momentum=self.momentum,
corr_type=expanded_args['corr_type'][l],
corr_frac=expanded_args['corr_frac'][l],
num_epochs=expanded_args['num_epochs'][l],
batch_size=expanded_args['batch_size'][l]))
self.autoencoder_graphs.append(tf.Graph())
def __init__(self,
layers,
name='srbm',
num_epochs=[10],
batch_size=[10],
learning_rate=[0.01],
gibbs_k=[1],
loss_func=['mse'],
momentum=0.5,
finetune_dropout=1,
finetune_loss_func='cross_entropy',
finetune_enc_act_func=[tf.nn.relu],
finetune_dec_act_func=[tf.nn.sigmoid],
finetune_opt='sgd',
finetune_learning_rate=0.001,
regcoef=5e-4,
finetune_num_epochs=10,
noise=['gauss'],
stddev=0.1,
finetune_batch_size=20,
do_pretrain=False,
tied_weights=False,
regtype=['none'],
finetune_regtype='none'):
"""Constructor.
:param layers: list containing the hidden units for each layer
:param finetune_loss_func: Loss function for the softmax layer.
string, default ['cross_entropy', 'mse']
:param finetune_dropout: dropout parameter
:param finetune_learning_rate: learning rate for the finetuning.
float, default 0.001
:param finetune_enc_act_func: activation function for the encoder
finetuning phase
:param finetune_dec_act_func: activation function for the decoder
finetuning phase
:param finetune_opt: optimizer for the finetuning phase
:param finetune_num_epochs: Number of epochs for the finetuning.
int, default 20
:param finetune_batch_size: Size of each mini-batch for the finetuning.
int, default 20
:param do_pretrain: True: uses variables from pretraining,
False: initialize new variables.
"""
# WARNING! This must be the first expression in the function or else it
# will send other variables to expanded_args()
# This function takes all the passed parameters that are lists and
# expands them to the number of layers, if the number
# of layers is more than the list of the parameter.
expanded_args = utilities.expand_args(**locals())
UnsupervisedModel.__init__(self, name)
self.loss_func = finetune_loss_func
self.learning_rate = finetune_learning_rate
self.opt = finetune_opt
self.num_epochs = finetune_num_epochs
self.batch_size = finetune_batch_size
self.momentum = momentum
self.dropout = finetune_dropout
self.regtype = finetune_regtype
self.regcoef = regcoef
self.loss = Loss(self.loss_func)
self.trainer = Trainer(finetune_opt,
learning_rate=finetune_learning_rate,
momentum=momentum)
self.do_pretrain = do_pretrain
self.layers = layers
self.tied_weights = tied_weights
self.finetune_enc_act_func = expanded_args['finetune_enc_act_func']
self.finetune_dec_act_func = expanded_args['finetune_dec_act_func']
self.input_ref = None
# Model parameters
self.encoding_w_ = [] # list of matrices of encoding weights per layer
self.encoding_b_ = [] # list of arrays of encoding biases per layer
self.decoding_w = [] # list of matrices of decoding weights per layer
self.decoding_b = [] # list of arrays of decoding biases per layer
self.reconstruction = None
self.rbms = []
self.rbm_graphs = []
for l, layer in enumerate(layers):
rbm_str = 'rbm-' + str(l + 1)
new_rbm = rbm.RBM(name=self.name + '-' + rbm_str,
loss_func=expanded_args['loss_func'][l],
visible_unit_type=expanded_args['noise'][l],
stddev=stddev,
num_hidden=expanded_args['layers'][l],
learning_rate=expanded_args['learning_rate'][l],
gibbs_sampling_steps=expanded_args['gibbs_k'][l],
num_epochs=expanded_args['num_epochs'][l],
batch_size=expanded_args['batch_size'][l],
regtype=expanded_args['regtype'][l])
self.rbms.append(new_rbm)
self.rbm_graphs.append(tf.Graph())