def new_trainer(self, pars, data):
modules = ['theano', 'breze', 'climin', 'alchemie']
git_log(modules)
copy_theanorc()
m = Mlp(2, [pars['n_hidden']],
1,
hidden_transfers=[pars['hidden_transfer']],
out_transfer='sigmoid',
loss='bern_ces',
optimizer=pars['optimizer'])
climin.initialize.randomize_normal(m.parameters.data, 0,
pars['par_std'])
n_report = 100
t = Trainer(
model=m,
data=data,
stop=climin.stops.Any([
climin.stops.AfterNIterations(10000),
climin.stops.NotBetterThanAfter(1e-1, 5000, key='val_loss')
]),
pause=climin.stops.ModuloNIterations(n_report),
report=OneLinePrinter(
keys=['n_iter', 'runtime', 'train_loss', 'val_loss'],
spaces=[6, '10.2f', '15.8f', '15.8f']),
interrupt=climin.stops.OnSignal(),
)
return t
def new_trainer(pars, data):
m = Mlp(2, [pars['n_hidden']],
1,
hidden_transfers=[pars['hidden_transfer']],
out_transfer='sigmoid',
loss='bern_ces',
optimizer=pars['optimizer'])
climin.initialize.randomize_normal(m.parameters.data, 0, pars['par_std'])
n_report = 100
interrupt = climin.stops.OnSignal()
print dir(climin.stops)
stop = climin.stops.Any([
climin.stops.AfterNIterations(10000),
climin.stops.OnSignal(signal.SIGTERM),
climin.stops.NotBetterThanAfter(1e-1, 500, key='train_loss'),
])
pause = climin.stops.ModuloNIterations(n_report)
reporter = KeyPrinter(['n_iter', 'train_loss'])
t = Trainer(m,
stop=stop,
pause=pause,
report=reporter,
interrupt=interrupt)
make_data_dict(t, data)
return t
def test_mlp_fit():
X = np.random.standard_normal((10, 2))
Z = np.random.standard_normal((10, 1))
X, Z = theano_floatx(X, Z)
mlp = Mlp(2, [10], 1, ['tanh'], 'identity', 'squared', max_iter=10)
mlp.fit(X, Z)
def test_mlp_iter_fit():
X = np.random.standard_normal((10, 2))
Z = np.random.standard_normal((10, 1))
X, Z = theano_floatx(X, Z)
mlp = Mlp(2, [10], 1, ['tanh'], 'identity', 'squared', max_iter=10)
for i, info in enumerate(mlp.iter_fit(X, Z)):
if i >= 10:
break
def test_mlp_fit_with_imp_weight():
X = np.random.standard_normal((10, 2))
Z = np.random.standard_normal((10, 1))
W = np.random.random((10, 1)) > 0.5
X, Z, W = theano_floatx(X, Z, W)
mlp = Mlp(2, [10], 1, ['tanh'], 'identity', 'squared', max_iter=10, imp_weight=True)
mlp.fit(X, Z, W)
def test_mlp_pickle():
X = np.random.standard_normal((10, 2))
Z = np.random.standard_normal((10, 1))
X, Z = theano_floatx(X, Z)
mlp = Mlp(2, [10], 1, ['tanh'], 'identity', 'squared', max_iter=2)
climin.initialize.randomize_normal(mlp.parameters.data, 0, 1)
mlp.fit(X, Z)
Y = mlp.predict(X)
pickled = cPickle.dumps(mlp)
mlp2 = cPickle.loads(pickled)
Y2 = mlp2.predict(X)
assert np.allclose(Y, Y2)
def test_mlp_predict():
X = np.random.standard_normal((10, 2))
X, = theano_floatx(X)
mlp = Mlp(2, [10], 1, ['tanh'], 'identity', 'squared', max_iter=10)
mlp.predict(X)
#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, [800, 800],
15,
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],
15,
X,
Z,
TX,
TZ,
hidden_transfers=['tanh', 'tanh'],
out_transfer='identity',
loss='squared',
p_dropout_inpt=.1,
def do_one_eval(X, Z, TX, TZ, test_labels, train_labels, step_rate, momentum,
decay, c_wd, counter, opt):
seed = 3453
np.random.seed(seed)
max_passes = 200
batch_size = 25
max_iter = 5000000
n_report = X.shape[0] / batch_size
weights = []
optimizer = 'gd', {
'step_rate': step_rate,
'momentum': momentum,
'decay': decay
}
stop = climin.stops.AfterNIterations(max_iter)
pause = climin.stops.ModuloNIterations(n_report)
# This defines our NN. Since BayOpt does not support categorical data, we just
# use a fixed hidden layer length and transfer functions.
m = Mlp(2100, [400, 100],
1,
X,
Z,
hidden_transfers=['tanh', 'tanh'],
out_transfer='identity',
loss='squared',
optimizer=optimizer,
batch_size=batch_size,
max_iter=max_iter)
#climin.initialize.randomize_normal(m.parameters.data, 0, 1e-3)
# Transform the test data
#TX = m.transformedData(TX)
TX = np.array([m.transformedData(TX) for _ in range(10)]).mean(axis=0)
losses = []
print 'max iter', max_iter
m.init_weights()
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 = c_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.txt', 'a')
results.write(header + '\n')
results.write('-' * len(header) + '\n')
results.write("%f %f %f %f %s" % (step_rate, momentum, decay, c_wd, opt))
results.write('\n')
results.close()
EXP_DIR = os.getcwd()
base_path = os.path.join(EXP_DIR, "pars_hp_" + opt + str(counter) + ".pkl")
n_iter = 0
if os.path.isfile(base_path):
with open("pars_hp_" + opt + str(counter) + ".pkl", 'rb') as tp:
n_iter, best_pars = dill.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
if math.isnan(info['loss']) == True:
info.update({'mae_test': f_mae(TX, test_labels)})
n_iter = info['n_iter']
break
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.txt', 'a')
print row
results.write(row + '\n')
results.close()
with open("pars_hp_" + opt + str(counter) + ".pkl", 'wb') as fp:
dill.dump((info['n_iter'], info['best_pars']), fp)
with open("apsis_pars_" + opt + str(counter) + ".pkl", 'rb') as fp:
LAss, opt, step_rate, momentum, decay, c_wd, counter, n_iter1, result1 = dill.load(
fp)
n_iter1 = info['n_iter']
result1 = info['mae_test']
with open("apsis_pars_" + opt + str(counter) + ".pkl", 'wb') as fp:
dill.dump((LAss, opt, step_rate, momentum, decay, c_wd, counter,
n_iter1, result1), fp)
return info['mae_test'], info['n_iter']
def run_mlp(arch, func, step, batch, X, Z, TX, TZ, wd, opt):
batch_size = batch
#max_iter = max_passes * X.shape[ 0] / batch_size
max_iter = 100000
n_report = X.shape[0] / batch_size
weights = []
input_size = len(X[0])
train_labels = Z
test_labels = TZ
stop = climin.stops.AfterNIterations(max_iter)
pause = climin.stops.ModuloNIterations(n_report)
optimizer = opt, {'step_rate': step}
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, [400, 100], 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)
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()
+ (weights[3]**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
'''
weight_decay = ((m.parameters.in_to_hidden**2).sum()
+ (m.parameters.hidden_to_out**2).sum()
+ (m.parameters.hidden_to_hidden_0**2).sum())
weight_decay /= m.exprs['inpt'].shape[0]
m.exprs['true_loss'] = m.exprs['loss']
c_wd = 0.1
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.txt', 'a')
results.write(header + '\n')
results.write('-' * len(header) + '\n')
results.close()
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)
})
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.txt','a')
print row
results.write(row + '\n')
results.close()
m.parameters.data[...] = info['best_pars']
cp.dump(info['best_pars'], open('best_pars.pkl', 'w'))
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 do_one_eval(X, Z, VX, VZ, step_rate, momentum, decay, c_wd):
"""
Does one evaluation of a neural network with the above parameters.
Parameters
----------
X, Z : matrix
Feature and Target matrices of the training set, one-hot encoded.
VX, VZ : matrix
Feature and Target matrices of the validation set, one-hot encoded.
step_rate : float
The step-rate/learning rate of the rmsprop-algorithm
momentum : float
The momentum of the rmsprop.
decay : float
The step-rate decay
c_wd : float
Penalty term for the weight
Returns
-------
val_emp : float
The percentage of wrongly classified samples.
"""
max_passes = 100
batch_size = 250
max_iter = max_passes * X.shape[0] / batch_size
n_report = X.shape[0] / batch_size
optimizer = 'rmsprop', {
'step_rate': step_rate,
'momentum': momentum,
'decay': decay
}
# This defines our NN. Since BayOpt does not support categorical data, we just
# use a fixed hidden layer length and transfer functions.
m = Mlp(784, [800],
10,
hidden_transfers=['sigmoid'],
out_transfer='softmax',
loss='cat_ce',
optimizer=optimizer,
batch_size=batch_size)
climin.initialize.randomize_normal(m.parameters.data, 0, 1e-1)
losses = []
weight_decay = ((m.parameters.in_to_hidden**2).sum() +
(m.parameters.hidden_to_out**2).sum())
weight_decay /= m.exprs['inpt'].shape[0]
m.exprs['true_loss'] = m.exprs['loss']
c_wd = c_wd
m.exprs['loss'] = m.exprs['loss'] + c_wd * weight_decay
n_wrong = 1 - T.eq(T.argmax(m.exprs['output'], axis=1),
T.argmax(m.exprs['target'], axis=1)).mean()
f_n_wrong = m.function(['inpt', 'target'], n_wrong)
stop = climin.stops.AfterNIterations(max_iter)
pause = climin.stops.ModuloNIterations(n_report)
start = time.time()
# Set up a nice printout.
keys = '#', 'seconds', 'loss', 'val loss', 'train emp', 'val emp'
max_len = max(len(i) for i in keys)
header = '\t'.join(i for i in keys)
#print header
#print '-' * len(header)
for i, info in enumerate(m.powerfit((X, Z), (VX, VZ), stop, pause)):
passed = time.time() - start
losses.append((info['loss'], info['val_loss']))
#img = tile_raster_images(fe.parameters['in_to_hidden'].T, image_dims, feature_dims, (1, 1))
#save_and_display(img, 'filters-%i.png' % i)
info.update({
'time': passed,
'train_emp': f_n_wrong(X, Z),
'val_emp': f_n_wrong(VX, VZ),
})
row = '%(n_iter)i\t%(time)g\t%(loss)g\t%(val_loss)g\t%(train_emp)g\t%(val_emp)g' % info
# Comment in this row if you want updates during the computation.
#print row
return info["val_emp"]