def build_model(images, labels):
# Construct a bottom convolutional sequence
bottom_conv_sequence = convolutional_sequence((3,3), 16, (160, 160))
bottom_conv_sequence._push_allocation_config()
# Flatten layer
flattener = Flattener()
# Construct a top MLP
conv_out_dim = numpy.prod(bottom_conv_sequence.get_dim('output'))
#top_mlp = MLP([Rectifier(name='non_linear_9'), Softmax(name='non_linear_11')], [conv_out_dim, 1024, 10], weights_init=IsotropicGaussian(), biases_init=Constant(0))
top_mlp = BatchNormalizedMLP([Rectifier(name='non_linear_9'), Softmax(name='non_linear_11')], [conv_out_dim, 1024, 10], weights_init=IsotropicGaussian(), biases_init=Constant(0))
# Construct feedforward sequence
ss_seq = FeedforwardSequence([bottom_conv_sequence.apply, flattener.apply, top_mlp.apply])
ss_seq.push_initialization_config()
ss_seq.initialize()
prediction = ss_seq.apply(images)
cost_noreg = CategoricalCrossEntropy().apply(labels.flatten(), prediction)
# add regularization
selector = Selector([top_mlp])
Ws = selector.get_parameters('W')
mlp_brick_name = 'batchnormalizedmlp'
W0 = Ws['/%s/linear_0.W' % mlp_brick_name]
W1 = Ws['/%s/linear_1.W' % mlp_brick_name]
cost = cost_noreg + .01 * (W0 ** 2).mean() + .01 * (W1 ** 2).mean()
return cost
def build_model(images, labels):
vgg = VGG(layer='conv4_4')
vgg.push_initialization_config()
vgg.initialize()
tdb = top_direction_block()
tdb.push_initialization_config()
tdb.initialize()
# Construct feedforward sequence
ss_seq = FeedforwardSequence([vgg.apply, tdb.apply])
ss_seq.push_initialization_config()
ss_seq.initialize()
prediction = ss_seq.apply(images)
cost = StructuredCost().apply(labels, theano.tensor.clip(prediction, 1e-5, 1 - 1e-5))
cg = ComputationGraph(cost)
cg_dropout = apply_dropout(cg, [VariableFilter(roles=[OUTPUT])(cg.variables)[0]], .5)
cost_dropout = cg_dropout.outputs[0]
# define learned parameters
selector = Selector([ss_seq])
W = selector.get_parameters()
parameters = []
parameters += [v for k, v in W.items()]
return cost_dropout, parameters
def net_dvc(image_size=(32,32)):
convos = [5,5,5]
pools = [2,2,2]
filters = [100,200,300]
tuplify = lambda x: (x,x)
convos = list(map(tuplify, convos))
conv_layers = [Convolutional(filter_size=s,num_filters=o, num_channels=i, name="Conv"+str(n))\
for s,o,i,n in zip(convos, filters, [3] + filters, range(1000))]
pool_layers = [MaxPooling(p) for p in map(tuplify, pools)]
activations = [Rectifier() for i in convos]
layers = [i for l in zip(conv_layers, activations, pool_layers) for i in l]
cnn = ConvolutionalSequence(layers, 3, image_size=image_size, name="cnn",
weights_init=Uniform(width=.1),
biases_init=Constant(0))
cnn._push_allocation_config()
cnn_output = np.prod(cnn.get_dim('output'))
mlp_size = [cnn_output,500,2]
mlp = MLP([Rectifier(), Softmax()], mlp_size, name="mlp",
weights_init=Uniform(width=.1),
biases_init=Constant(0))
seq = FeedforwardSequence([net.apply for net in [cnn,Flattener(),mlp]])
seq.push_initialization_config()
seq.initialize()
return seq
def build_model(images, labels):
# Construct a bottom convolutional sequence
bottom_conv_sequence = convolutional_sequence((3, 3), 64, (150, 150))
bottom_conv_sequence._push_allocation_config()
# Flatten layer
flattener = Flattener()
# Construct a top MLP
conv_out_dim = numpy.prod(bottom_conv_sequence.get_dim('output'))
top_mlp = MLP([
LeakyRectifier(name='non_linear_9'),
LeakyRectifier(name='non_linear_10'),
Softmax(name='non_linear_11')
], [conv_out_dim, 2048, 612, 10],
weights_init=IsotropicGaussian(),
biases_init=Constant(1))
# Construct feedforward sequence
ss_seq = FeedforwardSequence(
[bottom_conv_sequence.apply, flattener.apply, top_mlp.apply])
ss_seq.push_initialization_config()
ss_seq.initialize()
prediction = ss_seq.apply(images)
cost = CategoricalCrossEntropy().apply(labels.flatten(), prediction)
return cost
def net_dvc(image_size=(32, 32)):
convos = [5, 5, 5]
pools = [2, 2, 2]
filters = [100, 200, 300]
tuplify = lambda x: (x, x)
convos = list(map(tuplify, convos))
conv_layers = [Convolutional(filter_size=s,num_filters=o, num_channels=i, name="Conv"+str(n))\
for s,o,i,n in zip(convos, filters, [3] + filters, range(1000))]
pool_layers = [MaxPooling(p) for p in map(tuplify, pools)]
activations = [Rectifier() for i in convos]
layers = [i for l in zip(conv_layers, activations, pool_layers) for i in l]
cnn = ConvolutionalSequence(layers,
3,
image_size=image_size,
name="cnn",
weights_init=Uniform(width=.1),
biases_init=Constant(0))
cnn._push_allocation_config()
cnn_output = np.prod(cnn.get_dim('output'))
mlp_size = [cnn_output, 500, 2]
mlp = MLP([Rectifier(), Softmax()],
mlp_size,
name="mlp",
weights_init=Uniform(width=.1),
biases_init=Constant(0))
seq = FeedforwardSequence([net.apply for net in [cnn, Flattener(), mlp]])
seq.push_initialization_config()
seq.initialize()
return seq
def build_model(images, labels):
vgg = VGG(layer='conv3_4')
vgg.push_initialization_config()
vgg.initialize()
sb = SubstractBatch()
# Construct a bottom convolutional sequence
layers = [
Convolutional(filter_size=(3, 3),
num_filters=100,
use_bias=True,
tied_biases=True,
name='final_conv0'),
BatchNormalization(name='batchnorm_1'),
Rectifier(name='final_conv0_act'),
Convolutional(filter_size=(3, 3),
num_filters=100,
use_bias=True,
tied_biases=True,
name='final_conv1'),
BatchNormalization(name='batchnorm_2'),
Rectifier(name='final_conv1_act'),
MaxPooling(pooling_size=(2, 2), name='maxpool_final')
]
bottom_conv_sequence = ConvolutionalSequence(
layers,
num_channels=256,
image_size=(40, 40),
biases_init=Constant(0.),
weights_init=IsotropicGaussian(0.01))
bottom_conv_sequence._push_allocation_config()
# Flatten layer
flattener = Flattener()
# Construct a top MLP
conv_out_dim = numpy.prod(bottom_conv_sequence.get_dim('output'))
print 'dim output conv:', bottom_conv_sequence.get_dim('output')
# conv_out_dim = 20 * 40 * 40
top_mlp = BatchNormalizedMLP(
[Rectifier(name='non_linear_9'),
Softmax(name='non_linear_11')], [conv_out_dim, 1024, 10],
weights_init=IsotropicGaussian(),
biases_init=Constant(0))
# Construct feedforward sequence
ss_seq = FeedforwardSequence([
vgg.apply, bottom_conv_sequence.apply, flattener.apply, top_mlp.apply
])
ss_seq.push_initialization_config()
ss_seq.initialize()
prediction = ss_seq.apply(images)
cost_noreg = CategoricalCrossEntropy().apply(labels.flatten(), prediction)
# add regularization
selector = Selector([top_mlp])
Ws = selector.get_parameters('W')
mlp_brick_name = 'batchnormalizedmlp'
W0 = Ws['/%s/linear_0.W' % mlp_brick_name]
W1 = Ws['/%s/linear_1.W' % mlp_brick_name]
cost = cost_noreg + .0001 * (W0**2).sum() + .001 * (W1**2).sum()
# define learned parameters
selector = Selector([ss_seq])
Ws = selector.get_parameters('W')
bs = selector.get_parameters('b')
BNSCs = selector.get_parameters('batch_norm_scale')
BNSHs = selector.get_parameters('batch_norm_shift')
parameters_top = []
parameters_top += [v for k, v in Ws.items()]
parameters_top += [v for k, v in bs.items()]
parameters_top += [v for k, v in BNSCs.items()]
parameters_top += [v for k, v in BNSHs.items()]
selector = Selector([vgg])
convs = selector.get_parameters()
parameters_all = []
parameters_all += parameters_top
parameters_all += [v for k, v in convs.items()]
return cost, [parameters_top, parameters_all]
