def __init__(self,
weight_matrices,
batches,
fout,
hidden_biases=None,
visible_biases=None,
binary_output=False,
line_searches=3):
"""
Initialize variables of the finetuning.
@param weight_matrices: The list of weight matrices for the DBN.
@param batches: The list of batch-sizes.
@param fout: The output function. For progress monitoring of the training.
@param hidden_biases: The hidden biases for the finetuning.
@param visible_biases: The visible biases for the finetuning.
@param binary_output: If the output of the DBN must be binary. If so, Gaussian noise will be added to bottleneck.
@param line_searches: Number of line searches for the conjugate gradient optimization.
"""
# Progress and info monitoring
self.fout = fout
self.binary_output = binary_output
self.line_searches = line_searches
# Generate sampled noise matrices
self.sampled_noise = []
self.current_sampled_noise = None
self.batches = batches
if hidden_biases == None: # If finetuning shall continue.
self.weight_matrices_added_biases = weight_matrices
else:
weight_matrices = weight_matrices
hidden_biases = hidden_biases
visible_biases = visible_biases
# Unfold network to make deep autoencoder
self.weight_matrices_added_biases = []
weight_matrices = append(weight_matrices, weight_matrices[::-1])
visible_hidden_biases = append(hidden_biases, visible_biases[::-1])
# Add the visible and hidden biases to the weight matrices
for i in range(len(weight_matrices)):
if i < (len(weight_matrices) / 2):
# Make sure that the hidden biases are in the same format as the weight matrix
tmp = zeros((1, len(visible_hidden_biases[i])))
tmp[0] = visible_hidden_biases[i]
self.weight_matrices_added_biases.append(
append(weight_matrices[i], tmp, axis=0))
else:
# Make sure that the visible biases are in the same format as the weight matrix
tmp = zeros((1, len(visible_hidden_biases[i])))
tmp[0] = visible_hidden_biases[i]
self.weight_matrices_added_biases.append(
append(weight_matrices[i].T, tmp, axis=0))
if not os.path.exists(env_paths.get_dbn_batches_lst_path()):
self.generate_large_batch()
self.large_batches_lst = load_large_batches_lst()
if self.binary_output:
self.generate_sampled_noise(self.weight_matrices_added_biases)
def __init__(self, weight_matrices, batches, fout, hidden_biases=None, visible_biases=None, binary_output=False,
line_searches=3):
"""
Initialize variables of the finetuning.
@param weight_matrices: The list of weight matrices for the DBN.
@param batches: The list of batch-sizes.
@param fout: The output function. For progress monitoring of the training.
@param hidden_biases: The hidden biases for the finetuning.
@param visible_biases: The visible biases for the finetuning.
@param binary_output: If the output of the DBN must be binary. If so, Gaussian noise will be added to bottleneck.
@param line_searches: Number of line searches for the conjugate gradient optimization.
"""
# Progress and info monitoring
self.fout = fout
self.binary_output = binary_output
self.line_searches = line_searches
# Generate sampled noise matrices
self.sampled_noise = []
self.current_sampled_noise = None
self.batches = batches
if hidden_biases == None: # If finetuning shall continue.
self.weight_matrices_added_biases = weight_matrices
else:
weight_matrices = weight_matrices
hidden_biases = hidden_biases
visible_biases = visible_biases
# Unfold network to make deep autoencoder
self.weight_matrices_added_biases = []
weight_matrices = append(weight_matrices, weight_matrices[::-1])
visible_hidden_biases = append(hidden_biases, visible_biases[::-1])
# Add the visible and hidden biases to the weight matrices
for i in range(len(weight_matrices)):
if i < (len(weight_matrices) / 2):
# Make sure that the hidden biases are in the same format as the weight matrix
tmp = zeros((1, len(visible_hidden_biases[i])))
tmp[0] = visible_hidden_biases[i]
self.weight_matrices_added_biases.append(append(weight_matrices[i], tmp, axis=0))
else:
# Make sure that the visible biases are in the same format as the weight matrix
tmp = zeros((1, len(visible_hidden_biases[i])))
tmp[0] = visible_hidden_biases[i]
self.weight_matrices_added_biases.append(append(weight_matrices[i].T, tmp, axis=0))
if not os.path.exists(env_paths.get_dbn_batches_lst_path()):
self.generate_large_batch()
self.large_batches_lst = load_large_batches_lst()
if self.binary_output:
self.generate_sampled_noise(self.weight_matrices_added_biases)
def __init__(self,weight_matrices,batches,fout,fprogress,hidden_biases = None,visible_biases = None):
"""
Initialize variables of the finetuning.
@param weight_matrices: The list of weight matrices for the DBN.
@param batches: The list of batch-sizes.
@param fout: The output function. For progress monitoring of the training.
@param fprogress: The incrementer. For progress monitoring of the training.
@param hidden_biases: The hidden biases for the finetuning.
@param visible_biases: The visible biases for the finetuning.
"""
# Progress and info monitoring
self.fout = fout
self.fprogress = fprogress
self.batches = batches
if hidden_biases == None: # If finetuning shall continue.
self.weight_matrices_added_biases = weight_matrices
else:
weight_matrices = weight_matrices
hidden_biases = hidden_biases
visible_biases = visible_biases
# Unfold network to make deep autoencoder
self.weight_matrices_added_biases = []
weight_matrices = append(weight_matrices,weight_matrices[::-1])
visible_hidden_biases =append(hidden_biases,visible_biases[::-1])
# Add the visible and hidden biases to the weight matrices
for i in range(len(weight_matrices)):
if i < (len(weight_matrices)/2):
# Make sure that the hidden biases are in the same format as the weight matrix
tmp = zeros((1,len(visible_hidden_biases[i])))
tmp[0] = visible_hidden_biases[i]
self.weight_matrices_added_biases.append(append(weight_matrices[i],tmp,axis = 0))
else:
# Make sure that the visible biases are in the same format as the weight matrix
tmp = zeros((1,len(visible_hidden_biases[i])))
tmp[0] = visible_hidden_biases[i]
self.weight_matrices_added_biases.append(append(weight_matrices[i].T,tmp,axis = 0))
if not os.path.exists(env_paths.get_dbn_batches_lst_path()):
self.generate_large_batch()
self.large_batches_lst = load_large_batches_lst()
def load_large_batches_lst():
return s.load(open(env_paths.get_dbn_batches_lst_path(), 'rb'))
def save_large_batches_lst(lst):
s.dump(lst, open(env_paths.get_dbn_batches_lst_path(), 'wb'))
def load_large_batches_lst():
return s.load(open(env_paths.get_dbn_batches_lst_path(), 'rb'))
def save_large_batches_lst(lst):
s.dump(lst, open(env_paths.get_dbn_batches_lst_path(), 'wb'))
