def run_mlp(func, step, momentum, X, Z, TX, TZ, wd, opt, counter, arch,
batches):
print func, step, momentum, wd, opt, counter, arch, batches
seed = 3453
np.random.seed(seed)
batch_size = batches
#max_iter = max_passes * X.shape[ 0] / batch_size
max_iter = 25000000
n_report = X.shape[0] / batch_size
weights = []
input_size = len(X[0])
stop = climin.stops.AfterNIterations(max_iter)
pause = climin.stops.ModuloNIterations(n_report)
optimizer = opt, {'step_rate': step, 'momentum': momentum}
typ = 'plain'
if typ == 'plain':
m = Mlp(input_size,
arch,
1,
X,
Z,
hidden_transfers=func,
out_transfer='identity',
loss='squared',
optimizer=optimizer,
batch_size=batch_size,
max_iter=max_iter)
elif typ == 'fd':
m = FastDropoutNetwork(2099,
arch,
1,
X,
Z,
TX,
TZ,
hidden_transfers=['tanh', 'tanh'],
out_transfer='identity',
loss='squared',
p_dropout_inpt=.1,
p_dropout_hiddens=.2,
optimizer=optimizer,
batch_size=batch_size,
max_iter=max_iter)
#climin.initialize.randomize_normal(m.parameters.data, 0, 1 / np.sqrt(m.n_inpt))
# Transform the test data
#TX = m.transformedData(TX)
TX = np.array([m.transformedData(TX) for _ in range(10)]).mean(axis=0)
print TX.shape
losses = []
print 'max iter', max_iter
m.init_weights()
X, Z, TX, TZ = [
breze.learn.base.cast_array_to_local_type(i) for i in (X, Z, TX, TZ)
]
for layer in m.mlp.layers:
weights.append(m.parameters[layer.weights])
weight_decay = ((weights[0]**2).sum() + (weights[1]**2).sum() +
(weights[2]**2).sum())
weight_decay /= m.exprs['inpt'].shape[0]
m.exprs['true_loss'] = m.exprs['loss']
c_wd = wd
m.exprs['loss'] = m.exprs['loss'] + c_wd * weight_decay
mae = T.abs_((m.exprs['output'] * np.std(train_labels) +
np.mean(train_labels)) - m.exprs['target']).mean()
f_mae = m.function(['inpt', 'target'], mae)
rmse = T.sqrt(
T.square((m.exprs['output'] * np.std(train_labels) +
np.mean(train_labels)) - m.exprs['target']).mean())
f_rmse = m.function(['inpt', 'target'], rmse)
start = time.time()
# Set up a nice printout.
keys = '#', 'seconds', 'loss', 'val loss', 'mae_train', 'rmse_train', 'mae_test', 'rmse_test'
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 = cp.load(tp)
m.parameters.data[...] = best_pars
for i, info in enumerate(m.powerfit((X, Z), (TX, TZ), stop, pause)):
if info['n_iter'] % n_report != 0:
continue
passed = time.time() - start
losses.append((info['loss'], info['val_loss']))
info.update({
'time': passed,
'mae_train': f_mae(m.transformedData(X), train_labels),
'rmse_train': f_rmse(m.transformedData(X), train_labels),
'mae_test': f_mae(TX, test_labels),
'rmse_test': f_rmse(TX, test_labels)
})
info['n_iter'] += n_iter
row = '%(n_iter)i\t%(time)g\t%(loss)f\t%(val_loss)f\t%(mae_train)g\t%(rmse_train)g\t%(mae_test)g\t%(rmse_test)g' % 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']), fp)
with open("hps" + str(counter) + ".pkl", 'wb') as tp:
cp.dump((func, step, momentum, wd, opt, counter, info['n_iter'],
arch, batches), tp)
m.parameters.data[...] = info['best_pars']
cp.dump(info['best_pars'], open('best_pars.pkl', 'wb'))
Y = m.predict(m.transformedData(X))
TY = m.predict(TX)
output_train = Y * np.std(train_labels) + np.mean(train_labels)
output_test = TY * np.std(train_labels) + np.mean(train_labels)
print 'TRAINING SET\n'
print('MAE: %5.2f kcal/mol' %
np.abs(output_train - train_labels).mean(axis=0))
print('RMSE: %5.2f kcal/mol' %
np.square(output_train - train_labels).mean(axis=0)**.5)
print 'TESTING SET\n'
print('MAE: %5.2f kcal/mol' %
np.abs(output_test - test_labels).mean(axis=0))
print('RMSE: %5.2f kcal/mol' %
np.square(output_test - test_labels).mean(axis=0)**.5)
mae_train = np.abs(output_train - train_labels).mean(axis=0)
rmse_train = np.square(output_train - train_labels).mean(axis=0)**.5
mae_test = np.abs(output_test - test_labels).mean(axis=0)
rmse_test = np.square(output_test - test_labels).mean(axis=0)**.5
results = open('result.txt', 'a')
results.write('Training set:\n')
results.write('MAE:\n')
results.write("%5.2f" % mae_train)
results.write('\nRMSE:\n')
results.write("%5.2f" % rmse_train)
results.write('\nTesting set:\n')
results.write('MAE:\n')
results.write("%5.2f" % mae_test)
results.write('\nRMSE:\n')
results.write("%5.2f" % rmse_test)
results.close()
def run_mlp(func, step, momentum, X, Z, TX, TZ, wd, opt, counter, arch, batches):
print func, step, momentum, wd, opt, counter, arch, batches
seed = 3453
np.random.seed(seed)
batch_size = batches
#max_iter = max_passes * X.shape[ 0] / batch_size
max_iter = 25000000
n_report = X.shape[0] / batch_size
weights = []
input_size = len(X[0])
stop = climin.stops.AfterNIterations(max_iter)
pause = climin.stops.ModuloNIterations(n_report)
optimizer = opt, {'step_rate': step, 'momentum': momentum}
typ = 'plain'
if typ == 'plain':
m = Mlp(input_size, arch, 1, X, Z, hidden_transfers=func, out_transfer='identity', loss='squared', optimizer=optimizer, batch_size=batch_size, max_iter=max_iter)
elif typ == 'fd':
m = FastDropoutNetwork(2099, arch, 1, X, Z, TX, TZ,
hidden_transfers=['tanh', 'tanh'], out_transfer='identity', loss='squared',
p_dropout_inpt=.1,
p_dropout_hiddens=.2,
optimizer=optimizer, batch_size=batch_size, max_iter=max_iter)
#climin.initialize.randomize_normal(m.parameters.data, 0, 1 / np.sqrt(m.n_inpt))
# Transform the test data
#TX = m.transformedData(TX)
TX = np.array([m.transformedData(TX) for _ in range(10)]).mean(axis=0)
print TX.shape
losses = []
print 'max iter', max_iter
m.init_weights()
X, Z, TX, TZ = [breze.learn.base.cast_array_to_local_type(i) for i in (X, Z, TX, TZ)]
for layer in m.mlp.layers:
weights.append(m.parameters[layer.weights])
weight_decay = ((weights[0]**2).sum()
+ (weights[1]**2).sum()
+ (weights[2]**2).sum()
)
weight_decay /= m.exprs['inpt'].shape[0]
m.exprs['true_loss'] = m.exprs['loss']
c_wd = wd
m.exprs['loss'] = m.exprs['loss'] + c_wd * weight_decay
mae = T.abs_((m.exprs['output'] * np.std(train_labels) + np.mean(train_labels))- m.exprs['target']).mean()
f_mae = m.function(['inpt', 'target'], mae)
rmse = T.sqrt(T.square((m.exprs['output'] * np.std(train_labels) + np.mean(train_labels))- m.exprs['target']).mean())
f_rmse = m.function(['inpt', 'target'], rmse)
start = time.time()
# Set up a nice printout.
keys = '#', 'seconds', 'loss', 'val loss', 'mae_train', 'rmse_train', 'mae_test', 'rmse_test'
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 = cp.load(tp)
m.parameters.data[...] = best_pars
for i, info in enumerate(m.powerfit((X, Z), (TX, TZ), stop, pause)):
if info['n_iter'] % n_report != 0:
continue
passed = time.time() - start
losses.append((info['loss'], info['val_loss']))
info.update({
'time': passed,
'mae_train': f_mae(m.transformedData(X), train_labels),
'rmse_train': f_rmse(m.transformedData(X), train_labels),
'mae_test': f_mae(TX, test_labels),
'rmse_test': f_rmse(TX, test_labels)
})
info['n_iter'] += n_iter
row = '%(n_iter)i\t%(time)g\t%(loss)f\t%(val_loss)f\t%(mae_train)g\t%(rmse_train)g\t%(mae_test)g\t%(rmse_test)g' % 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']), fp)
with open("hps"+str(counter)+".pkl", 'wb') as tp:
cp.dump((func, step, momentum, wd, opt, counter, info['n_iter'], arch, batches), tp)
m.parameters.data[...] = info['best_pars']
cp.dump(info['best_pars'], open('best_pars.pkl', 'wb'))
Y = m.predict(m.transformedData(X))
TY = m.predict(TX)
output_train = Y * np.std(train_labels) + np.mean(train_labels)
output_test = TY * np.std(train_labels) + np.mean(train_labels)
print 'TRAINING SET\n'
print('MAE: %5.2f kcal/mol'%np.abs(output_train - train_labels).mean(axis=0))
print('RMSE: %5.2f kcal/mol'%np.square(output_train - train_labels).mean(axis=0) ** .5)
print 'TESTING SET\n'
print('MAE: %5.2f kcal/mol'%np.abs(output_test - test_labels).mean(axis=0))
print('RMSE: %5.2f kcal/mol'%np.square(output_test - test_labels).mean(axis=0) ** .5)
mae_train = np.abs(output_train - train_labels).mean(axis=0)
rmse_train = np.square(output_train - train_labels).mean(axis=0) ** .5
mae_test = np.abs(output_test - test_labels).mean(axis=0)
rmse_test = np.square(output_test - test_labels).mean(axis=0) ** .5
results = open('result.txt', 'a')
results.write('Training set:\n')
results.write('MAE:\n')
results.write("%5.2f" %mae_train)
results.write('\nRMSE:\n')
results.write("%5.2f" %rmse_train)
results.write('\nTesting set:\n')
results.write('MAE:\n')
results.write("%5.2f" %mae_test)
results.write('\nRMSE:\n')
results.write("%5.2f" %rmse_test)
results.close()
batch_size = 25
#max_iter = max_passes * X.shape[ 0] / batch_size
max_iter = 75000000
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 = 'plain'
if typ == 'plain':
m = Mlp(2099, [400, 100], 1, X, Z,
hidden_transfers=['tanh', 'tanh'], out_transfer='identity', loss='squared', optimizer=optimizer, batch_size=batch_size, max_iter=max_iter)
elif typ == 'fd':
m = FastDropoutNetwork(2099, [800, 800], 14, X, Z, TX, TZ,
hidden_transfers=['tanh', 'tanh'], out_transfer='identity', loss='squared',
p_dropout_inpt=.1,
p_dropout_hiddens=.2,
optimizer=optimizer, batch_size=batch_size, max_iter=max_iter)
#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)
#max_iter = max_passes * X.shape[ 0] / batch_size
max_iter = 75000000
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 = 'plain'
if typ == 'plain':
m = Mlp(2099, [400, 100],
1,
X,
Z,
hidden_transfers=['tanh', 'tanh'],
out_transfer='identity',
loss='squared',
optimizer=optimizer,
batch_size=batch_size,
max_iter=max_iter)
elif typ == 'fd':
m = FastDropoutNetwork(2099, [800, 800],
14,
X,
Z,
TX,
TZ,
hidden_transfers=['tanh', 'tanh'],
out_transfer='identity',
loss='squared',
p_dropout_inpt=.1,
