def pretraining_functions(self, train_set_x, batch_size):
# 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
# 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, corruption_level, learning_rate
# theano.In(corruption_level, value=0.2),
# theano.In(learning_rate, value=0.1)
],
outputs=cost,
updates=updates,
givens={self.x: train_set_x[batch_begin:batch_end]})
# append `fn` to the list of functions
pretrain_fns.append(fn)
return pretrain_fns
def pretraining_functions(self, train_set_x, batch_size):
# 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
# 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,
corruption_level,
learning_rate
# theano.In(corruption_level, value=0.2),
# theano.In(learning_rate, value=0.1)
],
outputs=cost,
updates=updates,
givens={
self.x: train_set_x[batch_begin: batch_end]
}
)
# append `fn` to the list of functions
pretrain_fns.append(fn)
return pretrain_fns
def pretraining_functions(self, train_set_x, batch_size):
""" 各レイヤーのAutoEncoderによる学習 """
# minibatchのindex
index = T.lscalar('index')
# ノイズ率
corruption_level = T.scalar('corruption')
# 学習率
learning_rate = T.scalar('lr')
batch_begin = index * batch_size
batch_end = batch_begin + batch_size
# 事前学習のfunctionリスト
pretrain_functions = []
for dA in self.dA_layers:
cost, updates = dA.get_cost_updates(corruption_level,
learning_rate)
fn = theano.function(
inputs=[
index,
theano.Param(corruption_level,
default=0.2), # Paramを使うとTensorの引数の名前で値を指定できる
theano.Param(learning_rate, default=0.1)
],
outputs=cost,
updates=updates,
givens={self.x: train_set_x[batch_begin:batch_end]})
# 事前学習の関数リストに各層のオートエンコーダのcost計算とパラメータupdatesのfunctionを追加
pretrain_functions.append(fn)
return pretrain_functions
def pretraining_functions(self, train_set_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_set_x: theano.tensor.TensorType
:param train_set_x: Shared variable that contains all datapoints used
for training the dA
:type batch_size: int
:param batch_size: size of a [mini]batch
:type learning_rate: float
:param learning_rate: learning rate used during training for any of
the dA layers
'''
# 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
# 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_set_x[batch_begin: batch_end]
}
)
# append `fn` to the list of functions
pretrain_fns.append(fn)
return pretrain_fns
