def build_CompressModel():
print 'STEP 1 start...'
dataset = Nikkei(dataset_type=params['experiment_type'],
brandcode=params['STEP3']['brandcode'])
# pdb.set_trace()
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
if params['STEP1']['model'] == 'rbm':
model = RBM(input=x,
n_visible=dataset.phase1_input_size,
n_hidden=params['STEP1']['n_hidden'],
reg_weight=params['STEP1']['beta'])
train_rbm(input=x,
model=model,
dataset=dataset,
learning_rate=params['STEP1']['learning_rate'],
outdir=model_dirs['STEP1'])
else:
model = SparseAutoencoder(input=x,
n_visible=dataset.phase1_input_size,
n_hidden=params['STEP1']['n_hidden'],
beta=params['STEP1']['beta'])
train_sae(input=x,
model=model,
dataset=dataset,
learning_rate=params['STEP1']['learning_rate'],
outdir=model_dirs['STEP1'])
def build_CompressModel():
print "STEP 1 start..."
dataset = Nikkei(dataset_type=params["experiment_type"], brandcode=params["STEP3"]["brandcode"])
# pdb.set_trace()
index = T.lscalar() # index to a [mini]batch
x = T.matrix("x") # the data is presented as rasterized images
if params["STEP1"]["model"] == "rbm":
model = RBM(
input=x,
n_visible=dataset.phase1_input_size,
n_hidden=params["STEP1"]["n_hidden"],
reg_weight=params["STEP1"]["beta"],
)
train_rbm(
input=x,
model=model,
dataset=dataset,
learning_rate=params["STEP1"]["learning_rate"],
outdir=model_dirs["STEP1"],
)
else:
model = SparseAutoencoder(
input=x,
n_visible=dataset.phase1_input_size,
n_hidden=params["STEP1"]["n_hidden"],
beta=params["STEP1"]["beta"],
)
train_sae(
input=x,
model=model,
dataset=dataset,
learning_rate=params["STEP1"]["learning_rate"],
outdir=model_dirs["STEP1"],
)
def build_CompressModel():
print 'STEP 1 start...'
dataset = Nikkei(dataset_type=params['dataset_type'], brandcode=params['STEP3']['brandcode'])
# pdb.set_trace()
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
if params['STEP1']['model'] == 'rbm':
model = RBM(input=x, n_visible=dataset.phase1_input_size, n_hidden=params['STEP1']['n_hidden'], reg_weight=params['STEP1']['reg_weight'], corruption_level=params['STEP1']['corruption_level'])
train_rbm(input=x, model=model, dataset=dataset, learning_rate=params['STEP1']['learning_rate'], outdir=model_dirs['STEP1'], batch_size=params['STEP1']['batch_size'])
elif params['STEP1']['model'] == 'sda':
sda_params = {
'dataset' : dataset,
'hidden_layers_sizes' : [params['STEP1']['n_hidden'], params['STEP1']['n_hidden'] / 2],
'pretrain_lr' : params['STEP1']['learning_rate'],
'pretrain_batch_size' : params['STEP1']['batch_size'],
'pretrain_epochs' : 5,
'corruption_levels' : [0.5, 0.5],
'k' : None,
'y_type' : 0,
'sparse_weight' : params['STEP1']['reg_weight']
}
model = SdA.compress(sda_params)
pre_params = get_model_params(model)
while(True):
try:
f_out = open(model_dirs['STEP1'], 'w')
f_out.write(cPickle.dumps(model, 1))
f_out.close()
break
except:
pdb.set_trace()
else:
model = SparseAutoencoder(input=x, n_visible=dataset.phase1_input_size, n_hidden=params['STEP1']['n_hidden'], reg_weight=params['STEP1']['reg_weight'], corruption_level=params['STEP1']['corruption_level'])
train_sae(input=x, model=model, dataset=dataset, learning_rate=params['STEP1']['learning_rate'], outdir=model_dirs['STEP1'], batch_size=params['STEP1']['batch_size'])
def retrain_CompressModel():
print 'STEP 1 start...'
dataset = Nikkei(dataset_type=params['dataset_type'],
brandcode=params['STEP3']['brandcode'])
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
if params['STEP1']['model'] == 'rbm':
model = load_model(model_type='rbm',
input=x,
params_dir=model_dirs['STEP1'])
train_rbm(input=x,
model=model,
dataset=dataset,
learning_rate=params['STEP1']['learning_rate'],
batch_size=params['STEP1']['batch_size'],
outdir=model_dirs['STEP1'])
# elif params['STEP1']['model'] == 'sda':
# presae_dir = '%s/%s/h%d_lr%s_b%s_c%s.%s' % (default_model_dir, 'STEP1', params['STEP1']['n_hidden'], str(params['STEP1']['learning_rate']), str(params['STEP1']['reg_weight']), str(params['STEP1']['corruption_level']), 'sae')
# x2 = T.matrix('x')
# pre_model = load_model(model_type='sae', input=x, params_dir=presae_dir)
# model = SparseAutoencoder(input=x2, n_visible=params['STEP1']['n_hidden'], n_hidden=params['STEP1']['n_hidden'], reg_weight=params['STEP1']['reg_weight'], corruption_level=params['STEP1']['corruption_level'])
# train_sae2(input=x, model=model, pre_model=pre_model, dataset=dataset, learning_rate=params['STEP1']['learning_rate'], outdir=model_dirs['STEP1'])
else:
model = load_model(model_type='sae',
input=x,
params_dir=model_dirs['STEP1'])
train_sae(input=x,
model=model,
dataset=dataset,
learning_rate=params['STEP1']['learning_rate'],
batch_size=params['STEP1']['batch_size'],
outdir=model_dirs['STEP1'])
def retrain_CompressModel():
print 'STEP 1 start...'
dataset = Nikkei(dataset_type=params['experiment_type'], brandcode=params['STEP3']['brandcode'])
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
if params['STEP1']['model'] == 'rbm':
model = load_model(model_type='rbm', input=x, params_dir=model_dirs['STEP1'])
train_rbm(input=x, model=model, dataset=dataset, learning_rate=params['STEP1']['learning_rate'], outdir=model_dirs['STEP1'])
else:
model = load_model(model_type='sae', input=x, params_dir=model_dirs['STEP1'])
train_sae(input=x, model=model, dataset=dataset, learning_rate=params['STEP1']['learning_rate'], outdir=model_dirs['STEP1'])
def retrain_CompressModel():
print 'STEP 1 start...'
dataset = Nikkei(dataset_type=params['experiment_type'], brandcode=params['STEP3']['brandcode'])
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
if params['STEP1']['model'] == 'rbm':
model = load_model(model_type='rbm', input=x, params_dir=model_dirs['STEP1'])
train_rbm(input=x, model=model, dataset=dataset, learning_rate=params['STEP1']['learning_rate'], outdir=model_dirs['STEP1'])
else:
model = load_model(model_type='sae', input=x, params_dir=model_dirs['STEP1'])
train_sae(input=x, model=model, dataset=dataset, learning_rate=params['STEP1']['learning_rate'], outdir=model_dirs['STEP1'])
def build_CompressModel():
print 'STEP 1 start...'
dataset = Nikkei(dataset_type=params['experiment_type'], brandcode=params['STEP3']['brandcode'])
# pdb.set_trace()
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
if params['STEP1']['model'] == 'rbm':
model = RBM(input=x, n_visible=dataset.phase1_input_size, n_hidden=params['STEP1']['n_hidden'], reg_weight=params['STEP1']['beta'])
train_rbm(input=x, model=model, dataset=dataset, learning_rate=params['STEP1']['learning_rate'], outdir=model_dirs['STEP1'])
else:
model = SparseAutoencoder(input=x, n_visible=dataset.phase1_input_size, n_hidden=params['STEP1']['n_hidden'], beta=params['STEP1']['beta'])
train_sae(input=x, model=model, dataset=dataset, learning_rate=params['STEP1']['learning_rate'], outdir=model_dirs['STEP1'])
def retrain_CompressModel():
print 'STEP 1 start...'
dataset = Nikkei(dataset_type=params['dataset_type'], brandcode=params['STEP3']['brandcode'])
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
if params['STEP1']['model'] == 'rbm':
model = load_model(model_type='rbm', input=x, params_dir=model_dirs['STEP1'])
train_rbm(input=x, model=model, dataset=dataset, learning_rate=params['STEP1']['learning_rate'], batch_size=params['STEP1']['batch_size'], outdir=model_dirs['STEP1'])
# elif params['STEP1']['model'] == 'sda':
# presae_dir = '%s/%s/h%d_lr%s_b%s_c%s.%s' % (default_model_dir, 'STEP1', params['STEP1']['n_hidden'], str(params['STEP1']['learning_rate']), str(params['STEP1']['reg_weight']), str(params['STEP1']['corruption_level']), 'sae')
# x2 = T.matrix('x')
# pre_model = load_model(model_type='sae', input=x, params_dir=presae_dir)
# model = SparseAutoencoder(input=x2, n_visible=params['STEP1']['n_hidden'], n_hidden=params['STEP1']['n_hidden'], reg_weight=params['STEP1']['reg_weight'], corruption_level=params['STEP1']['corruption_level'])
# train_sae2(input=x, model=model, pre_model=pre_model, dataset=dataset, learning_rate=params['STEP1']['learning_rate'], outdir=model_dirs['STEP1'])
else:
model = load_model(model_type='sae', input=x, params_dir=model_dirs['STEP1'])
train_sae(input=x, model=model, dataset=dataset, learning_rate=params['STEP1']['learning_rate'], batch_size=params['STEP1']['batch_size'], outdir=model_dirs['STEP1'])
def retrain_CompressModel():
print "STEP 1 start..."
dataset = Nikkei(dataset_type=params["experiment_type"], brandcode=params["STEP3"]["brandcode"])
index = T.lscalar() # index to a [mini]batch
x = T.matrix("x") # the data is presented as rasterized images
if params["STEP1"]["model"] == "rbm":
model = load_model(model_type="rbm", input=x, params_dir=model_dirs["STEP1"])
train_rbm(
input=x,
model=model,
dataset=dataset,
learning_rate=params["STEP1"]["learning_rate"],
outdir=model_dirs["STEP1"],
)
else:
model = load_model(model_type="sae", input=x, params_dir=model_dirs["STEP1"])
train_sae(
input=x,
model=model,
dataset=dataset,
learning_rate=params["STEP1"]["learning_rate"],
outdir=model_dirs["STEP1"],
)
def build_CompressModel():
print 'STEP 1 start...'
dataset = Nikkei(dataset_type=params['dataset_type'],
brandcode=params['STEP3']['brandcode'])
# pdb.set_trace()
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
if params['STEP1']['model'] == 'rbm':
model = RBM(input=x,
n_visible=dataset.phase1_input_size,
n_hidden=params['STEP1']['n_hidden'],
reg_weight=params['STEP1']['reg_weight'],
corruption_level=params['STEP1']['corruption_level'])
train_rbm(input=x,
model=model,
dataset=dataset,
learning_rate=params['STEP1']['learning_rate'],
outdir=model_dirs['STEP1'],
batch_size=params['STEP1']['batch_size'])
elif params['STEP1']['model'] == 'sda':
sda_params = {
'dataset':
dataset,
'hidden_layers_sizes':
[params['STEP1']['n_hidden'], params['STEP1']['n_hidden'] / 2],
'pretrain_lr':
params['STEP1']['learning_rate'],
'pretrain_batch_size':
params['STEP1']['batch_size'],
'pretrain_epochs':
5,
'corruption_levels': [0.5, 0.5],
'k':
None,
'y_type':
0,
'sparse_weight':
params['STEP1']['reg_weight']
}
model = SdA.compress(sda_params)
pre_params = get_model_params(model)
while (True):
try:
f_out = open(model_dirs['STEP1'], 'w')
f_out.write(cPickle.dumps(model, 1))
f_out.close()
break
except:
pdb.set_trace()
else:
model = SparseAutoencoder(
input=x,
n_visible=dataset.phase1_input_size,
n_hidden=params['STEP1']['n_hidden'],
reg_weight=params['STEP1']['reg_weight'],
corruption_level=params['STEP1']['corruption_level'])
train_sae(input=x,
model=model,
dataset=dataset,
learning_rate=params['STEP1']['learning_rate'],
outdir=model_dirs['STEP1'],
batch_size=params['STEP1']['batch_size'])