/
cifar10_convolutional.yaml
174 lines (174 loc) · 6.41 KB
/
cifar10_convolutional.yaml
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!obj:pylearn2.train.Train {
dataset: &train !obj:pylearn2.datasets.cifar10.CIFAR10 {
axes: ['c', 0, 1, 'b'],
gcn: 55.,
which_set: 'train',
start: 0,
stop: 40000
},
model: !obj:adversarial.AdversaryPair {
generator: !obj:adversarial.Generator {
mlp: !obj:pylearn2.models.mlp.MLP {
layers: [
!obj:pylearn2.models.mlp.RectifiedLinear {
layer_name: 'gh0',
dim: 8000,
irange: .05,
#max_col_norm: 1.9365,
},
!obj:pylearn2.models.mlp.Sigmoid {
layer_name: 'h1',
dim: 8000,
irange: .05,
#max_col_norm: 1.9365,
},
!obj:pylearn2.models.mlp.SpaceConverter {
layer_name: 'converter',
output_space: !obj:pylearn2.space.Conv2DSpace {
shape: [10, 10],
num_channels: 80,
axes: ['c', 0, 1, 'b'],
}},
!obj:adversarial.deconv.Deconv {
#W_lr_scale: .05,
#b_lr_scale: .05,
num_channels: 3,
output_stride: [3, 3],
kernel_shape: [5, 5],
pad_out: 0,
#max_kernel_norm: 1.9365,
# init_bias: !obj:pylearn2.models.dbm.init_sigmoid_bias_from_marginals { dataset: *train},
layer_name: 'y',
irange: .05,
tied_b: 0
},
],
nvis: 100,
}},
discriminator:
!obj:pylearn2.models.mlp.MLP {
layers: [
!obj:pylearn2.models.maxout.MaxoutConvC01B {
layer_name: 'dh0',
pad: 4,
tied_b: 1,
#W_lr_scale: .05,
#b_lr_scale: .05,
num_channels: 32,
num_pieces: 2,
kernel_shape: [8, 8],
pool_shape: [4, 4],
pool_stride: [2, 2],
irange: .005,
#max_kernel_norm: .9,
partial_sum: 33,
},
!obj:pylearn2.models.maxout.MaxoutConvC01B {
layer_name: 'h1',
pad: 3,
tied_b: 1,
#W_lr_scale: .05,
#b_lr_scale: .05,
num_channels: 32, # 192 ran out of memory
num_pieces: 2,
kernel_shape: [8, 8],
pool_shape: [4, 4],
pool_stride: [2, 2],
irange: .005,
#max_kernel_norm: 1.9365,
partial_sum: 15,
},
!obj:pylearn2.models.maxout.MaxoutConvC01B {
pad: 3,
layer_name: 'h2',
tied_b: 1,
#W_lr_scale: .05,
#b_lr_scale: .05,
num_channels: 192,
num_pieces: 2,
kernel_shape: [5, 5],
pool_shape: [2, 2],
pool_stride: [2, 2],
irange: .005,
#max_kernel_norm: 1.9365,
},
!obj:pylearn2.models.maxout.Maxout {
layer_name: 'h3',
irange: .005,
num_units: 500,
num_pieces: 5,
#max_col_norm: 1.9
},
!obj:pylearn2.models.mlp.Sigmoid {
#W_lr_scale: .1,
#b_lr_scale: .1,
#max_col_norm: 1.9365,
layer_name: 'y',
dim: 1,
irange: .005
}
],
input_space: !obj:pylearn2.space.Conv2DSpace {
shape: [32, 32],
num_channels: 3,
axes: ['c', 0, 1, 'b'],
}
},
},
algorithm: !obj:pylearn2.training_algorithms.sgd.SGD {
batch_size: 128,
learning_rate: .004,
learning_rule: !obj:pylearn2.training_algorithms.learning_rule.Momentum {
init_momentum: .5,
},
monitoring_dataset:
{
#'train' : *train,
'valid' : !obj:pylearn2.datasets.cifar10.CIFAR10 {
axes: ['c', 0, 1, 'b'],
gcn: 55.,
which_set: 'train',
start: 40000,
stop: 50000
},
#'test' : !obj:pylearn2.datasets.cifar10.CIFAR10 {
# which_set: 'test',
# gcn: 55.,
# }
},
cost: !obj:adversarial.AdversaryCost2 {
scale_grads: 0,
#target_scale: .1,
discriminator_default_input_include_prob: .5,
discriminator_input_include_probs: {
'dh0': .8
},
discriminator_default_input_scale: 2.,
discriminator_input_scales: {
'dh0': 1.25
}
},
#termination_criterion: !obj:pylearn2.termination_criteria.MonitorBased {
# channel_name: "valid_y_misclass",
# prop_decrease: 0.,
# N: 100
#},
update_callbacks: !obj:pylearn2.training_algorithms.sgd.ExponentialDecay {
decay_factor: 1.000004,
min_lr: .000001
}
},
extensions: [
#!obj:pylearn2.train_extensions.best_params.MonitorBasedSaveBest {
# channel_name: 'valid_y_misclass',
# save_path: "${PYLEARN2_TRAIN_FILE_FULL_STEM}_best.pkl"
#},
!obj:pylearn2.training_algorithms.learning_rule.MomentumAdjustor {
start: 1,
saturate: 250,
final_momentum: .7
}
],
save_path: "${PYLEARN2_TRAIN_FILE_FULL_STEM}.pkl",
save_freq: 1
}