def run_ae(step, momentum, decay, n_hidden, hidden_transfers, par_std, batch_size, counter, X, TX):
print step, momentum, decay, n_hidden, hidden_transfers, par_std, batch_size, counter
seed = 3453
np.random.seed(seed)
batch_size = batch_size
#max_iter = max_passes * X.shape[ 0] / batch_size
max_iter = 10000000
n_report = X.shape[0] / batch_size
weights = []
#X = X.reshape(X.shape[0], 23*23)
#TX = TX.reshape(TX.shape[0], 23*23)
input_size = X.shape[1]
print input_size
stop = climin.stops.AfterNIterations(max_iter)
pause = climin.stops.ModuloNIterations(n_report)
optimizer = 'gd', {'step_rate': step, 'momentum': momentum, 'decay': decay}
typ = 'dae'
if typ == 'cae':
m = ContractiveAutoEncoder(X.shape[1], n_hidden, X,
hidden_transfers=hidden_transfers, out_transfer='identity', loss='squared', optimizer=optimizer,
batch_size=batch_size, max_iter=max_iter, c_jacobian=1)
elif typ == 'dae':
m = DenoisingAutoEncoder(X.shape[1], n_hidden, X,
hidden_transfers=hidden_transfers, out_transfer='identity', loss='squared', optimizer=optimizer,
batch_size=batch_size, max_iter=max_iter, noise_type='gauss', c_noise=.2)
climin.initialize.randomize_normal(m.parameters.data, 0, par_std)
# Transform the test data
TX = m.transformedData(TX)
#m.init_weights()
#TX = np.array([TX for _ in range(10)]).mean(axis=0)
print TX.shape
losses = []
print 'max iter', max_iter
X, TX = [breze.learn.base.cast_array_to_local_type(i) for i in (X, TX)]
start = time.time()
# Set up a nice printout.
keys = '#', 'seconds', 'loss', 'val loss'
max_len = max(len(i) for i in keys)
header = '\t'.join(i for i in keys)
print header
print '-' * len(header)
results = open('result_hp.txt', 'a')
results.write(header + '\n')
results.write('-' * len(header) + '\n')
results.close()
EXP_DIR = os.getcwd()
base_path = os.path.join(EXP_DIR, "pars_hp"+str(counter)+".pkl")
n_iter = 0
if os.path.isfile(base_path):
with open("pars_hp"+str(counter)+".pkl", 'rb') as tp:
n_iter, best_pars, best_loss = cp.load(tp)
m.parameters.data[...] = best_pars
for i, info in enumerate(m.powerfit((X,), (TX,), stop, pause)):
if info['n_iter'] % n_report != 0:
continue
if math.isnan(info['loss']) == True:
n_iter = info['n_iter']
break
passed = time.time() - start
losses.append((info['loss'], info['val_loss']))
info.update({
'time': passed,
})
best_pars = info['best_pars']
best_loss = info['best_loss']
info['n_iter'] += n_iter
row = '%(n_iter)i\t%(time)g\t%(loss)f\t%(val_loss)f' % info
results = open('result_hp.txt', 'a')
print row
results.write(row + '\n')
results.close()
with open("pars_hp"+str(counter)+".pkl", 'wb') as fp:
cp.dump((info['n_iter'], info['best_pars'], info['best_loss']), fp)
with open("hps"+str(counter)+".pkl", 'wb') as tp:
cp.dump((step, momentum, decay, n_hidden, hidden_transfers, par_std, batch_size, counter, info['n_iter']), tp)
m.parameters.data[...] = best_pars
cp.dump((best_pars, best_loss), open("best_pars"+str(counter)+".pkl", 'wb'))
i = m.mlp.layers.__len__() - 2
f_reconstruct = m.function([m.exprs['inpt']], m.mlp.layers[i].output)
L = f_reconstruct(m.transformedData(X))
LT = f_reconstruct(TX)
print L.shape, LT.shape
mydict = {'X': np.array(L), 'Z': Z, 'TZ': TZ, 'TX': np.array(LT), 'P': CV}
output = open("autoencoder"+str(counter)+".pkl", 'wb')
cp.dump(mydict, output)
output.close()
#max_iter = max_passes * X.shape[ 0] / batch_size
max_iter = 1000
n_report = X.shape[0] / batch_size
stop = climin.stops.AfterNIterations(max_iter)
pause = climin.stops.ModuloNIterations(n_report)
optimizer = 'gd', {'step_rate': 0.001, 'momentum': 0}
typ = 'dae'
if typ == 'cae':
m = ContractiveAutoEncoder(X.shape[1], [2700], X,
hidden_transfers=['tanh'], out_transfer='identity', loss='squared', optimizer=optimizer,
batch_size=batch_size, max_iter=max_iter, c_jacobian=1)
elif typ == 'dae':
m = DenoisingAutoEncoder(X.shape[1], [2500], X,
hidden_transfers=['tanh'], out_transfer='identity', loss='squared', optimizer=optimizer,
batch_size=batch_size, max_iter=max_iter, noise_type='gauss', c_noise=.2)
#climin.initialize.randomize_normal(m.parameters.data, 0, 1 / np.sqrt(m.n_inpt))
#m.init_weights()
#Transform the test data
#TX = m.transformedData(TX)
TX = np.array([m.transformedData(TX) for _ in range(10)]).mean(axis=0)
print TX.shape
def run_ae(step, momentum, decay, n_hidden, hidden_transfers, par_std, batch_size, counter, X, TX):
print step, momentum, decay, n_hidden, hidden_transfers, par_std, batch_size, counter
seed = 3453
np.random.seed(seed)
batch_size = batch_size
#max_iter = max_passes * X.shape[ 0] / batch_size
max_iter = 5000000
n_report = X.shape[0] / batch_size
weights = []
#X = X.reshape(X.shape[0], 23*23)
#TX = TX.reshape(TX.shape[0], 23*23)
input_size = X.shape[1]
print input_size
stop = climin.stops.AfterNIterations(max_iter)
pause = climin.stops.ModuloNIterations(n_report)
optimizer = 'gd', {'step_rate': step, 'momentum': momentum, 'decay': decay}
typ = 'cae'
if typ == 'cae':
m = ContractiveAutoEncoder(X.shape[1], n_hidden, X,
hidden_transfers=hidden_transfers, out_transfer='identity', loss='squared', optimizer=optimizer,
batch_size=batch_size, max_iter=max_iter, c_jacobian=1)
elif typ == 'dae':
m = DenoisingAutoEncoder(X.shape[1], n_hidden, X,
hidden_transfers=hidden_transfers, out_transfer='identity', loss='squared', optimizer=optimizer,
batch_size=batch_size, max_iter=max_iter, noise_type='gauss', c_noise=.2)
climin.initialize.randomize_normal(m.parameters.data, 0, par_std)
# Transform the test data
TX = m.transformedData(TX)
#m.init_weights()
#TX = np.array([TX for _ in range(10)]).mean(axis=0)
print TX.shape
losses = []
print 'max iter', max_iter
X, TX = [breze.learn.base.cast_array_to_local_type(i) for i in (X, TX)]
start = time.time()
# Set up a nice printout.
keys = '#', 'seconds', 'loss', 'val loss'
max_len = max(len(i) for i in keys)
header = '\t'.join(i for i in keys)
print header
print '-' * len(header)
results = open('result_hp.txt', 'a')
results.write(header + '\n')
results.write('-' * len(header) + '\n')
results.close()
EXP_DIR = os.getcwd()
base_path = os.path.join(EXP_DIR, "pars_hp"+str(counter)+".pkl")
n_iter = 0
if os.path.isfile(base_path):
with open("pars_hp"+str(counter)+".pkl", 'rb') as tp:
n_iter, best_pars, best_loss = cp.load(tp)
m.parameters.data[...] = best_pars
for i, info in enumerate(m.powerfit((X,), (TX,), stop, pause)):
if info['n_iter'] % n_report != 0:
continue
if 0 <= info['val_loss'] < 1:
with open("pars_bestloss.pkl", 'wb') as fp:
cp.dump((info['n_iter'], info['best_pars'], info['best_loss']), fp)
m.parameters.data[...] = info['best_pars']
i = m.mlp.layers.__len__() - 2
f_reconstruct = m.function([m.exprs['inpt']], m.mlp.layers[i].output)
L = f_reconstruct(m.transformedData(X))
LT = f_reconstruct(TX)
mydict = {'X': np.array(L), 'Z': Z, 'TZ': TZ, 'TX': np.array(LT), 'P': CV}
output = open("autoencoder_best.pkl", 'wb')
cp.dump(mydict, output)
output.close()
if math.isnan(info['val_loss']) == True:
n_iter = info['n_iter']
break
passed = time.time() - start
losses.append((info['loss'], info['val_loss']))
info.update({
'time': passed,
})
best_pars = info['best_pars']
best_loss = info['best_loss']
info['n_iter'] += n_iter
row = '%(n_iter)i\t%(time)g\t%(loss)f\t%(val_loss)f' % info
results = open('result_hp.txt', 'a')
print row
results.write(row + '\n')
results.close()
with open("pars_hp"+str(counter)+".pkl", 'wb') as fp:
cp.dump((info['n_iter'], info['best_pars'], info['best_loss']), fp)
with open("hps"+str(counter)+".pkl", 'wb') as tp:
cp.dump((step, momentum, decay, n_hidden, hidden_transfers, par_std, batch_size, counter, info['n_iter']), tp)
m.parameters.data[...] = best_pars
cp.dump((best_pars, best_loss), open("best_pars"+str(counter)+".pkl", 'wb'))
i = m.mlp.layers.__len__() - 2
f_reconstruct = m.function([m.exprs['inpt']], m.mlp.layers[i].output)
L = f_reconstruct(m.transformedData(X))
LT = f_reconstruct(TX)
print L.shape, LT.shape
mydict = {'X': np.array(L), 'Z': Z, 'TZ': TZ, 'TX': np.array(LT), 'P': CV}
output = open("autoencoder"+str(counter)+".pkl", 'wb')
cp.dump(mydict, output)
output.close()
#max_iter = max_passes * X.shape[ 0] / batch_size
max_iter = 1000
n_report = X.shape[0] / batch_size
stop = climin.stops.AfterNIterations(max_iter)
pause = climin.stops.ModuloNIterations(n_report)
optimizer = 'gd', {'step_rate': 0.001, 'momentum': 0}
typ = 'dae'
if typ == 'cae':
m = ContractiveAutoEncoder(X.shape[1], [2700],
X,
hidden_transfers=['tanh'],
out_transfer='identity',
loss='squared',
optimizer=optimizer,
batch_size=batch_size,
max_iter=max_iter,
c_jacobian=1)
elif typ == 'dae':
m = DenoisingAutoEncoder(X.shape[1], [2500],
X,
hidden_transfers=['tanh'],
out_transfer='identity',
loss='squared',
optimizer=optimizer,
batch_size=batch_size,
max_iter=max_iter,
noise_type='gauss',