def cnn_run_dropout_maxout(data_path, num_rows, num_cols, num_channels,
input_path, pred_path):
t = time.time()
sub_window = gen_center_sub_window(76, num_cols)
trn = SarDataset(ds[0][0], ds[0][1], sub_window)
vld = SarDataset(ds[1][0], ds[1][1], sub_window)
tst = SarDataset(ds[2][0], ds[2][1], sub_window)
print 'Take {}s to read data'.format(time.time() - t)
t = time.time()
batch_size = 100
h1 = maxout.Maxout(layer_name='h2', num_units=1, num_pieces=100, irange=.1)
hidden_layer = mlp.ConvRectifiedLinear(layer_name='h2',
output_channels=8,
irange=0.05,
kernel_shape=[5, 5],
pool_shape=[2, 2],
pool_stride=[2, 2],
max_kernel_norm=1.9365)
hidden_layer2 = mlp.ConvRectifiedLinear(layer_name='h3',
output_channels=8,
irange=0.05,
kernel_shape=[5, 5],
pool_shape=[2, 2],
pool_stride=[2, 2],
max_kernel_norm=1.9365)
#output_layer = mlp.Softplus(dim=1,layer_name='output',irange=0.1)
output_layer = mlp.Linear(dim=1, layer_name='output', irange=0.05)
trainer = sgd.SGD(learning_rate=0.001,
batch_size=100,
termination_criterion=EpochCounter(2000),
cost=dropout.Dropout(),
train_iteration_mode='even_shuffled_sequential',
monitor_iteration_mode='even_shuffled_sequential',
monitoring_dataset={
'test': tst,
'valid': vld,
'train': trn
})
layers = [hidden_layer, hidden_layer2, output_layer]
input_space = space.Conv2DSpace(shape=[num_rows, num_cols],
num_channels=num_channels)
ann = mlp.MLP(layers, input_space=input_space, batch_size=batch_size)
watcher = best_params.MonitorBasedSaveBest(channel_name='valid_objective',
save_path='sar_cnn_mlp.pkl')
experiment = Train(dataset=trn,
model=ann,
algorithm=trainer,
extensions=[watcher])
print 'Take {}s to compile code'.format(time.time() - t)
t = time.time()
experiment.main_loop()
print 'Training time: {}s'.format(time.time() - t)
serial.save('cnn_hhv_{0}_{1}.pkl'.format(num_rows, num_cols),
ann,
on_overwrite='backup')
#read hh and hv into a 3D numpy
image = read_hhv(input_path)
return ann, sar_predict(ann, image, pred_path)
# termination criterion that stops after 50 epochs without
# any increase in misclassification on the validation set
termination_criterion = MonitorBased(channel_name='objective', N=20, prop_decrease=0.0)
# create Stochastic Gradient Descent trainer
trainer = sgd.SGD(learning_rate=.001,
batch_size=10,
monitoring_dataset=ds_valid,
termination_criterion=termination_criterion,
cost=L1_cost)
#learning_rule=momentum_rule,
trainer.setup(ann, ds_train)
# add monitor for saving the model with best score
monitor_save_best = best_params.MonitorBasedSaveBest('objective','./tmp/best.pkl')
#####################################
#Train model
####################################
# train neural net until the termination criterion is true
while True:
trainer.train(dataset=ds_train)
ann.monitor.report_epoch()
ann.monitor()
monitor_save_best.on_monitor(ann, ds_valid, trainer)
if not trainer.continue_learning(ann):
break
def main():
training_data, validation_data, test_data, std_scale = load_training_data()
kaggle_test_features = load_test_data(std_scale)
###############
# pylearn2 ML
hl1 = mlp.Sigmoid(layer_name='hl1', dim=200, irange=.1, init_bias=1.)
hl2 = mlp.Sigmoid(layer_name='hl2', dim=100, irange=.1, init_bias=1.)
# create Softmax output layer
output_layer = mlp.Softmax(9, 'output', irange=.1)
# create Stochastic Gradient Descent trainer that runs for 400 epochs
trainer = sgd.SGD(learning_rate=.05,
batch_size=300,
learning_rule=learning_rule.Momentum(.5),
termination_criterion=MonitorBased(
channel_name='valid_objective',
prop_decrease=0.,
N=10),
monitoring_dataset={
'valid': validation_data,
'train': training_data
})
layers = [hl1, hl2, output_layer]
# create neural net
model = mlp.MLP(layers, nvis=93)
watcher = best_params.MonitorBasedSaveBest(
channel_name='valid_objective',
save_path='pylearn2_results/pylearn2_test.pkl')
velocity = learning_rule.MomentumAdjustor(final_momentum=.6,
start=1,
saturate=250)
decay = sgd.LinearDecayOverEpoch(start=1, saturate=250, decay_factor=.01)
######################
experiment = Train(dataset=training_data,
model=model,
algorithm=trainer,
extensions=[watcher, velocity, decay])
experiment.main_loop()
#load best model and test
################
model = serial.load('pylearn2_results/pylearn2_test.pkl')
# get an prediction of the accuracy from the test_data
test_results = model.fprop(theano.shared(test_data[0],
name='test_data')).eval()
print test_results.shape
loss = multiclass_log_loss(test_data[1], test_results)
print 'Test multiclass log loss:', loss
out_file = 'pylearn2_results/' + str(loss) + 'ann'
#exp.save(out_file + '.pkl')
#save the kaggle results
results = model.fprop(
theano.shared(kaggle_test_features, name='kaggle_test_data')).eval()
save_results(out_file + '.csv', kaggle_test_features, results)
lr = 0.1
epochs = 400
trainer = sgd.SGD(
learning_rate=lr,
batch_size=100,
learning_rule=learning_rule.Momentum(.05),
# Remember, default dropout is .5
#cost=Dropout(input_include_probs={'l1': .5},
# input_scales={'l1': 1.}),
termination_criterion=EpochCounter(epochs),
monitoring_dataset={
'train': ds,
'valid': ds_test
})
watcher = best_params.MonitorBasedSaveBest(channel_name='valid_roc_auc',
save_path='saved_clf.pkl')
velocity = learning_rule.MomentumAdjustor(final_momentum=.9,
start=1,
saturate=250)
decay = sgd.LinearDecayOverEpoch(start=1, saturate=250, decay_factor=lr * .05)
rocauc = roc_auc.RocAucChannel()
experiment = Train(dataset=ds,
model=ann,
algorithm=trainer,
extensions=[watcher, velocity, decay, rocauc])
experiment.main_loop()
termination_criterion=EpochCounter(max_epochs=max_epochs),
monitoring_dataset={'train':X_train, 'valid':X_test},
)
input_space = Conv2DSpace(shape=(central_window_shape, central_window_shape),
axes = axes,
num_channels = 1)
ann = mlp.MLP(layers, input_space=input_space)
velocity = learning_rule.MomentumAdjustor(final_momentum=momentum_end,
start=1,
saturate=momentum_saturate)
watcher = best_params.MonitorBasedSaveBest(channel_name='valid_y_nll',
save_path=save_path)
decay = sgd.LinearDecayOverEpoch(start=1, saturate=decay_saturate, decay_factor=decay_factor)
ra = RealtimeAugment(window_shape=[img_dim, img_dim], randomize=[X_train, X_test],
scale_diff=scale_diff, translation=translation, center_shape=center_shape, center=[X_train, X_test],
preprocess=preprocess)
train = Train(dataset=X_train, model=ann, algorithm=trainer,
extensions=[watcher, velocity, decay, ra])
train.main_loop()
print "using model", save_path
model = serial.load(save_path)
batch_size=128,
learning_rule=learning_rule.Momentum(.5),
# Remember, default dropout is .5
cost=Dropout(input_include_probs={'l1': .8},
input_scales={'l1': 1.}),
termination_criterion=EpochCounter(max_epochs=475),
monitoring_dataset={'valid': tst,
'train': trn})
preprocessor = Pipeline([GlobalContrastNormalization(scale=55.), ZCA()])
trn.apply_preprocessor(preprocessor=preprocessor, can_fit=True)
tst.apply_preprocessor(preprocessor=preprocessor, can_fit=False)
serial.save('kaggle_cifar10_preprocessor.pkl', preprocessor)
watcher = best_params.MonitorBasedSaveBest(
channel_name='valid_y_misclass',
save_path='kaggle_cifar10_maxout_zca.pkl')
velocity = learning_rule.MomentumAdjustor(final_momentum=.65,
start=1,
saturate=250)
decay = sgd.LinearDecayOverEpoch(start=1,
saturate=500,
decay_factor=.01)
win = window_flip.WindowAndFlipC01B(pad_randomized=8,
window_shape=(32, 32),
randomize=[trn],
center=[tst])
def cnn_train_tranformer(train_path,
test_path,
valid_path,
save_path,
predict_path,
num_rows=28,
num_cols=28,
num_channels=2,
batch_size=128,
output_channels=[64, 64],
kernel_shape=[[12, 12], [5, 5]],
pool_shape=[[4, 4], [2, 2]],
pool_stride=[[2, 2], [2, 2]],
irange=[0.05, 0.05, 0.05],
max_kernel_norm=[1.9365, 1.9365],
learning_rate=0.001,
init_momentum=0.9,
weight_decay=[0.0002, 0.0002, 0.0002],
n_epoch=1000,
image_path=''):
ds = load_data_transformed(train_path, num_cols, batch_size)
ds = (np.transpose(ds[0], axes=[0, 3, 1, 2]), ds[1])
trn = SarDataset(np.array(ds[0]), ds[1])
ds = load_data_transformed(valid_path, num_cols, batch_size)
ds = (np.transpose(ds[0], axes=[0, 3, 1, 2]), ds[1])
vld = SarDataset(np.array(ds[0]), ds[1])
ds = load_data_transformed(test_path, num_cols, batch_size)
ds = (np.transpose(ds[0], axes=[0, 3, 1, 2]), ds[1])
tst = SarDataset(np.array(ds[0]), ds[1])
#setup the network
#X = np.random.random([400000,2,41,41])
#y = np.random.random([400000,1])
#trn = SarDataset(X,y)
#X = np.random.random([60000,2,41,41])
#y = np.random.random([60000,1])
#tst = SarDataset(X,y)
#X = np.random.random([60000,2,41,41])
#y = np.random.random([60000,1])
#vld = SarDataset(X,y)
t = time.time()
layers = []
for i in range(len(output_channels)):
layer_name = 'h{}'.format(i + 1)
convlayer = mlp.ConvRectifiedLinear(layer_name=layer_name,
output_channels=output_channels[i],
irange=irange[i],
kernel_shape=kernel_shape[i],
pool_shape=pool_shape[i],
pool_stride=pool_stride[i],
max_kernel_norm=max_kernel_norm[i])
layers.append(convlayer)
output_mlp = mlp.Linear(dim=1, layer_name='output', irange=irange[-1])
#output_mlp = mlp.linear_mlp_bayesian_cost(dim=1,layer_name='output',irange=irange[-1])
layers.append(output_mlp)
trainer = sgd.SGD(
learning_rate=learning_rate,
batch_size=batch_size,
termination_criterion=EpochCounter(n_epoch),
#termination_criterion = termination_criteria.And([termination_criteria.MonitorBased(channel_name = 'train_objective', prop_decrease=0.01,N=10),EpochCounter(n_epoch)]),
#cost = dropout.Dropout(),
cost=cost.SumOfCosts(
[cost.MethodCost('cost_from_X'),
WeightDecay(weight_decay)]),
init_momentum=init_momentum,
train_iteration_mode='even_shuffled_sequential',
monitor_iteration_mode='even_shuffled_sequential',
monitoring_dataset={
'test': tst,
'valid': vld,
'train': trn
})
input_space = space.Conv2DSpace(shape=[num_rows, num_cols],
num_channels=num_channels)
#ann = mlp.MLP(layers,input_space=input_space,batch_size=batch_size)
watcher = best_params.MonitorBasedSaveBest(channel_name='valid_objective',
save_path=predict_path +
save_path)
#flip = window_flip.WindowAndFlip((num_rows,num_cols),randomize=[tst,vld,trn])
experiment = Train(dataset=trn,
model=ann,
algorithm=trainer,
extensions=[watcher])
print 'Take {}s to compile code'.format(time.time() - t)
#train the network
t = time.time()
experiment.main_loop()
print 'Training time: {}h'.format((time.time() - t) / 3600)
utils.sms_notice('Training time:{}'.format((time.time() - t) / 3600))
return ann
def cnn_train(
train_path,
test_path,
valid_path,
save_path,
predict_path,
image_path,
num_rows=28,
num_cols=28,
num_channels=2,
batch_size=128,
output_channels=[64, 64],
kernel_shape=[[12, 12], [5, 5]],
pool_shape=[[4, 4], [2, 2]],
pool_stride=[[2, 2], [2, 2]],
irange=[0.05, 0.05, 0.05],
max_kernel_norm=[1.9365, 1.9365],
learning_rate=0.001,
init_momentum=0.9,
weight_decay=[0.0002, 0.0002, 0.0002],
n_epoch=1000,
):
#load data
#t = time.time()
ds = load_data(valid_path, num_rows, num_cols, num_channels)
vld = SarDataset(np.array(ds[0]), ds[1])
ds = load_data(train_path, num_rows, num_cols, num_channels)
trn = SarDataset(np.array(ds[0]), ds[1])
ds = load_data(test_path, num_rows, num_cols, num_channels)
tst = SarDataset(np.array(ds[0]), ds[1])
#load balanced data
#ds = load_data_balance_under_sample(train_path, num_rows,num_cols, num_channels)
#trn = SarDataset(np.array(ds[0]),ds[1])
#ds = load_data_balance(valid_path, num_rows,num_cols, num_channels)
#vld = SarDataset(np.array(ds[0]),ds[1])
#ds = load_data_balance(test_path, num_rows,num_cols, num_channels)
#tst = SarDataset(np.array(ds[0]),ds[1])
#print 'Take {}s to read data'.format( time.time()-t)
#use gaussian convlution on the origional image to see if it can concentrate in the center
#trn,tst,vld = load_data_lidar()
#mytransformer = transformer.TransformationPipeline(input_space=space.Conv2DSpace(shape=[num_rows,num_cols],num_channels=num_channels),transformations=[transformer.Rotation(),transformer.Flipping()])
#trn = contestTransformerDataset.TransformerDataset(trn,mytransformer,space_preserving=True)
#tst = contestTransformerDataset.TransformerDataset(tst,mytransformer,space_preserving=True)
#vld = contestTransformerDataset.TransformerDataset(vld,mytransformer,space_preserving=True)
#trn = transformer_dataset.TransformerDataset(trn,mytransformer,space_preserving=True)
#tst = transformer_dataset.TransformerDataset(tst,mytransformer,space_preserving=True)
#vld = transformer_dataset.TransformerDataset(vld,mytransformer,space_preserving=True)
#setup the network
t = time.time()
layers = []
for i in range(len(output_channels)):
layer_name = 'h{}'.format(i + 1)
convlayer = mlp.ConvRectifiedLinear(layer_name=layer_name,
output_channels=output_channels[i],
irange=irange[i],
kernel_shape=kernel_shape[i],
pool_shape=pool_shape[i],
pool_stride=pool_stride[i],
max_kernel_norm=max_kernel_norm[i])
layers.append(convlayer)
output_mlp = mlp.Linear(dim=1,
layer_name='output',
irange=irange[-1],
use_abs_loss=True)
#output_mlp = mlp.linear_mlp_ace(dim=1,layer_name='output',irange=irange[-1])
layers.append(output_mlp)
#ann = cPickle.load(open('../output/train_with_2010_2l_40_64/original_500/f/f0.pkl'))
#layers = []
#for layer in ann.layers:
# layer.set_mlp_force(None)
# layers.append(layer)
trainer = sgd.SGD(
learning_rate=learning_rate,
batch_size=batch_size,
termination_criterion=EpochCounter(n_epoch),
#termination_criterion = termination_criteria.And([termination_criteria.MonitorBased(channel_name = 'train_objective', prop_decrease=0.01,N=10),EpochCounter(n_epoch)]),
#cost = dropout.Dropout(),
cost=cost.SumOfCosts(
[cost.MethodCost('cost_from_X'),
WeightDecay(weight_decay)]),
init_momentum=init_momentum,
train_iteration_mode='even_shuffled_sequential',
monitor_iteration_mode='even_shuffled_sequential',
monitoring_dataset={
'test': tst,
'valid': vld,
'train': trn
})
input_space = space.Conv2DSpace(shape=[num_rows, num_cols],
num_channels=num_channels)
#ann = mlp.MLP(layers,input_space=input_space,batch_size=batch_size)
ann = serial.load(
'../output/train_with_2010_2l_40_64/original_500/f/f0.pkl')
ann = monitor.push_monitor(ann, 'stage_0')
watcher = best_params.MonitorBasedSaveBest(channel_name='valid_objective',
save_path=predict_path +
save_path)
flip = window_flip.WindowAndFlip((num_rows, num_cols),
randomize=[tst, vld, trn])
experiment = Train(dataset=trn,
model=ann,
algorithm=trainer,
extensions=[watcher, flip])
print 'Take {}s to compile code'.format(time.time() - t)
#train the network
t = time.time()
experiment.main_loop()
print 'Training time: {}h'.format((time.time() - t) / 3600)
utils.sms_notice('Training time:{}'.format((time.time() - t) / 3600))
return ann
def supervisedLayerwisePRL(trainset, testset):
'''
The supervised layerwise training as used in the PRL Paper.
Input
------
trainset : A path to an hdf5 file created through h5py.
testset : A path to an hdf5 file created through h5py.
'''
batch_size = 100
# Both train and test h5py files are expected to have a 'topo_view' and 'y'
# datasets side them corresponding to the 'b01c' data format as used in pylearn2
# and 'y' equivalent to the one hot encoded labels
trn = HDF5Dataset(filename=trainset,
topo_view='topo_view',
y='y',
load_all=False)
tst = HDF5Dataset(filename=testset,
topo_view='topo_view',
y='y',
load_all=False)
'''
The 1st Convolution and Pooling Layers are added below.
'''
h1 = mlp.ConvRectifiedLinear(layer_name='h1',
output_channels=64,
irange=0.05,
kernel_shape=[4, 4],
pool_shape=[4, 4],
pool_stride=[2, 2],
max_kernel_norm=1.9365)
fc = mlp.RectifiedLinear(layer_name='fc', dim=1500, irange=0.05)
output = mlp.Softmax(layer_name='y',
n_classes=171,
irange=.005,
max_col_norm=1.9365)
layers = [h1, fc, output]
mdl = mlp.MLP(layers,
input_space=Conv2DSpace(shape=(70, 70), num_channels=1))
trainer = sgd.SGD(
learning_rate=0.002,
batch_size=batch_size,
learning_rule=learning_rule.RMSProp(),
cost=SumOfCosts(
costs=[Default(),
WeightDecay(coeffs=[0.0005, 0.0005, 0.0005])]),
train_iteration_mode='shuffled_sequential',
monitor_iteration_mode='sequential',
termination_criterion=EpochCounter(max_epochs=15),
monitoring_dataset={
'test': tst,
'valid': vld
})
watcher = best_params.MonitorBasedSaveBest(
channel_name='valid_y_misclass',
save_path='./Saved Models/conv_supervised_layerwise_best1.pkl')
decay = sgd.LinearDecayOverEpoch(start=8, saturate=15, decay_factor=0.1)
experiment = Train(
dataset=trn,
model=mdl,
algorithm=trainer,
extensions=[watcher, decay],
)
experiment.main_loop()
del mdl
mdl = serial.load('./Saved Models/conv_supervised_layerwise_best1.pkl')
mdl = push_monitor(mdl, 'k')
'''
The 2nd Convolution and Pooling Layers are added below.
'''
h2 = mlp.ConvRectifiedLinear(layer_name='h2',
output_channels=64,
irange=0.05,
kernel_shape=[4, 4],
pool_shape=[4, 4],
pool_stride=[2, 2],
max_kernel_norm=1.9365)
fc = mlp.RectifiedLinear(layer_name='fc', dim=1500, irange=0.05)
output = mlp.Softmax(layer_name='y',
n_classes=171,
irange=.005,
max_col_norm=1.9365)
del mdl.layers[-1]
mdl.layer_names.remove('y')
del mdl.layers[-1]
mdl.layer_names.remove('fc')
mdl.add_layers([h2, fc, output])
trainer = sgd.SGD(learning_rate=0.002,
batch_size=batch_size,
learning_rule=learning_rule.RMSProp(),
cost=SumOfCosts(costs=[
Default(),
WeightDecay(coeffs=[0.0005, 0.0005, 0.0005, 0.0005])
]),
train_iteration_mode='shuffled_sequential',
monitor_iteration_mode='sequential',
termination_criterion=EpochCounter(max_epochs=15),
monitoring_dataset={
'test': tst,
'valid': vld
})
watcher = best_params.MonitorBasedSaveBest(
channel_name='valid_y_misclass',
save_path='./Saved Models/conv_supervised_layerwise_best2.pkl')
decay = sgd.LinearDecayOverEpoch(start=8, saturate=15, decay_factor=0.1)
experiment = Train(
dataset=trn,
model=mdl,
algorithm=trainer,
extensions=[watcher, decay],
)
experiment.main_loop()
del mdl
mdl = serial.load('./Saved Models/conv_supervised_layerwise_best2.pkl')
mdl = push_monitor(mdl, 'l')
'''
The 3rd Convolution and Pooling Layers are added below.
'''
h3 = mlp.ConvRectifiedLinear(layer_name='h2',
output_channels=64,
irange=0.05,
kernel_shape=[4, 4],
pool_shape=[4, 4],
pool_stride=[2, 2],
max_kernel_norm=1.9365)
fc = mlp.RectifiedLinear(layer_name='h3', dim=1500, irange=0.05)
output = mlp.Softmax(layer_name='y',
n_classes=10,
irange=.005,
max_col_norm=1.9365)
del mdl.layers[-1]
mdl.layer_names.remove('y')
del mdl.layers[-1]
mdl.layer_names.remove('fc')
mdl.add_layers([h3, output])
trainer = sgd.SGD(
learning_rate=.002,
batch_size=batch_size,
learning_rule=learning_rule.RMSProp(),
cost=SumOfCosts(costs=[
Default(),
WeightDecay(coeffs=[0.0005, 0.0005, 0.0005, 0.0005, 0.0005])
]),
train_iteration_mode='shuffled_sequential',
monitor_iteration_mode='sequential',
termination_criterion=EpochCounter(max_epochs=15),
monitoring_dataset={
'test': tst,
'valid': vld
})
watcher = best_params.MonitorBasedSaveBest(
channel_name='valid_y_misclass',
save_path='./Saved Models/conv_supervised_layerwise_best3.pkl')
decay = sgd.LinearDecayOverEpoch(start=8, saturate=15, decay_factor=0.1)
experiment = Train(
dataset=trn,
model=mdl,
algorithm=trainer,
extensions=[watcher, decay],
)
experiment.main_loop()
batch_size=32,
learning_rule=learning_rule.Momentum(.5),
# Remember, default dropout is .5
cost=Dropout(input_include_probs={'h2': .8}, input_scales={'h2': 1.}),
termination_criterion=EpochCounter(max_epochs=475),
monitoring_dataset={
'valid': tst,
'train': trn
})
preprocessor = Pipeline([GlobalContrastNormalization(scale=55.), ZCA()])
trn.apply_preprocessor(preprocessor=preprocessor, can_fit=True)
tst.apply_preprocessor(preprocessor=preprocessor, can_fit=False)
serial.save('deepcuts_preprocessor.pkl', preprocessor)
watcher = best_params.MonitorBasedSaveBest(channel_name='valid_y_misclass',
save_path='deepcuts_maxout_zca.pkl')
velocity = learning_rule.MomentumAdjustor(final_momentum=.65,
start=1,
saturate=250)
decay = sgd.LinearDecayOverEpoch(start=1, saturate=500, decay_factor=.01)
'''
win = window_flip.WindowAndFlipC01B(pad_randomized=8,
window_shape=(32, 32),
randomize=[trn],
center=[tst])
'''
#Define experiment
experiment = Train(dataset=trn,
model=mdl,
def main( x ):
l1_dim = x[0]
l2_dim = x[1]
learning_rate = x[2]
momentum = x[3]
l1_dropout = x[4]
decay_factor = x[5]
min_lr = 1e-7
#
train = np.loadtxt( train_file, delimiter = ',' )
x_train = train[:,0:-1]
y_train = train[:,-1]
y_train.shape = ( y_train.shape[0], 1 )
#
validation = np.loadtxt( validation_file, delimiter = ',' )
x_valid = validation[:,0:-1]
y_valid = validation[:,-1]
y_valid.shape = ( y_valid.shape[0], 1 )
#
#input_space = VectorSpace( dim = x.shape[1] )
full = DenseDesignMatrix( X = x_train, y = y_train )
valid = DenseDesignMatrix( X = x_valid, y = y_valid )
l1 = mlp.RectifiedLinear(
layer_name='l1',
irange=.001,
dim = l1_dim,
# "Rather than using weight decay, we constrain the norms of the weight vectors"
max_col_norm=1.
)
l2 = mlp.RectifiedLinear(
layer_name='l2',
irange=.001,
dim = l2_dim,
max_col_norm=1.
)
output = mlp.Linear( dim = 1, layer_name='y', irange=.0001 )
layers = [l1, l2, output]
nvis = x_train.shape[1]
mdl = mlp.MLP( layers, nvis = nvis ) # input_space = input_space
#lr = .001
#epochs = 100
decay = sgd.ExponentialDecay( decay_factor = decay_factor, min_lr = min_lr )
trainer = sgd.SGD(
learning_rate = learning_rate,
batch_size=128,
learning_rule=learning_rule.Momentum( momentum ),
update_callbacks = [ decay ],
# Remember, default dropout is .5
cost = Dropout( input_include_probs = {'l1': l1_dropout},
input_scales={'l1': 1.}),
#termination_criterion = EpochCounter(epochs),
termination_criterion = MonitorBased(
channel_name = "valid_objective",
prop_decrease = 0.001, # 0.1% of objective
N = 10
),
# valid_objective is MSE
monitoring_dataset = { 'train': full, 'valid': valid }
)
watcher = best_params.MonitorBasedSaveBest( channel_name = 'valid_objective', save_path = output_model_file )
experiment = Train( dataset = full, model = mdl, algorithm = trainer, extensions = [ watcher ] )
experiment.main_loop()
###
error = get_error_from_model( output_model_file )
print "*** error: {} ***".format( error )
return error
delimiter=',')
# ------------------------------------------Simple ANN
h0 = mlp.Sigmoid(layer_name="h0", dim=73, sparse_init=0)
y0 = mlp.Softmax(n_classes=5, layer_name="y0", irange=0)
layers = [h0, y0]
nn = mlp.MLP(layers, nvis=train.X.shape[1])
algo = sgd.SGD(learning_rate=0.05,
batch_size=100,
monitoring_dataset=valid,
termination_criterion=EpochCounter(100))
algo.setup(nn, train)
save_best = best_params.MonitorBasedSaveBest(channel_name="objective",
save_path='best_params.pkl')
while True:
algo.train(dataset=train)
nn.monitor.report_epoch()
nn.monitor()
save_best.on_monitor(nn, train, algo)
if not algo.continue_learning(nn):
break
# SoftPlus with Dropout
h0 = mlp.Softplus(layer_name='h0', dim=60, sparse_init=0)
h1 = mlp.Softplus(layer_name='h1', dim=60, sparse_init=0)
y0 = mlp.Softmax(layer_name='y0', n_classes=5, irange=0)
layers = [h0, h1, y0]
mdl = mlp.MLP(layers,
input_space=in_space)
lr = .0001
epochs = 100
trainer = sgd.SGD(learning_rate=lr,
batch_size=128,
learning_rule=learning_rule.Momentum(.5),
# Remember, default dropout is .5
cost=Dropout(input_include_probs={'l1': .8},
input_scales={'l1': 1.}),
termination_criterion=EpochCounter(epochs),
monitoring_dataset={'train': full})
watcher = best_params.MonitorBasedSaveBest(
channel_name='train_y_misclass',
save_path='saved_clf.pkl')
velocity = learning_rule.MomentumAdjustor(final_momentum=.6,
start=1,
saturate=250)
decay = sgd.LinearDecayOverEpoch(start=1,
saturate=250,
decay_factor=lr*.05)
experiment = Train(dataset=full,
model=mdl,
algorithm=trainer,
extensions=[watcher, velocity, decay])
# learning rate
start = 1
saturate = 50
decay_factor = .1
learning_rate_adjustor = sgd.LinearDecayOverEpoch(start, saturate, decay_factor)
# create neural net
ann = mlp.MLP(layers, nvis=ds_train.nr_inputs)
# create Stochastic Gradient Descent trainer
trainer = sgd.SGD(learning_rate=.05, batch_size=10, monitoring_dataset=ds_valid,
termination_criterion=termination_criterion, learning_rule=momentum_rule)
trainer.setup(ann, ds_train)
# add monitor for saving the model with best score
monitor_save_best = best_params.MonitorBasedSaveBest('output_misclass',
'/tmp/best.pkl')
# train neural net until the termination criterion is true
while True:
trainer.train(dataset=ds_train)
ann.monitor.report_epoch()
ann.monitor()
monitor_save_best.on_monitor(ann, ds_valid, trainer)
if not trainer.continue_learning(ann):
break
momentum_adjustor.on_monitor(ann, ds_valid, trainer)
learning_rate_adjustor.on_monitor(ann, ds_valid, trainer)
# load the best model
ann = serial.load('/tmp/best.pkl')