def get_weights():
"""
Retrieve the weights from the generated DBN.
@return: Weights of the DBN.
"""
return [array(w) for w in s.load(open(env_paths.get_dbn_weight_path(), "rb" ) )]
def check_for_data():
"""
Check for DBN network data.
"""
if not (os.path.exists(ep.get_test_data_path()) or os.path.exists(ep.get_dbn_weight_path())):
return False
return True
def load_dbn_weights():
"""
Load the weight matrices from the finetuning.
@param weight_matrices: the weight matrices of the finetuning.
"""
return [array(w) for w in s.load(open(env_paths.get_dbn_weight_path(), "rb" ) )]
def compare_real_data_to_reconstructed_data():
weights = s.load(open(env_paths.get_dbn_weight_path(),"rb"))
batches = s.load(open(env_paths.get_batches_path(train=False),"rb"))
class_indices = s.load(open(env_paths.get_class_indices_path(False,batches[0]).replace(".0",""),"rb"))
batch = batches[0]
data = data_processing.get_bag_of_words_matrix(batch,training = False)
dict = {}
for i in range(len(class_indices)):
idx = class_indices[i]
if idx in dict.keys(): continue
dict[idx] = data[i]
if len(dict) >= 10:
break
print dict.keys()
data_points = dict.values()
output_data_points = []
for d in data_points:
d = append(d,1.)
out = generate_output_data(d,weights)
output_data_points.append(out)
visualise_data_points(data_points,output_data_points)
def get_weights():
"""
Retrieve the weights from the generated DBN.
@return: Weights of the DBN.
"""
return [
array(w) for w in s.load(open(env_paths.get_dbn_weight_path(), "rb"))
]
def load_dbn_weights():
"""
Load the weight matrices from the finetuning.
@param weight_matrices: the weight matrices of the finetuning.
"""
return [
array(w) for w in s.load(open(env_paths.get_dbn_weight_path(), "rb"))
]
def save_dbn(weight_matrices,fine_tuning_error_train,fine_tuning_error_test,output = None):
"""
Save the deep belief network into serialized files.
@param weight_matrices: the weight matrices of the deep belief network.
"""
s.dump([w.tolist() for w in weight_matrices] , open( env_paths.get_dbn_weight_path(), "wb" ) )
s.dump(fine_tuning_error_train , open( env_paths.get_dbn_training_error_path(), "wb" ) )
s.dump(fine_tuning_error_test , open( env_paths.get_dbn_test_error_path(), "wb" ) )
if not output == None:
out = open(env_paths.get_dbn_output_txt_path(),"w")
for elem in output:
out.write(elem+"\n")
out.close()
def compare_real_data_to_reconstructed_data_random():
weights = s.load(open(env_paths.get_dbn_weight_path(),"rb"))
batches = s.load(open(env_paths.get_batches_path(train=False),"rb"))
batch = choice(batches) # make sure to pick batch at random
data = data_processing.get_bag_of_words_matrix(batch,training = False)
# choose 10 data points at random
data_points = []
indices = random.randint(0,len(data),10)
for idx in indices:
data_points.append(data[idx])
output_data_points = []
for d in data_points:
d = append(d,1.)
out = generate_output_data(d,weights)
output_data_points.append(out)
visualise_data_points(data_points,output_data_points)
def save_dbn(weight_matrices,
fine_tuning_error_train,
fine_tuning_error_test,
output=None):
"""
Save the deep belief network into serialized files.
@param weight_matrices: the weight matrices of the deep belief network.
"""
s.dump([w.tolist() for w in weight_matrices],
open(env_paths.get_dbn_weight_path(), "wb"))
s.dump(fine_tuning_error_train,
open(env_paths.get_dbn_training_error_path(), "wb"))
s.dump(fine_tuning_error_test,
open(env_paths.get_dbn_test_error_path(), "wb"))
if not output == None:
out = open(env_paths.get_dbn_output_txt_path(), "w")
for elem in output:
out.write(elem + "\n")
out.close()
