old_contents: __author__ = 'vps'
  
  
  from sklearn.base import BaseEstimator
  from dA import *
  import numpy
  
  import theano
  import theano.tensor as T
  from theano.tensor.shared_randomstreams import RandomStreams
  import timeit
  import os
  import sys
  from DenoisingAutoencoder import DenoisingAutoencoder
  
  
  class StackedDenoisingAutoencoders(BaseEstimator):
      def __init__(self, hidden_layers_sizes, learning_rate=0.1, training_epochs=15, corruption_level=0.0, batch_size=20, verbose=False):
          self.n_visible = None
          self.hidden_layers_sizes = hidden_layers_sizes
          self.learning_rate = learning_rate
          self.corruption_level = corruption_level
          self.training_epochs = training_epochs
          self.batch_size = batch_size
          self.verbose = verbose
          self.das = []
  
      def fit(self,X):
          start_time = timeit.default_timer()
          self.n_visible = X.shape[1]
  
          for hidden_layer_size in self.hidden_layers_sizes:
              da = DenoisingAutoencoder(hidden_layer_size,
                                        self.learning_rate,
                                        self.training_epochs,
                                        self.corruption_level,
                                        self.batch_size,
                                        self.verbose)
              self.das.append(da)
  
          # Greedily train the AE
          input = X
          for da in self.das:
              da.fit(input)
              input = da.transform_latent_representation(input)
  
          end_time = timeit.default_timer()
          training_time = (end_time - start_time)
  
          print(("The %d%% corruption code " % (self.corruption_level*100) +
                 ' ran for %.2fm' % (training_time / 60.)))
  
      def transform(self, X):
          input = X
          for da in self.das:
              input = da.transform(input)
  
          return input
  
      def transform_latent_representation(self, X):
          input = X
          for da in self.das:
              input = da.transform_latent_representation(input)
  
          return input
  
new_contents: __author__ = 'vps'
  
  import timeit
  
  from sklearn.base import BaseEstimator
  from DenoisingAutoencoder import DenoisingAutoencoder
  
  
  class StackedDenoisingAutoencoders(BaseEstimator):
      def __init__(self, hidden_layers_sizes, learning_rate=0.1, training_epochs=15, corruption_level=0.0, batch_size=20, verbose=False):
          self.n_visible = None
          self.hidden_layers_sizes = hidden_layers_sizes
          self.learning_rate = learning_rate
          self.corruption_level = corruption_level
          self.training_epochs = training_epochs
          self.batch_size = batch_size
          self.verbose = verbose
          self.das = []
  
      def fit(self,X):
          start_time = timeit.default_timer()
          self.n_visible = X.shape[1]
  
          for hidden_layer_size in self.hidden_layers_sizes:
              da = DenoisingAutoencoder(hidden_layer_size,
                                        self.learning_rate,
                                        self.training_epochs,
                                        self.corruption_level,
                                        self.batch_size,
                                        self.verbose)
              self.das.append(da)
  
          # Greedily train the AE
          input = X
          for da in self.das:
              da.fit(input)
              input = da.transform_latent_representation(input)
  
          end_time = timeit.default_timer()
          training_time = (end_time - start_time)
  
          print(("The %d%% corruption code " % (self.corruption_level*100) +
                 ' ran for %.2fm' % (training_time / 60.)))
  
      def transform(self, X):
          input = X
          for da in self.das:
              input = da.transform(input)
  
          return input
  
      def transform_latent_representation(self, X):
          input = X
          for da in self.das:
              input = da.transform_latent_representation(input)
  
          return input
  
subject: Optimize imports