# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
import hebel
hebel.init(0)
import unittest
import random
import numpy as np
from pycuda import gpuarray
from pycuda.curandom import rand as curand
from hebel import sampler
from hebel.models import NeuralNet, NeuralNetRegression
from hebel.optimizers import SGD
from hebel.parameter_updaters import SimpleSGDUpdate, \
MomentumUpdate, NesterovMomentumUpdate
from hebel.data_providers import MNISTDataProvider, BatchDataProvider
from hebel.monitors import SimpleProgressMonitor
from hebel.schedulers import exponential_scheduler, linear_scheduler_up, \
constant_scheduler
#!/usr/bin/env python
import hebel
from hebel.models import NeuralNet
from hebel.optimizers import SGD
from hebel.parameter_updaters import MomentumUpdate
from hebel.data_providers import MNISTDataProvider
from hebel.monitors import ProgressMonitor
from hebel.schedulers import exponential_scheduler, linear_scheduler_up
hebel.init(random_seed=0)
# Initialize data providers
train_data = MNISTDataProvider('train', batch_size=100)
validation_data = MNISTDataProvider('val')
test_data = MNISTDataProvider('test')
D = train_data.D # Dimensionality of inputs
K = 10 # Number of classes
# Create model object
model = NeuralNet(n_in=train_data.D,
n_out=K,
layers=[2000, 2000, 2000, 500],
activation_function='relu',
dropout=True,
input_dropout=0.2)
# Create optimizer object
progress_monitor = ProgressMonitor(experiment_name='mnist',
save_model_path='examples/mnist',
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
import hebel
hebel.init(0)
import unittest
import random
import numpy as np
from pycuda import gpuarray
from pycuda.curandom import rand as curand
from hebel import sampler
from hebel.models import NeuralNet, NeuralNetRegression
from hebel.optimizers import SGD
from hebel.parameter_updaters import SimpleSGDUpdate, \
MomentumUpdate, NesterovMomentumUpdate
from hebel.data_providers import MNISTDataProvider, BatchDataProvider
from hebel.monitors import SimpleProgressMonitor
from hebel.schedulers import exponential_scheduler, linear_scheduler_up, \
constant_scheduler
#!/usr/bin/env python
import hebel
from hebel.models import NeuralNet
from hebel.optimizers import SGD
from hebel.parameter_updaters import MomentumUpdate
from hebel.data_providers import MNISTDataProvider
from hebel.monitors import ProgressMonitor
from hebel.schedulers import exponential_scheduler, linear_scheduler_up
hebel.init(random_seed=0)
# Initialize data providers
train_data = MNISTDataProvider("train", batch_size=100)
validation_data = MNISTDataProvider("val")
test_data = MNISTDataProvider("test")
D = train_data.D # Dimensionality of inputs
K = 10 # Number of classes
# Create model object
model = NeuralNet(
n_in=train_data.D,
n_out=K,
layers=[2000, 2000, 2000, 500],
activation_function="relu",
dropout=True,
input_dropout=0.2,
)
# Create optimizer object
