def generate_model(class_num):
models_folder = "models"
optimizer = 'rmsprop', {'steprate': 0.0001, 'momentum': 0.9, 'decay': 0.9, 'step_adapt': False}
feature_size = len(testing_dataset.tfidf.vocabulary_)
num_examples = 200
batch_size = num_examples
max_iter = 3000
m = GeneralizedLinearSparseModel(feature_size, 1, out_transfer='sigmoid', loss='fmeasure',
optimizer=optimizer, batch_size=batch_size, max_iter=max_iter,
num_examples=num_examples)
weight_decay = ((m.parameters.in_to_out ** 2).sum())
weight_decay /= m.exprs['inpt'].shape[0]
m.exprs['true_loss'] = m.exprs['loss']
c_wd = 0.001
m.exprs['loss'] += c_wd * weight_decay
parameters_file = os.path.join(models_folder, "class%d.npy" % class_num)
m.parameters.data = np.load(parameters_file)
return m
def run(self):
models_folder = 'models'
num_examples = 1000
batch_size = num_examples
max_iter = 700
actual_time = time.time()
new_time = time.time()
print "Time spent in transforming the training dataset: "+str(new_time-actual_time)
actual_time = new_time
VX, VZ = self.get_validation_data()
new_time = time.time()
print "Time spent in transforming the validation dataset: "+str(new_time-actual_time)
stop = climin.stops.any_([
climin.stops.after_n_iterations(max_iter),
])
pause = climin.stops.modulo_n_iterations(10)
optimizer = 'rmsprop', {'steprate': 0.01, 'momentum': 0.9, 'decay': 0.9, 'step_adapt': 0.001}
m = GeneralizedLinearSparseModel(self.feature_size, 1, out_transfer='sigmoid', loss='fmeasure',
optimizer=optimizer, batch_size=batch_size, max_iter=max_iter,
num_examples=num_examples)
v_losses = []
weight_decay = ((m.parameters.in_to_out ** 2).sum())
weight_decay /= m.exprs['inpt'].shape[0]
m.exprs['true_loss'] = m.exprs['loss']
c_wd = 0.001
m.exprs['loss'] = m.exprs['loss'] + c_wd * weight_decay
# Set up a nice printout.
keys = '#', 'val loss'
max_len = max(len(i) for i in keys)
header = ' '.join(i.ljust(max_len) for i in keys)
print header
print '-' * len(header)
bp = None
for i, info in enumerate(m.powerfit(self.fit_data, (VX, VZ), stop, pause)):
v_losses.append(info['val_loss'])
bp = info['best_pars']
row = '%i' % i, '%.6f' % (1-info['val_loss'])
print ' '.join(i.ljust(max_len) for i in row)
np.save(os.path.join(models_folder, 'class%d' % self.class_num), bp)
def run(self):
models_folder = 'models'
num_examples = 1000
batch_size = num_examples
max_iter = 700
actual_time = time.time()
new_time = time.time()
print "Time spent in transforming the training dataset: "+str(new_time-actual_time)
actual_time = new_time
VX, VZ = self.get_validation_data()
new_time = time.time()
print "Time spent in transforming the validation dataset: "+str(new_time-actual_time)
stop = climin.stops.any_([
climin.stops.after_n_iterations(max_iter),
])
pause = climin.stops.modulo_n_iterations(10)
optimizer = 'rmsprop', {'steprate': 0.01, 'momentum': 0.9, 'decay': 0.9, 'step_adapt': 0.001}
m = GeneralizedLinearSparseModel(self.feature_size, 1, out_transfer='sigmoid', loss='fmeasure',
optimizer=optimizer, batch_size=batch_size, max_iter=max_iter,
num_examples=num_examples)
v_losses = []
weight_decay = ((m.parameters.in_to_out ** 2).sum())
weight_decay /= m.exprs['inpt'].shape[0]
m.exprs['true_loss'] = m.exprs['loss']
c_wd = 0.001
m.exprs['loss'] = m.exprs['loss'] + c_wd * weight_decay
# Set up a nice printout.
keys = '#', 'val loss'
max_len = max(len(i) for i in keys)
header = ' '.join(i.ljust(max_len) for i in keys)
print header
print '-' * len(header)
bp = None
for i, info in enumerate(m.powerfit(self.fit_data, (VX, VZ), stop, pause)):
v_losses.append(info['val_loss'])
bp = info['best_pars']
row = '%i' % i, '%.6f' % (1-info['val_loss'])
print ' '.join(i.ljust(max_len) for i in row)
np.save(os.path.join(models_folder, 'class%d' % self.class_num), bp)
