本文整理汇总了Python中layers.logistic_sgd.LogisticRegression.negative_log_likelihood方法的典型用法代码示例。如果您正苦于以下问题：Python LogisticRegression.negative_log_likelihood方法的具体用法？Python LogisticRegression.negative_log_likelihood怎么用？Python LogisticRegression.negative_log_likelihood使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类layers.logistic_sgd.LogisticRegression的用法示例。
在下文中一共展示了LogisticRegression.negative_log_likelihood方法的14个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: DNN_2Tower
# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import negative_log_likelihood [as 别名]
#.........这里部分代码省略.........
input=layer_input,
n_in=input_size,
n_out=tower2_hidden_layers_sizes[i],
activation=T.nnet.sigmoid)
# add the layer to our list of layers
self.tower2_layers.append(layer)
self.params.extend(layer.params)
self.delta_params.extend(layer.delta_params)
for i in xrange(len(upper_hidden_layers_sizes)):
# construct the sigmoidal layer
if i == 0:
input_size = tower1_hidden_layers_sizes[-1] + tower2_hidden_layers_sizes[-1]
layer_input = T.concatenate([self.tower1_layers[-1].output, self.tower2_layers[-1].output], axis=1)
else:
input_size = upper_hidden_layers_sizes[i - 1]
layer_input = self.upper_layers[-1].output
sigmoid_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=upper_hidden_layers_sizes[i],
activation=activation)
# add the layer to our list of layers
self.upper_layers.append(sigmoid_layer)
self.params.extend(sigmoid_layer.params)
self.delta_params.extend(sigmoid_layer.delta_params)
# We now need to add a logistic layer on top of the MLP
self.logLayer = LogisticRegression(
input=self.upper_layers[-1].output,
n_in=upper_hidden_layers_sizes[-1], n_out=n_outs)
self.upper_layers.append(self.logLayer)
self.params.extend(self.logLayer.params)
self.delta_params.extend(self.logLayer.delta_params)
# construct a function that implements one step of finetunining
# compute the cost for second phase of training,
# defined as the negative log likelihood
self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
self.errors = self.logLayer.errors(self.y)
# if self.l1_reg is not None:
# for i in xrange(self.n_layers):
# W = self.params[i * 2]
# self.finetune_cost += self.l1_reg * (abs(W).sum())
# if self.l2_reg is not None:
# for i in xrange(self.n_layers):
# W = self.params[i * 2]
# self.finetune_cost += self.l2_reg * T.sqr(W).sum()
def build_finetune_functions(self, train_shared_xy, valid_shared_xy, batch_size):
(train_set_x, train_set_y) = train_shared_xy
(valid_set_x, valid_set_y) = valid_shared_xy
index = T.lscalar('index') # index to a [mini]batch
learning_rate = T.fscalar('learning_rate')
momentum = T.fscalar('momentum')
# compute the gradients with respect to the model parameters
gparams = T.grad(self.finetune_cost, self.params)
# compute list of fine-tuning updates
updates = collections.OrderedDict()
for dparam, gparam in zip(self.delta_params, gparams):
updates[dparam] = momentum * dparam - gparam*learning_rate
for dparam, param in zip(self.delta_params, self.params):
updates[param] = param + updates[dparam]
if self.max_col_norm is not None:
for i in xrange(self.n_layers):
W = self.params[i * 2]
if W in updates:
updated_W = updates[W]
col_norms = T.sqrt(T.sum(T.sqr(updated_W), axis=0))
desired_norms = T.clip(col_norms, 0, self.max_col_norm)
updates[W] = updated_W * (desired_norms / (1e-7 + col_norms))
train_fn = theano.function(inputs=[index, theano.Param(learning_rate, default = 0.0001),
theano.Param(momentum, default = 0.5)],
outputs=self.errors,
updates=updates,
givens={
self.x: train_set_x[index * batch_size:
(index + 1) * batch_size],
self.y: train_set_y[index * batch_size:
(index + 1) * batch_size]})
valid_fn = theano.function(inputs=[index],
outputs=self.errors,
givens={
self.x: valid_set_x[index * batch_size:
(index + 1) * batch_size],
self.y: valid_set_y[index * batch_size:
(index + 1) * batch_size]})
return train_fn, valid_fn示例2: DNN
# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import negative_log_likelihood [as 别名]
class DNN(object):
def __init__(self, numpy_rng, theano_rng=None,
cfg = None, # the network configuration
dnn_shared = None, shared_layers=[], input = None):
self.layers = []
self.params = []
self.delta_params = []
self.rnn_layerX = 2
print "Use DRN"
self.cfg = cfg
self.n_ins = cfg.n_ins; self.n_outs = cfg.n_outs
self.hidden_layers_sizes = cfg.hidden_layers_sizes
self.hidden_layers_number = len(self.hidden_layers_sizes)
self.activation = cfg.activation
self.do_maxout = cfg.do_maxout; self.pool_size = cfg.pool_size
self.max_col_norm = cfg.max_col_norm
self.l1_reg = cfg.l1_reg
self.l2_reg = cfg.l2_reg
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
# allocate symbolic variables for the data
if input == None:
self.x = T.matrix('x')
else:
self.x = input
self.y = T.ivector('y')
for i in xrange(self.hidden_layers_number):
# construct the hidden layer
if i == 0:
input_size = self.n_ins
layer_input = self.x
else:
input_size = self.hidden_layers_sizes[i - 1]
layer_input = self.layers[-1].output
W = None; b = None
if (i in shared_layers) :
W = dnn_shared.layers[i].W; b = dnn_shared.layers[i].b
if i == self.rnn_layerX:
hidden_layer = RnnLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=self.hidden_layers_sizes[i],
W = W, b = b,
activation=self.activation)
else:
if self.do_maxout == True:
hidden_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=self.hidden_layers_sizes[i] * self.pool_size,
W = W, b = b,
activation = (lambda x: 1.0*x),
do_maxout = True, pool_size = self.pool_size)
else:
hidden_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=self.hidden_layers_sizes[i],
W = W, b = b,
activation=self.activation)
# add the layer to our list of layers
self.layers.append(hidden_layer)
self.params.extend(hidden_layer.params)
self.delta_params.extend(hidden_layer.delta_params)
# We now need to add a logistic layer on top of the MLP
self.logLayer = LogisticRegression(
input=self.layers[-1].output,
n_in=self.hidden_layers_sizes[-1], n_out=self.n_outs)
if self.n_outs > 0:
self.layers.append(self.logLayer)
self.params.extend(self.logLayer.params)
self.delta_params.extend(self.logLayer.delta_params)
# compute the cost for second phase of training,
# defined as the negative log likelihood
self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
self.errors = self.logLayer.errors(self.y)
if self.l1_reg is not None:
for i in xrange(self.hidden_layers_number):
W = self.layers[i].W
self.finetune_cost += self.l1_reg * (abs(W).sum())
if self.l2_reg is not None:
for i in xrange(self.hidden_layers_number):
W = self.layers[i].W
self.finetune_cost += self.l2_reg * T.sqr(W).sum()
def build_finetune_functions(self, train_shared_xy, valid_shared_xy, batch_size):
#.........这里部分代码省略.........
示例3: DNN_Dropout
# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import negative_log_likelihood [as 别名]
class DNN_Dropout(object):
def __init__(self, numpy_rng, theano_rng=None,
cfg = None,
dnn_shared = None, shared_layers=[]):
self.layers = []
self.dropout_layers = []
self.params = []
self.delta_params = []
self.cfg = cfg
self.n_ins = cfg.n_ins; self.n_outs = cfg.n_outs
self.hidden_layers_sizes = cfg.hidden_layers_sizes
self.hidden_layers_number = len(self.hidden_layers_sizes)
self.activation = cfg.activation
self.do_maxout = cfg.do_maxout; self.pool_size = cfg.pool_size
self.input_dropout_factor = cfg.input_dropout_factor; self.dropout_factor = cfg.dropout_factor
self.max_col_norm = cfg.max_col_norm
self.l1_reg = cfg.l1_reg
self.l2_reg = cfg.l2_reg
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
# allocate symbolic variables for the data
self.x = T.matrix('x')
self.y = T.ivector('y')
for i in range(self.hidden_layers_number):
# construct the hidden layer
if i == 0:
input_size = self.n_ins
layer_input = self.x
if self.input_dropout_factor > 0.0:
dropout_layer_input = _dropout_from_layer(theano_rng, self.x, self.input_dropout_factor)
else:
dropout_layer_input = self.x
else:
input_size = self.hidden_layers_sizes[i - 1]
layer_input = (1 - self.dropout_factor[i - 1]) * self.layers[-1].output
dropout_layer_input = self.dropout_layers[-1].dropout_output
W = None; b = None
if (i in shared_layers) :
W = dnn_shared.layers[i].W; b = dnn_shared.layers[i].b
if self.do_maxout == False:
dropout_layer = DropoutHiddenLayer(rng=numpy_rng,
input=dropout_layer_input,
n_in=input_size,
n_out=self.hidden_layers_sizes[i],
W = W, b = b,
activation= self.activation,
dropout_factor=self.dropout_factor[i])
hidden_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=self.hidden_layers_sizes[i],
activation= self.activation,
W=dropout_layer.W, b=dropout_layer.b)
else:
dropout_layer = DropoutHiddenLayer(rng=numpy_rng,
input=dropout_layer_input,
n_in=input_size,
n_out=self.hidden_layers_sizes[i] * self.pool_size,
W = W, b = b,
activation= (lambda x: 1.0*x),
dropout_factor=self.dropout_factor[i],
do_maxout = True, pool_size = self.pool_size)
hidden_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=self.hidden_layers_sizes[i] * self.pool_size,
activation= (lambda x: 1.0*x),
W=dropout_layer.W, b=dropout_layer.b,
do_maxout = True, pool_size = self.pool_size)
# add the layer to our list of layers
self.layers.append(hidden_layer)
self.dropout_layers.append(dropout_layer)
self.params.extend(dropout_layer.params)
self.delta_params.extend(dropout_layer.delta_params)
# We now need to add a logistic layer on top of the MLP
self.dropout_logLayer = LogisticRegression(
input=self.dropout_layers[-1].dropout_output,
n_in=self.hidden_layers_sizes[-1], n_out=self.n_outs)
self.logLayer = LogisticRegression(
input=(1 - self.dropout_factor[-1]) * self.layers[-1].output,
n_in=self.hidden_layers_sizes[-1], n_out=self.n_outs,
W=self.dropout_logLayer.W, b=self.dropout_logLayer.b)
self.dropout_layers.append(self.dropout_logLayer)
self.layers.append(self.logLayer)
self.params.extend(self.dropout_logLayer.params)
self.delta_params.extend(self.dropout_logLayer.delta_params)
# compute the cost
self.finetune_cost = self.dropout_logLayer.negative_log_likelihood(self.y)
#.........这里部分代码省略.........
示例4: CNN_SAT
# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import negative_log_likelihood [as 别名]
#.........这里部分代码省略.........
input=self.ivec_layers[-1].output,
n_in=ivec_layers_sizes[-1],
n_out=n_ins,
activation=linear_func)
self.ivec_layers.append(sigmoid_layer)
if 0 in update_part:
self.params.extend(sigmoid_layer.params)
self.delta_params.extend(sigmoid_layer.delta_params)
for i in xrange(self.conv_layer_num):
if i == 0:
input = self.raw + self.ivec_layers[-1].output
else:
input = self.conv_layers[-1].output
config = conv_layer_configs[i]
conv_layer = ConvLayer(numpy_rng=numpy_rng, input=input,
input_shape = config['input_shape'], filter_shape = config['filter_shape'], poolsize = config['poolsize'],
activation = conv_activation, flatten = config['flatten'], use_fast = use_fast)
self.conv_layers.append(conv_layer)
if 1 in update_part:
self.params.extend(conv_layer.params)
self.delta_params.extend(conv_layer.delta_params)
self.conv_output_dim = config['output_shape'][1] * config['output_shape'][2] * config['output_shape'][3]
for i in xrange(self.full_layer_num):
# construct the sigmoidal layer
if i == 0:
input_size = self.conv_output_dim
layer_input = self.conv_layers[-1].output
else:
input_size = hidden_layers_sizes[i - 1]
layer_input = self.full_layers[-1].output
sigmoid_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i],
activation=full_activation)
# add the layer to our list of layers
self.full_layers.append(sigmoid_layer)
if 1 in update_part:
self.params.extend(sigmoid_layer.params)
self.delta_params.extend(sigmoid_layer.delta_params)
# We now need to add a logistic layer on top of the MLP
self.logLayer = LogisticRegression(
input=self.full_layers[-1].output,
n_in=hidden_layers_sizes[-1], n_out=n_outs)
self.full_layers.append(self.logLayer)
if 1 in update_part:
self.params.extend(self.logLayer.params)
self.delta_params.extend(self.logLayer.delta_params)
self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
self.errors = self.logLayer.errors(self.y)
def kl_divergence(self, p, p_hat):
return p * T.log(p / p_hat) + (1 - p) * T.log((1 - p) / (1 - p_hat))
def build_finetune_functions(self, train_shared_xy, valid_shared_xy, batch_size):
(train_set_x, train_set_y) = train_shared_xy
(valid_set_x, valid_set_y) = valid_shared_xy
index = T.lscalar('index') # index to a [mini]batch
learning_rate = T.fscalar('learning_rate')
momentum = T.fscalar('momentum')
# compute the gradients with respect to the model parameters
gparams = T.grad(self.finetune_cost, self.params)
# compute list of fine-tuning updates
updates = {}
for dparam, gparam in zip(self.delta_params, gparams):
updates[dparam] = momentum * dparam - gparam*learning_rate
for dparam, param in zip(self.delta_params, self.params):
updates[param] = param + updates[dparam]
train_fn = theano.function(inputs=[index, theano.Param(learning_rate, default = 0.0001),
theano.Param(momentum, default = 0.5)],
outputs=self.errors,
updates=updates,
givens={
self.x: train_set_x[index * batch_size:
(index + 1) * batch_size],
self.y: train_set_y[index * batch_size:
(index + 1) * batch_size]})
valid_fn = theano.function(inputs=[index],
outputs=self.errors,
givens={
self.x: valid_set_x[index * batch_size:
(index + 1) * batch_size],
self.y: valid_set_y[index * batch_size:
(index + 1) * batch_size]})
return train_fn, valid_fn示例5: __init__
# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import negative_log_likelihood [as 别名]
def __init__(self, numpy_rng, theano_rng=None,
cfg = None, # the network configuration
dnn_shared = None, shared_layers=[], input = None):
self.cfg = cfg
self.params = []
self.delta_params = []
self.n_ins = cfg.n_ins; self.n_outs = cfg.n_outs
self.l1_reg = cfg.l1_reg
self.l2_reg = cfg.l2_reg
self.do_maxout = cfg.do_maxout; self.pool_size = cfg.pool_size
self.max_col_norm = cfg.max_col_norm
print self.max_col_norm
self.layers = []
self.lstm_layers = []
self.fc_layers = []
# 1. lstm
self.lstm_layers_sizes = cfg.lstm_layers_sizes
self.lstm_layers_number = len(self.lstm_layers_sizes)
# 2. dnn
self.hidden_layers_sizes = cfg.hidden_layers_sizes
self.hidden_layers_number = len(self.hidden_layers_sizes)
self.activation = cfg.activation
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
if input == None:
self.x = T.matrix('x')
else:
self.x = input
self.y = T.ivector('y')
#######################
# build lstm layers #
#######################
print '1. start to build lstm layer: '+ str(self.lstm_layers_number)
for i in xrange(self.lstm_layers_number):
if i == 0:
input_size = self.n_ins
input = self.x
else:
input_size = self.lstm_layers_sizes[i - 1]
input = self.layers[-1].output
lstm_layer = LSTMLayer(rng=numpy_rng, input=input, n_in=input_size, n_out=self.lstm_layers_sizes[i])
print '\tbuild lstm layer: ' + str(input_size) +' x '+ str(lstm_layer.n_out)
self.layers.append(lstm_layer)
self.lstm_layers.append(lstm_layer)
self.params.extend(lstm_layer.params)
self.delta_params.extend(lstm_layer.delta_params)
print '1. finish lstm layer: '+ str(self.layers[-1].n_out)
#######################
# build dnnv layers #
#######################
#print '2. start to build dnnv layer: '+ str(self.hidden_layers_number)
#for i in xrange(self.hidden_layers_number):
# if i == 0:
# input_size = self.layers[-1].n_out
# else:
# input_size = self.hidden_layers_sizes[i - 1]
# input = self.layers[-1].output
# fc_layer = HiddenLayer(rng=numpy_rng, input=input, n_in=input_size, n_out=self.hidden_layers_sizes[i], activation=self.activation)
# print '\tbuild dnnv layer: ' + str(input_size) +' x '+ str(fc_layer.n_out)
# self.layers.append(fc_layer)
# self.fc_layers.append(fc_layer)
# self.params.extend(fc_layer.params)
# self.delta_params.extend(fc_layer.delta_params)
#print '2. finish dnnv layer: '+ str(self.layers[-1].n_out)
#######################
# build log layers #
#######################
print '3. start to build log layer: 1'
input_size = self.layers[-1].n_out
input = self.layers[-1].output
logLayer = LogisticRegression(input=input, n_in=input_size, n_out=self.n_outs)
print '\tbuild final layer: ' + str(input_size) +' x '+ str(self.n_outs)
self.layers.append(logLayer)
self.params.extend(logLayer.params)
self.delta_params.extend(logLayer.delta_params)
print '3. finish log layer: '+ str(self.layers[-1].n_out)
print 'Total layers: '+ str(len(self.layers))
sys.stdout.flush()
self.finetune_cost = logLayer.negative_log_likelihood(self.y)
self.errors = logLayer.errors(self.y)示例6: CNN
# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import negative_log_likelihood [as 别名]
class CNN(object):
def __init__(self, numpy_rng, theano_rng=None,
batch_size = 256, n_outs=500,
sparsity = None, sparsity_weight = None, sparse_layer = 3,
conv_layer_configs = [],
hidden_layers_sizes=[500, 500],
conv_activation = T.nnet.sigmoid,
full_activation = T.nnet.sigmoid,
use_fast = False):
self.layers = []
self.params = []
self.delta_params = []
self.sparsity = sparsity
self.sparsity_weight = sparsity_weight
self.sparse_layer = sparse_layer
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
# allocate symbolic variables for the data
self.x = T.matrix('x')
self.y = T.ivector('y')
self.conv_layer_num = len(conv_layer_configs)
self.full_layer_num = len(hidden_layers_sizes)
for i in xrange(self.conv_layer_num):
if i == 0:
input = self.x
is_input_layer = True
else:
input = self.layers[-1].output
is_input_layer = False
config = conv_layer_configs[i]
conv_layer = ConvLayer(numpy_rng=numpy_rng, input=input, is_input_layer = is_input_layer,
input_shape = config['input_shape'], filter_shape = config['filter_shape'], poolsize = config['poolsize'],
activation = conv_activation, flatten = config['flatten'])
self.layers.append(conv_layer)
self.params.extend(conv_layer.params)
self.delta_params.extend(conv_layer.delta_params)
self.conv_output_dim = config['output_shape'][1] * config['output_shape'][2] * config['output_shape'][3]
for i in xrange(self.full_layer_num):
# construct the sigmoidal layer
if i == 0:
input_size = self.conv_output_dim
else:
input_size = hidden_layers_sizes[i - 1]
layer_input = self.layers[-1].output
sigmoid_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i],
activation=full_activation)
# add the layer to our list of layers
self.layers.append(sigmoid_layer)
self.params.extend(sigmoid_layer.params)
self.delta_params.extend(sigmoid_layer.delta_params)
# We now need to add a logistic layer on top of the MLP
self.logLayer = LogisticRegression(
input=self.layers[-1].output,
n_in=hidden_layers_sizes[-1], n_out=n_outs)
self.layers.append(self.logLayer)
self.params.extend(self.logLayer.params)
self.delta_params.extend(self.logLayer.delta_params)
if self.sparsity_weight is not None:
sparsity_level = T.extra_ops.repeat(self.sparsity, 630)
avg_act = self.sigmoid_layers[sparse_layer].output.mean(axis=0)
kl_div = self.kl_divergence(sparsity_level, avg_act)
self.finetune_cost = self.logLayer.negative_log_likelihood(self.y) + self.sparsity_weight * kl_div.sum()
else:
self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
self.errors = self.logLayer.errors(self.y)
def kl_divergence(self, p, p_hat):
return p * T.log(p / p_hat) + (1 - p) * T.log((1 - p) / (1 - p_hat))
def build_finetune_functions(self, train_shared_xy, valid_shared_xy, batch_size):
(train_set_x, train_set_y) = train_shared_xy
(valid_set_x, valid_set_y) = valid_shared_xy
index = T.lscalar('index') # index to a [mini]batch
learning_rate = T.fscalar('learning_rate')
momentum = T.fscalar('momentum')
# compute the gradients with respect to the model parameters
gparams = T.grad(self.finetune_cost, self.params)
# compute list of fine-tuning updates
updates = {}
for dparam, gparam in zip(self.delta_params, gparams):
#.........这里部分代码省略.........
示例7: __init__
# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import negative_log_likelihood [as 别名]
def __init__(self, numpy_rng, theano_rng=None,
cfg = None, # the network configuration
dnn_shared = None, shared_layers=[], input = None, draw=None):
self.cfg = cfg
self.params = []
self.delta_params = []
self.n_ins = cfg.n_ins; self.n_outs = cfg.n_outs
self.l1_reg = cfg.l1_reg
self.l2_reg = cfg.l2_reg
self.do_maxout = cfg.do_maxout; self.pool_size = cfg.pool_size
self.max_col_norm = 1
print self.max_col_norm
self.layers = []
self.lstm_layers = []
self.fc_layers = []
self.bilayers = []
# 1. lstm
self.lstm_layers_sizes = cfg.lstm_layers_sizes
self.lstm_layers_number = len(self.lstm_layers_sizes)
# 2. dnn
self.hidden_layers_sizes = cfg.hidden_layers_sizes
self.hidden_layers_number = len(self.hidden_layers_sizes)
self.activation = cfg.activation
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
if input == None:
self.x = T.matrix('x')
else:
self.x = input
self.y = T.ivector('y')
#######################
# build lstm layers #
#######################
print '1. start to build AttendLSTMLayer : '+ str(self.lstm_layers_number) + ', n_attendout: '+ str(cfg.batch_size)
for i in xrange(1):
if i == 0:
input_size = self.n_ins
input = self.x
else:
input_size = self.lstm_layers_sizes[i - 1]
input = self.bilayers[-1].output
# Forward
f_lstm_layer = AttendLSTMLayer(rng=numpy_rng, input=input, n_in=input_size, n_out=self.lstm_layers_sizes[i],
steps=cfg.batch_size, draw=draw)
print '\tbuild f_lstm layer: ' + str(input_size) +' x '+ str(f_lstm_layer.n_out)
self.layers.append(f_lstm_layer)
self.lstm_layers.append(f_lstm_layer)
self.params.extend(f_lstm_layer.params)
self.delta_params.extend(f_lstm_layer.delta_params)
# Backward
b_lstm_layer = AttendLSTMLayer(rng=numpy_rng, input=input, n_in=input_size, n_out=self.lstm_layers_sizes[i], backwards=True,
steps=cfg.batch_size, draw=draw)
print '\tbuild b_lstm layer: ' + str(input_size) +' x '+ str(b_lstm_layer.n_out)
self.layers.append(b_lstm_layer)
self.lstm_layers.append(b_lstm_layer)
self.params.extend(b_lstm_layer.params)
self.delta_params.extend(b_lstm_layer.delta_params)
# Sum forward + backward
bi_layer = SUMLayer(finput=f_lstm_layer.output,binput=b_lstm_layer.output[::-1], n_out=self.lstm_layers_sizes[i - 1])
self.bilayers.append(bi_layer)
print '\tbuild sum layer: ' + str(input_size) +' x '+ str(bi_layer.n_out)
print '1. finish AttendLSTMLayer: '+ str(self.bilayers[-1].n_out)
#######################
# build log layers #
#######################
print '3. start to build log layer: 1'
input_size = self.bilayers[-1].n_out
input = self.bilayers[-1].output
logLayer = LogisticRegression(input=input, n_in=input_size, n_out=self.n_outs)
print '\tbuild final layer: ' + str(input_size) +' x '+ str(self.n_outs)
self.layers.append(logLayer)
self.params.extend(logLayer.params)
self.delta_params.extend(logLayer.delta_params)
print '3. finish log layer: '+ str(self.bilayers[-1].n_out)
print 'Total layers: '+ str(len(self.layers))
sys.stdout.flush()
self.finetune_cost = logLayer.negative_log_likelihood(self.y)
self.errors = logLayer.errors(self.y)示例8: DBN
# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import negative_log_likelihood [as 别名]
#.........这里部分代码省略.........
activation=activation)
# add the layer to our list of layers
self.layers.append(sigmoid_layer)
# the parameters of the sigmoid_layers are parameters of the DBN.
# The visible biases in the RBM are parameters of those RBMs,
# but not of the DBN.
self.params.extend(sigmoid_layer.params)
self.delta_params.extend(sigmoid_layer.delta_params)
# Construct an RBM that shared weights with this layer
# the first layer could be Gaussian-Bernoulli RBM
# other layers are Bernoulli-Bernoulli RBMs
if i == 0 and first_layer_gb:
rbm_layer = GBRBM(numpy_rng=numpy_rng,
theano_rng=theano_rng,
input=layer_input,
n_visible=input_size,
n_hidden=hidden_layers_sizes[i],
W=sigmoid_layer.W,
hbias=sigmoid_layer.b)
else:
rbm_layer = RBM(numpy_rng=numpy_rng,
theano_rng=theano_rng,
input=layer_input,
n_visible=input_size,
n_hidden=hidden_layers_sizes[i],
W=sigmoid_layer.W,
hbias=sigmoid_layer.b)
self.rbm_layers.append(rbm_layer)
# We now need to add a logistic layer on top of the MLP
self.logLayer = LogisticRegression(
input=self.layers[-1].output,
n_in=hidden_layers_sizes[-1],
n_out=n_outs)
self.layers.append(self.logLayer)
self.params.extend(self.logLayer.params)
self.delta_params.extend(self.logLayer.delta_params)
# compute the cost for second phase of training, defined as the
# negative log likelihood of the logistic regression (output) layer
self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
# compute the gradients with respect to the model parameters
# symbolic variable that points to the number of errors made on the
# minibatch given by self.x and self.y
self.errors = self.logLayer.errors(self.y)
self.output = self.logLayer.prediction();
self.features = self.layers[-2].output;
self.features_dim = self.layers[-2].n_out
def pretraining_functions(self, train_set_x, batch_size, weight_cost):
'''Generates a list of functions, for performing one step of
gradient descent at a given layer. The function will require
as input the minibatch index, and to train an RBM you just
need to iterate, calling the corresponding function on all
minibatch indexes.
:type train_set_x: theano.tensor.TensorType
:param train_set_x: Shared var. that contains all datapoints used
for training the RBM
:type batch_size: int
:param batch_size: size of a [mini]batch
:param weight_cost: weigth cost
'''
# index to a [mini]batch
index = T.lscalar('index') # index to a minibatch
momentum = T.scalar('momentum')
learning_rate = T.scalar('lr') # learning rate to use
# number of batches
n_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
# begining of a batch, given `index`
batch_begin = index * batch_size
# ending of a batch given `index`
batch_end = batch_begin + batch_size
pretrain_fns = []
for rbm in self.rbm_layers:
# get the cost and the updates list
# using CD-k here (persisent=None,k=1) for training each RBM.
r_cost, fe_cost, updates = rbm.get_cost_updates(batch_size, learning_rate,
momentum, weight_cost)
# compile the theano function
fn = theano.function(inputs=[index,
theano.Param(learning_rate, default=0.0001),
theano.Param(momentum, default=0.5)],
outputs= [r_cost, fe_cost],
updates=updates,
givens={self.x: train_set_x[batch_begin:batch_end]})
# append function to the list of functions
pretrain_fns.append(fn)
return pretrain_fns示例9: DNN
# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import negative_log_likelihood [as 别名]
class DNN(nnet):
def __init__(self, numpy_rng, theano_rng=None, n_ins=784,
hidden_layers_sizes=[500, 500], n_outs=10,
activation = T.nnet.sigmoid, adv_activation = None,
max_col_norm = None, l1_reg = None, l2_reg = None):
super(DNN, self).__init__()
self.layers = []
self.n_layers = len(hidden_layers_sizes)
self.max_col_norm = max_col_norm
self.l1_reg = l1_reg
self.l2_reg = l2_reg
assert self.n_layers > 0
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
# allocate symbolic variables for the data
self.x = T.matrix('x')
self.y = T.ivector('y')
for i in xrange(self.n_layers):
# construct the sigmoidal layer
if i == 0:
input_size = n_ins
layer_input = self.x
else:
input_size = hidden_layers_sizes[i - 1]
layer_input = self.layers[-1].output
if not adv_activation is None:
sigmoid_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i] * pool_size,
activation = activation,
adv_activation_method = adv_activation['method'],
pool_size = adv_activation['pool_size'],
pnorm_order = adv_activation['pnorm_order'])
else:
sigmoid_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i],
activation=activation)
# add the layer to our list of layers
self.layers.append(sigmoid_layer)
self.params.extend(sigmoid_layer.params)
self.delta_params.extend(sigmoid_layer.delta_params)
# We now need to add a logistic layer on top of the MLP
self.logLayer = LogisticRegression(
input=self.layers[-1].output,
n_in=hidden_layers_sizes[-1], n_out=n_outs)
self.layers.append(self.logLayer)
self.params.extend(self.logLayer.params)
self.delta_params.extend(self.logLayer.delta_params)
# construct a function that implements one step of finetunining
# compute the cost for second phase of training,
# defined as the negative log likelihood
self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
self.errors = self.logLayer.errors(self.y)
if self.l1_reg is not None:
self.__l1Regularization__();
if self.l2_reg is not None:
self.__l2Regularization__();
self.output = self.logLayer.prediction();
self.features = self.layers[-2].output;
self.features_dim = self.layers[-2].n_out示例10: DNN_Dropout
# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import negative_log_likelihood [as 别名]
class DNN_Dropout(object):
def __init__(self, numpy_rng, theano_rng=None, n_ins=784,
hidden_layers_sizes=[500, 500], n_outs=10,
activation = T.nnet.sigmoid,
input_dropout_factor = 0,
dropout_factor = [0.2,0.2,0.2,0.2,0.2,0.2,0.2],
do_maxout = False, pool_size = 1,
max_col_norm = None, l1_reg = None, l2_reg = None):
self.sigmoid_layers = []
self.dropout_layers = []
self.params = []
self.delta_params = []
self.n_layers = len(hidden_layers_sizes)
self.max_col_norm = max_col_norm
self.l1_reg = l1_reg
self.l2_reg = l2_reg
self.input_dropout_factor = input_dropout_factor
self.dropout_factor = dropout_factor
assert self.n_layers > 0
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
# allocate symbolic variables for the data
self.x = T.matrix('x')
self.y = T.ivector('y')
for i in xrange(self.n_layers):
# construct the sigmoidal layer
if i == 0:
input_size = n_ins
layer_input = self.x
if input_dropout_factor > 0.0:
dropout_layer_input = _dropout_from_layer(theano_rng, self.x, input_dropout_factor)
else:
dropout_layer_input = self.x
else:
input_size = hidden_layers_sizes[i - 1]
layer_input = (1 - self.dropout_factor[i - 1]) * self.sigmoid_layers[-1].output
dropout_layer_input = self.dropout_layers[-1].dropout_output
if do_maxout == False:
dropout_layer = DropoutHiddenLayer(rng=numpy_rng,
input=dropout_layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i],
activation= activation,
dropout_factor=self.dropout_factor[i])
sigmoid_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i],
activation=activation,
W=dropout_layer.W, b=dropout_layer.b)
else:
dropout_layer = DropoutHiddenLayer(rng=numpy_rng,
input=dropout_layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i] * pool_size,
activation= (lambda x: 1.0*x),
dropout_factor=self.dropout_factor[i],
do_maxout = True, pool_size = pool_size)
sigmoid_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i] * pool_size,
activation= (lambda x: 1.0*x),
W=dropout_layer.W, b=dropout_layer.b,
do_maxout = True, pool_size = pool_size)
# add the layer to our list of layers
self.sigmoid_layers.append(sigmoid_layer)
self.dropout_layers.append(dropout_layer)
self.params.extend(dropout_layer.params)
self.delta_params.extend(dropout_layer.delta_params)
# We now need to add a logistic layer on top of the MLP
self.dropout_logLayer = LogisticRegression(
input=self.dropout_layers[-1].dropout_output,
n_in=hidden_layers_sizes[-1], n_out=n_outs)
self.logLayer = LogisticRegression(
input=(1 - self.dropout_factor[-1]) * self.sigmoid_layers[-1].output,
n_in=hidden_layers_sizes[-1], n_out=n_outs,
W=self.dropout_logLayer.W, b=self.dropout_logLayer.b)
self.dropout_layers.append(self.dropout_logLayer)
self.sigmoid_layers.append(self.logLayer)
self.params.extend(self.dropout_logLayer.params)
self.delta_params.extend(self.dropout_logLayer.delta_params)
# compute the cost
self.finetune_cost = self.dropout_logLayer.negative_log_likelihood(self.y)
self.errors = self.logLayer.errors(self.y)
if self.l1_reg is not None:
for i in xrange(self.n_layers):
W = self.params[i * 2]
#.........这里部分代码省略.........
示例11: DropoutCNN
# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import negative_log_likelihood [as 别名]
class DropoutCNN(CNNBase):
""" Instantiation of Convolution neural network ... """
def __init__(self, numpy_rng, theano_rng, batch_size, n_outs,conv_layer_configs, hidden_layer_configs,
use_fast=False,conv_activation = T.nnet.sigmoid,hidden_activation = T.nnet.sigmoid,
l1_reg=None,l2_reg=None,max_col_norm=None):
super(DropoutCNN, self).__init__(conv_layer_configs,hidden_layer_configs,l1_reg,l2_reg,max_col_norm)
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
for i in xrange(self.conv_layer_num): # construct the convolution layer
if i == 0: #is_input layer
input = self.x
is_input_layer = True
else:
input = self.layers[-1].output #output of previous layer
is_input_layer = False
config = conv_layer_configs[i]
conv_layer = ConvLayer(numpy_rng=numpy_rng, input=input,input_shape=config['input_shape'],
filter_shape=config['filter_shape'],poolsize=config['poolsize'],
activation = conv_activation, use_fast = use_fast)
self.layers.append(conv_layer)
self.conv_layers.append(conv_layer)
if config['update']==True: # only few layers of convolution layer are considered for updation
self.params.extend(conv_layer.params)
self.delta_params.extend(conv_layer.delta_params)
hidden_layers = hidden_layer_configs['hidden_layers'];
self.conv_output_dim = config['output_shape'][1] * config['output_shape'][2] * config['output_shape'][3]
adv_activation_configs = hidden_layer_configs['adv_activation']
#flattening the last convolution output layer
self.features = self.conv_layers[-1].output.flatten(2);
self.features_dim = self.conv_output_dim;
self.dropout_layers = [];
self.dropout_factor = hidden_layer_configs['dropout_factor'];
self.input_dropout_factor = hidden_layer_configs['input_dropout_factor'];
for i in xrange(self.hidden_layer_num): # construct the hidden layer
if i == 0: # is first sigmoidla layer
input_size = self.conv_output_dim
if self.dropout_factor[i] > 0.0:
dropout_layer_input = _dropout_from_layer(theano_rng, self.layers[-1].output, self.input_dropout_factor)
else:
dropout_layer_input = self.features
layer_input = self.features
else:
input_size = hidden_layers[i - 1] # number of hidden neurons in previous layers
dropout_layer_input = self.dropout_layers[-1].dropout_output
layer_input = (1 - self.dropout_factor[i-1]) * self.layers[-1].output
if adv_activation_configs is None:
dropout_sigmoid_layer = DropoutHiddenLayer(rng=numpy_rng, input=layer_input,n_in=input_size,
n_out = hidden_layers[i], activation=hidden_activation,
dropout_factor = self.dropout_factor[i]);
sigmoid_layer = HiddenLayer(rng=numpy_rng, input=layer_input,n_in=input_size,
n_out = hidden_layers[i], activation=hidden_activation,
W=dropout_sigmoid_layer.W, b=dropout_sigmoid_layer.b);
else:
dropout_sigmoid_layer = DropoutHiddenLayer(rng=numpy_rng, input=layer_input,n_in=input_size,
n_out = hidden_layers[i]*adv_activation_configs['pool_size'], activation=hidden_activation,
adv_activation_method = adv_activation_configs['method'],
pool_size = adv_activation_configs['pool_size'],
pnorm_order = adv_activation_configs['pnorm_order'],
dropout_factor = self.dropout_factor[i]);
sigmoid_layer = HiddenLayer(rng=numpy_rng, input=layer_input,n_in=input_size,
n_out = hidden_layers[i]*adv_activation_configs['pool_size'], activation=hidden_activation,
adv_activation_method = adv_activation_configs['method'],
pool_size = adv_activation_configs['pool_size'],
pnorm_order = adv_activation_configs['pnorm_order'],
W=dropout_sigmoid_layer.W, b=dropout_sigmoid_layer.b);
self.layers.append(sigmoid_layer)
self.dropout_layers.append(dropout_sigmoid_layer)
self.mlp_layers.append(sigmoid_layer)
if config['update']==True: # only few layers of hidden layer are considered for updation
self.params.extend(dropout_sigmoid_layer.params)
self.delta_params.extend(dropout_sigmoid_layer.delta_params)
self.dropout_logLayer = LogisticRegression(input=self.dropout_layers[-1].dropout_output,n_in=hidden_layers[-1],n_out=n_outs)
self.logLayer = LogisticRegression(
input=(1 - self.dropout_factor[-1]) * self.layers[-1].output,
n_in=hidden_layers[-1],n_out=n_outs,
W=self.dropout_logLayer.W, b=self.dropout_logLayer.b)
self.dropout_layers.append(self.dropout_logLayer)
self.layers.append(self.logLayer)
self.params.extend(self.dropout_logLayer.params)
self.delta_params.extend(self.dropout_logLayer.delta_params)
self.finetune_cost = self.dropout_logLayer.negative_log_likelihood(self.y)
self.errors = self.logLayer.errors(self.y)
#.........这里部分代码省略.........
示例12: DNN_Dropout
# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import negative_log_likelihood [as 别名]
class DNN_Dropout(nnet):
def __init__(self, numpy_rng, theano_rng=None, n_ins=784,
hidden_layers_sizes=[500, 500], n_outs=10,
activation = T.nnet.sigmoid, input_dropout_factor = 0,
dropout_factor = [0.2,0.2,0.2,0.2,0.2,0.2,0.2],
adv_activation = None, max_col_norm = None,
l1_reg = None, l2_reg = None):
super(DNN_Dropout, self).__init__()
self.layers = []
self.dropout_layers = []
self.n_layers = len(hidden_layers_sizes)
self.max_col_norm = max_col_norm
self.l1_reg = l1_reg
self.l2_reg = l2_reg
self.input_dropout_factor = input_dropout_factor
self.dropout_factor = dropout_factor
assert self.n_layers > 0
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
# allocate symbolic variables for the data
self.x = T.matrix('x')
self.y = T.ivector('y')
for i in xrange(self.n_layers):
# construct the sigmoidal layer
if i == 0:
input_size = n_ins
layer_input = self.x
if input_dropout_factor > 0.0:
dropout_layer_input = _dropout_from_layer(theano_rng, self.x, input_dropout_factor)
else:
dropout_layer_input = self.x
else:
input_size = hidden_layers_sizes[i - 1]
layer_input = (1 - self.dropout_factor[i - 1]) * self.layers[-1].output
dropout_layer_input = self.dropout_layers[-1].dropout_output
if not adv_activation is None:
dropout_layer = DropoutHiddenLayer(rng=numpy_rng,
input=dropout_layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i] * adv_activation['pool_size'],
activation= activation,
adv_activation_method = adv_activation['method'],
pool_size = adv_activation['pool_size'],
pnorm_order = adv_activation['pnorm_order'],
dropout_factor=self.dropout_factor[i])
sigmoid_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i] * adv_activation['pool_size'],
activation=activation,
adv_activation_method = adv_activation['method'],
pool_size = adv_activation['pool_size'],
pnorm_order = adv_activation['pnorm_order'],
W=dropout_layer.W, b=dropout_layer.b)
else:
dropout_layer = DropoutHiddenLayer(rng=numpy_rng,
input=dropout_layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i],
activation= activation,
dropout_factor=self.dropout_factor[i])
sigmoid_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i] ,
activation= activation,
W=dropout_layer.W, b=dropout_layer.b)
# add the layer to our list of layers
self.layers.append(sigmoid_layer)
self.dropout_layers.append(dropout_layer)
self.params.extend(dropout_layer.params)
self.delta_params.extend(dropout_layer.delta_params)
# We now need to add a logistic layer on top of the MLP
self.dropout_logLayer = LogisticRegression(
input=self.dropout_layers[-1].dropout_output,
n_in=hidden_layers_sizes[-1], n_out=n_outs)
self.logLayer = LogisticRegression(
input=(1 - self.dropout_factor[-1]) * self.layers[-1].output,
n_in=hidden_layers_sizes[-1], n_out=n_outs,
W=self.dropout_logLayer.W, b=self.dropout_logLayer.b)
self.dropout_layers.append(self.dropout_logLayer)
self.layers.append(self.logLayer)
self.params.extend(self.dropout_logLayer.params)
self.delta_params.extend(self.dropout_logLayer.delta_params)
# compute the cost
self.finetune_cost = self.dropout_logLayer.negative_log_likelihood(self.y)
#.........这里部分代码省略.........
示例13: DNN_SAT
# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import negative_log_likelihood [as 别名]
#.........这里部分代码省略.........
self.ivec_params.extend(ivec_layer.params)
self.ivec_delta_params.extend(ivec_layer.delta_params)
# the final output layer which has the same dimension as the input features
linear_func = lambda x: x
ivec_layer = HiddenLayer(rng=numpy_rng,
input=self.ivec_layers[-1].output,
n_in=ivec_layers_sizes[-1],
n_out=n_ins,
activation=linear_func)
self.ivec_layers.append(ivec_layer)
self.ivec_params.extend(ivec_layer.params)
self.ivec_delta_params.extend(ivec_layer.delta_params)
# construct the DNN (canonical model)
for i in xrange(self.n_layers):
if i == 0:
input_size = n_ins
layer_input = self.raw + self.ivec_layers[-1].output
else:
input_size = hidden_layers_sizes[i - 1]
layer_input = self.sigmoid_layers[-1].output
if do_maxout == True:
sigmoid_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i] * pool_size,
activation = (lambda x: 1.0*x),
do_maxout = True, pool_size = pool_size)
elif do_pnorm == True:
sigmoid_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i] * pool_size,
activation = (lambda x: 1.0*x),
do_pnorm = True, pool_size = pool_size, pnorm_order = pnorm_order)
else:
sigmoid_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i],
activation=activation)
# add the layer to our list of layers
self.sigmoid_layers.append(sigmoid_layer)
self.sigmoid_params.extend(sigmoid_layer.params)
self.sigmoid_delta_params.extend(sigmoid_layer.delta_params)
# We now need to add a logistic layer on top of the MLP
self.logLayer = LogisticRegression(
input=self.sigmoid_layers[-1].output,
n_in=hidden_layers_sizes[-1], n_out=n_outs)
self.sigmoid_layers.append(self.logLayer)
self.sigmoid_params.extend(self.logLayer.params)
self.sigmoid_delta_params.extend(self.logLayer.delta_params)
# construct a function that implements one step of finetunining
# compute the cost for second phase of training,
# defined as the negative log likelihood
self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
self.errors = self.logLayer.errors(self.y)
def build_finetune_functions(self, train_shared_xy, valid_shared_xy, batch_size):
(train_set_x, train_set_y) = train_shared_xy
(valid_set_x, valid_set_y) = valid_shared_xy
index = T.lscalar('index') # index to a [mini]batch
learning_rate = T.fscalar('learning_rate')
momentum = T.fscalar('momentum')
# compute the gradients with respect to the model parameters
gparams = T.grad(self.finetune_cost, self.params)
# compute list of fine-tuning updates
updates = {}
for dparam, gparam in zip(self.delta_params, gparams):
updates[dparam] = momentum * dparam - gparam*learning_rate
for dparam, param in zip(self.delta_params, self.params):
updates[param] = param + updates[dparam]
train_fn = theano.function(inputs=[index, theano.Param(learning_rate, default = 0.0001),
theano.Param(momentum, default = 0.5)],
outputs=self.errors,
updates=updates,
givens={
self.x: train_set_x[index * batch_size:
(index + 1) * batch_size],
self.y: train_set_y[index * batch_size:
(index + 1) * batch_size]})
valid_fn = theano.function(inputs=[index],
outputs=self.errors,
givens={
self.x: valid_set_x[index * batch_size:
(index + 1) * batch_size],
self.y: valid_set_y[index * batch_size:
(index + 1) * batch_size]})
return train_fn, valid_fn示例14: SDA
# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import negative_log_likelihood [as 别名]
#.........这里部分代码省略.........
# the input to this layer is either the activation of the hidden
# layer below or the input of the SdA if you are on the first
# layer
if i == 0:
layer_input = self.x
else:
layer_input = self.layers[-1].output
sigmoid_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i],
activation=T.nnet.sigmoid)
# add the layer to our list of layers
self.layers.append(sigmoid_layer)
# its arguably a philosophical question...
# but we are going to only declare that the parameters of the
# sigmoid_layers are parameters of the StackedDAA
# the visible biases in the dA are parameters of those
# dA, but not the SdA
self.params.extend(sigmoid_layer.params)
self.delta_params.extend(sigmoid_layer.delta_params)
# Construct a denoising autoencoder that shared weights with this
# layer
dA_layer = dA(numpy_rng=numpy_rng,
theano_rng=theano_rng,
input=layer_input,
n_visible=input_size,
n_hidden=hidden_layers_sizes[i],
W=sigmoid_layer.W,
bhid=sigmoid_layer.b,
activation=T.nnet.sigmoid)
self.dA_layers.append(dA_layer)
# We now need to add a logistic layer on top of the MLP
self.logLayer = LogisticRegression(
input=self.layers[-1].output,
n_in=hidden_layers_sizes[-1], n_out=n_outs)
self.layers.append(self.logLayer)
self.params.extend(self.logLayer.params)
self.delta_params.extend(self.logLayer.delta_params)
# construct a function that implements one step of finetunining
# compute the cost for second phase of training,
# defined as the negative log likelihood
self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
# compute the gradients with respect to the model parameters
# symbolic variable that points to the number of errors made on the
# minibatch given by self.x and self.y
self.errors = self.logLayer.errors(self.y)
self.output = self.logLayer.prediction();
self.features = self.layers[-2].output;
self.features_dim = self.layers[-2].n_out
def pretraining_functions(self, train_x, batch_size):
''' Generates a list of functions, each of them implementing one
step in trainnig the dA corresponding to the layer with same index.
The function will require as input the minibatch index, and to train
a dA you just need to iterate, calling the corresponding function on
all minibatch indexes.
:type train_x: theano.tensor.TensorType
:param train_x: Shared variable that contains all datapoints used
for training the dA
:type batch_size: int
:param batch_size: size of a [mini]batch
'''
# index to a [mini]batch
index = T.lscalar('index') # index to a minibatch
corruption_level = T.scalar('corruption') # % of corruption to use
learning_rate = T.scalar('lr') # learning rate to use
# number of batches
n_batches = train_x.get_value(borrow=True).shape[0] / batch_size
# begining of a batch, given `index`
batch_begin = index * batch_size
# ending of a batch given `index`
batch_end = batch_begin + batch_size
pretrain_fns = []
for dA in self.dA_layers:
# get the cost and the updates list
cost, updates = dA.get_cost_updates(corruption_level,
learning_rate)
# compile the theano function
fn = theano.function(inputs=[index,
theano.Param(corruption_level, default=0.2),
theano.Param(learning_rate, default=0.1)],
outputs=cost,
updates=updates,
givens={self.x: train_x[batch_begin:
batch_end]})
# append `fn` to the list of functions
pretrain_fns.append(fn)
return pretrain_fns