Inception([(64, ), (96, 128), (16, 32), (32, )]),
Inception([(128, ), (128, 192), (32, 96), (64, )]),
Pooling(pool_shape=(3, 3), padding=1, strides=2, pool_type='max'),
Inception([(192, ), (96, 208), (16, 48), (64, )]),
Inception([(160, ), (112, 224), (24, 64), (64, )]),
Inception([(128, ), (128, 256), (24, 64), (64, )]),
Inception([(112, ), (144, 288), (32, 64), (64, )]),
Inception([(256, ), (160, 320), (32, 128), (128, )]),
Pooling(pool_shape=(3, 3), padding=1, strides=2, pool_type='max'),
Inception([(256, ), (160, 320), (32, 128), (128, )]),
Inception([(384, ), (192, 384), (48, 128), (128, )]),
Pooling(pool_shape=(7, 7), strides=1, pool_type="avg"),
Affine(axes=ax.Y,
weight_init=XavierInit(),
bias_init=bias_init,
activation=Softmax())
])
lr_schedule = {
'name': 'schedule',
'base_lr': 0.01,
'gamma': (1 / 250.)**(1 / 3.),
'schedule': [22, 44, 65]
}
optimizer = GradientDescentMomentum(lr_schedule,
0.0,
wdecay=0.0005,
iteration=inputs['iteration'])
train_prob = seq1(inputs['image'])
train_loss = ng.cross_entropy_multi(train_prob,
out_axis = ng.make_axis(length=len(shakes.vocab) + 1, name="out_feature_axis")
in_axes = ng.make_axes([batch_axis, time_axis])
out_axes = ng.make_axes([batch_axis, time_axis])
# Build placeholders for the created axes
inputs = {'X': ng.placeholder(in_axes), 'y': ng.placeholder(out_axes),
'iteration': ng.placeholder(axes=())}
# Network Definition
if(use_embedding is False):
seq1 = Sequential([Preprocess(functor=expand_onehot),
LSTM(nout=recurrent_units, init=init_uni, backward=False, reset_cells=True,
activation=Logistic(), gate_activation=Tanh(),
return_sequence=True),
Affine(weight_init=init_uni, bias_init=init_uni,
activation=Softmax(), axes=out_axis)])
else:
embedding_dim = 8
seq1 = Sequential([LookupTable(len(shakes.vocab) + 1, embedding_dim, init_uni, update=True),
LSTM(nout=recurrent_units, init=init_uni, backward=False, reset_cells=True,
activation=Logistic(), gate_activation=Tanh(),
return_sequence=True),
Affine(weight_init=init_uni, bias_init=init_uni,
activation=Softmax(), axes=out_axis)])
# Optimizer
# Initial learning rate is 0.01 (base_lr)
# At iteration (num_iterations // 75), lr is multiplied by gamma (new lr = .95 * .01)
# At iteration (num_iterations * 2 // 75), it is reduced by gamma again
# So on..
no_steps = 75
ax.Y.length = 10
######################
# Model specification
def cifar_mean_subtract(x):
bgr_mean = ng.persistent_tensor(
axes=x.axes.find_by_name('C'),
initial_value=np.array([104., 119., 127.]))
return (x - bgr_mean) / 255.
seq1 = Sequential([Preprocess(functor=cifar_mean_subtract),
Affine(nout=200, weight_init=UniformInit(-0.1, 0.1), activation=Rectlin()),
Affine(axes=ax.Y, weight_init=UniformInit(-0.1, 0.1), activation=Softmax())])
optimizer = GradientDescentMomentum(0.1, 0.9)
train_prob = seq1(inputs['image'])
train_loss = ng.cross_entropy_multi(train_prob, ng.one_hot(inputs['label'], axis=ax.Y))
batch_cost = ng.sequential([optimizer(train_loss), ng.mean(train_loss, out_axes=())])
train_outputs = dict(batch_cost=batch_cost)
with Layer.inference_mode_on():
inference_prob = seq1(inputs['image'])
eval_loss = ng.cross_entropy_multi(inference_prob, ng.one_hot(inputs['label'], axis=ax.Y))
eval_outputs = dict(results=inference_prob, cross_ent_loss=eval_loss)
# Now bind the computations we are interested in
with closing(ngt.make_transformer()) as transformer:
train_computation = make_bound_computation(transformer, train_outputs, inputs)
else:
layer_0 = Preprocess(functor=lambda x: ng.one_hot(x, axis=ax.Y))
if args.layer_type == "rnn":
rlayer = Recurrent(hidden_size, init, activation=Tanh())
elif args.layer_type == "birnn":
rlayer = BiRNN(hidden_size,
init,
activation=Tanh(),
return_sequence=True,
sum_out=True)
# model initialization
seq1 = Sequential([
layer_0, rlayer,
Affine(init, activation=Softmax(), bias_init=init, axes=(ax.Y, ))
])
optimizer = RMSProp()
train_prob = seq1(inputs['inp_txt'])
train_loss = ng.cross_entropy_multi(train_prob,
ng.one_hot(inputs['tgt_txt'], axis=ax.Y),
usebits=True)
batch_cost = ng.sequential(
[optimizer(train_loss),
ng.mean(train_loss, out_axes=())])
train_outputs = dict(batch_cost=batch_cost)
with Layer.inference_mode_on():
inference_prob = seq1(inputs['inp_txt'])
return (x - bgr_mean) / 255.
init_uni = UniformInit(-0.1, 0.1)
seq1 = Sequential([Preprocess(functor=cifar_mean_subtract),
Convolution((5, 5, 16), filter_init=init_uni, activation=Rectlin(),
batch_norm=args.use_batch_norm),
Pooling((2, 2), strides=2),
Convolution((5, 5, 32), filter_init=init_uni, activation=Rectlin(),
batch_norm=args.use_batch_norm),
Pooling((2, 2), strides=2),
Affine(nout=500, weight_init=init_uni, activation=Rectlin(),
batch_norm=args.use_batch_norm),
Affine(axes=ax.Y, weight_init=init_uni, activation=Softmax())])
optimizer = GradientDescentMomentum(0.01, 0.9)
train_prob = seq1(inputs['image'])
train_loss = ng.cross_entropy_multi(train_prob, ng.one_hot(inputs['label'], axis=ax.Y))
batch_cost = ng.sequential([optimizer(train_loss), ng.mean(train_loss, out_axes=())])
train_outputs = dict(batch_cost=batch_cost)
with Layer.inference_mode_on():
inference_prob = seq1(inputs['image'])
# errors = ng.not_equal(ng.argmax(inference_prob, out_axes=[ax.N]), inputs['label'])
eval_loss = ng.cross_entropy_multi(inference_prob, ng.one_hot(inputs['label'], axis=ax.Y))
eval_outputs = dict(results=inference_prob, cross_ent_loss=eval_loss)
# Now bind the computations we are interested in
def __init__(self, net_type, resnet_size, bottleneck, num_resnet_mods, batch_norm=True):
# For CIFAR10 dataset
if (net_type in ('cifar10', 'cifar100')):
# Number of Filters
num_fils = [16, 32, 64]
# Network Layers
layers = [
# Subtracting mean as suggested in paper
Preprocess(functor=cifar10_mean_subtract),
# First Conv with 3x3 and stride=1
Convolution(**conv_params(3, 16, batch_norm=batch_norm))]
first_resmod = True # Indicates the first residual module
# Loop 3 times for each filter.
for fil in range(3):
# Lay out n residual modules so that we have 2n layers.
for resmods in range(num_resnet_mods):
if(resmods == 0):
if(first_resmod):
# Strides=1 and Convolution side path
main_path, side_path = self.get_mp_sp(num_fils[fil],
net_type, direct=False,
batch_norm=batch_norm)
layers.append(ResidualModule(main_path, side_path))
layers.append(Activation(Rectlin()))
first_resmod = False
else:
# Strides=2 and Convolution side path
main_path, side_path = self.get_mp_sp(num_fils[fil], net_type,
direct=False, strides=2,
batch_norm=batch_norm)
layers.append(ResidualModule(main_path, side_path))
layers.append(Activation(Rectlin()))
else:
# Strides=1 and direct connection
main_path, side_path = self.get_mp_sp(num_fils[fil], net_type,
batch_norm=batch_norm)
layers.append(ResidualModule(main_path, side_path))
layers.append(Activation(Rectlin()))
# Do average pooling --> fully connected--> softmax.
layers.append(Pooling((8, 8), pool_type='avg'))
layers.append(Affine(axes=ax.Y, weight_init=KaimingInit(), batch_norm=batch_norm))
layers.append(Activation(Softmax()))
# For I1K dataset
elif (net_type in ('i1k', 'i1k100')):
# Number of Filters
num_fils = [64, 128, 256, 512]
# Number of residual modules we need to instantiate at each level
num_resnet_mods = num_i1k_resmods(resnet_size)
# Network layers
layers = [
# Subtracting mean
Preprocess(functor=i1k_mean_subtract),
# First Conv layer
Convolution((7, 7, 64), strides=2, padding=3,
batch_norm=batch_norm, activation=Rectlin(),
filter_init=KaimingInit()),
# Max Pooling
Pooling((3, 3), strides=2, pool_type='max', padding=1)]
first_resmod = True # Indicates the first residual module for which strides are 1
# Loop 4 times for each filter
for fil in range(4):
# Lay out residual modules as in num_resnet_mods list
for resmods in range(num_resnet_mods[fil]):
if(resmods == 0):
if(first_resmod):
# Strides=1 and Convolution Side path
main_path, side_path = self.get_mp_sp(num_fils[fil],
net_type,
direct=False,
bottleneck=bottleneck,
batch_norm=batch_norm)
layers.append(ResidualModule(main_path, side_path))
layers.append(Activation(Rectlin()))
first_resmod = False
else:
# Strides=2 and Convolution side path
main_path, side_path = self.get_mp_sp(num_fils[fil],
net_type,
direct=False,
bottleneck=bottleneck,
strides=2,
batch_norm=batch_norm)
layers.append(ResidualModule(main_path, side_path))
layers.append(Activation(Rectlin()))
else:
# Strides=1 and direct connection
main_path, side_path = self.get_mp_sp(num_fils[fil],
net_type,
bottleneck=bottleneck,
batch_norm=batch_norm)
layers.append(ResidualModule(main_path, side_path))
layers.append(Activation(Rectlin()))
# Do average pooling --> fully connected--> softmax.
layers.append(Pooling((7, 7), pool_type='avg'))
layers.append(Affine(axes=ax.Y, weight_init=KaimingInit(),
batch_norm=batch_norm))
layers.append(Activation(Softmax()))
else:
raise NameError("Incorrect dataset. Should be --dataset cifar10 or --dataset i1k")
super(BuildResnet, self).__init__(layers=layers)