def test_batch_normalized_mlp_learn_scale_propagated_at_alloc():
"""Test that setting learn_scale on a BatchNormalizedMLP works."""
mlp = BatchNormalizedMLP([Tanh(), Tanh()], [5, 7, 9], learn_scale=False)
assert not mlp.learn_scale
assert all(act.children[0].learn_scale for act in mlp.activations)
mlp.allocate()
assert not any(act.children[0].learn_scale for act in mlp.activations)
def test_batch_normalized_mlp_transformed():
"""Smoke test that a graph involving a BatchNormalizedMLP transforms."""
x = tensor.matrix('x')
mlp = BatchNormalizedMLP([Tanh(), Tanh()], [5, 7, 9])
with batch_normalization(mlp):
y = mlp.apply(x)
assert len(get_batch_normalization_updates(ComputationGraph([y]))) == 4
def test_batch_normalized_mlp_allocation():
"""Test that BatchNormalizedMLP performs allocation correctly."""
mlp = BatchNormalizedMLP([Tanh(), Tanh()], [5, 7, 9])
mlp.allocate()
assert mlp.activations[0].children[0].input_dim == 7
assert mlp.activations[1].children[0].input_dim == 9
assert not any(l.use_bias for l in mlp.linear_transformations)
def test_batch_normalized_mlp_mean_only_propagated_at_alloc():
"""Test that setting mean_only on a BatchNormalizedMLP works."""
mlp = BatchNormalizedMLP([Tanh(), Tanh()], [5, 7, 9], mean_only=True)
assert mlp.mean_only
assert not any(act.children[0].mean_only for act in mlp.activations)
mlp.allocate()
assert all(act.children[0].mean_only for act in mlp.activations)
def test_get_batch_normalization_updates_mean_only(self):
"""Test get_batch_normalization_updates with mean_only bricks."""
mlp = BatchNormalizedMLP([Tanh(), Tanh()], [5, 7, 9], mean_only=True)
with batch_normalization(mlp):
y_bn = mlp.apply(self.x)
graph = ComputationGraph([y_bn])
updates = get_batch_normalization_updates(graph)
self.simple_assertions(updates, num_updates=2, mean_only=True)
def test_batch_normalized_mlp_conserve_memory_propagated():
"""Test that setting conserve_memory on a BatchNormalizedMLP works."""
mlp = BatchNormalizedMLP([Tanh(), Tanh()], [5, 7, 9],
conserve_memory=False)
assert not any(act.children[0].conserve_memory for act in mlp.activations)
mlp.conserve_memory = True
assert mlp.conserve_memory
assert all(act.children[0].conserve_memory for act in mlp.activations)
def test_get_batch_normalization_updates_mean_only(self):
"""Test get_batch_normalization_updates with mean_only bricks."""
mlp = BatchNormalizedMLP([Tanh(), Tanh()], [5, 7, 9], mean_only=True)
with batch_normalization(mlp):
y_bn = mlp.apply(self.x)
graph = ComputationGraph([y_bn])
updates = get_batch_normalization_updates(graph)
self.simple_assertions(updates, num_updates=2, mean_only=True)
def test_batch_normalized_mlp_learn_scale_propagated_at_alloc():
"""Test that setting learn_scale on a BatchNormalizedMLP works."""
mlp = BatchNormalizedMLP([Tanh(), Tanh()], [5, 7, 9],
learn_scale=False)
assert not mlp.learn_scale
assert all(act.children[0].learn_scale for act in mlp.activations)
mlp.allocate()
assert not any(act.children[0].learn_scale for act in mlp.activations)
def test_batch_normalized_mlp_mean_only_propagated_at_alloc():
"""Test that setting mean_only on a BatchNormalizedMLP works."""
mlp = BatchNormalizedMLP([Tanh(), Tanh()], [5, 7, 9],
mean_only=True)
assert mlp.mean_only
assert not any(act.children[0].mean_only for act in mlp.activations)
mlp.allocate()
assert all(act.children[0].mean_only for act in mlp.activations)
class TestSimpleGetBatchNormalizationUpdates(object):
def setUp(self):
self.mlp = BatchNormalizedMLP([Tanh(), Tanh()], [5, 7, 9])
self.x = tensor.matrix()
def simple_assertions(self, updates, num_bricks=2, num_updates=4):
"""Shared assertions for simple tests."""
assert len(updates) == num_updates
assert all(is_shared_variable(u[0]) for u in updates)
# This order is somewhat arbitrary and implementation_dependent
means = set(u[0] for u in updates
if has_roles(u[0], [BATCH_NORM_POPULATION_MEAN]))
stdevs = set(u[0] for u in updates
if has_roles(u[0], [BATCH_NORM_POPULATION_STDEV]))
assert means.isdisjoint(stdevs)
assert len(set(get_brick(v) for v in means)) == num_bricks
assert len(set(get_brick(v) for v in stdevs)) == num_bricks
def test_get_batch_normalization_updates(self):
"""Test that get_batch_normalization_updates works as expected."""
with batch_normalization(self.mlp):
y_bn = self.mlp.apply(self.x)
graph = ComputationGraph([y_bn])
updates = get_batch_normalization_updates(graph)
self.simple_assertions(updates)
def test_get_batch_normalization_updates_non_training_applications(self):
"""Test updates extracton in graph with non-training apply."""
y = self.mlp.apply(self.x)
with batch_normalization(self.mlp):
y_bn = self.mlp.apply(self.x)
graph = ComputationGraph([y_bn, y])
updates = get_batch_normalization_updates(graph)
self.simple_assertions(updates)
def test_get_batch_normalization_updates_no_training(self):
"""Test for exception if there are no training-mode nodes."""
y = self.mlp.apply(self.x)
graph = ComputationGraph([y])
numpy.testing.assert_raises(ValueError,
get_batch_normalization_updates, graph)
def test_get_batch_normalization_updates_duplicates_error(self):
"""Test that we get an error by default on multiple apply."""
with batch_normalization(self.mlp):
y = self.mlp.apply(self.x)
y2 = self.mlp.apply(self.x)
graph = ComputationGraph([y, y2])
numpy.testing.assert_raises(ValueError,
get_batch_normalization_updates, graph)
def test_get_batch_normalization_updates_allow_duplicates(self):
"""Test get_batch_normalization_updates(allow_duplicates=True)."""
with batch_normalization(self.mlp):
y = self.mlp.apply(self.x)
y2 = self.mlp.apply(self.x)
graph = ComputationGraph([y, y2])
updates = get_batch_normalization_updates(graph, allow_duplicates=True)
self.simple_assertions(updates, num_bricks=2, num_updates=8)
class TestSimpleGetBatchNormalizationUpdates(object):
def setUp(self):
self.mlp = BatchNormalizedMLP([Tanh(), Tanh()], [5, 7, 9])
self.x = tensor.matrix()
def simple_assertions(self, updates, num_bricks=2, num_updates=4):
"""Shared assertions for simple tests."""
assert len(updates) == num_updates
assert all(is_shared_variable(u[0]) for u in updates)
# This order is somewhat arbitrary and implementation_dependent
means = set(u[0] for u in updates
if has_roles(u[0], [BATCH_NORM_POPULATION_MEAN]))
stdevs = set(u[0] for u in updates
if has_roles(u[0], [BATCH_NORM_POPULATION_STDEV]))
assert means.isdisjoint(stdevs)
assert len(set(get_brick(v) for v in means)) == num_bricks
assert len(set(get_brick(v) for v in stdevs)) == num_bricks
def test_get_batch_normalization_updates(self):
"""Test that get_batch_normalization_updates works as expected."""
with batch_normalization(self.mlp):
y_bn = self.mlp.apply(self.x)
graph = ComputationGraph([y_bn])
updates = get_batch_normalization_updates(graph)
self.simple_assertions(updates)
def test_get_batch_normalization_updates_non_training_applications(self):
"""Test updates extracton in graph with non-training apply."""
y = self.mlp.apply(self.x)
with batch_normalization(self.mlp):
y_bn = self.mlp.apply(self.x)
graph = ComputationGraph([y_bn, y])
updates = get_batch_normalization_updates(graph)
self.simple_assertions(updates)
def test_get_batch_normalization_updates_no_training(self):
"""Test for exception if there are no training-mode nodes."""
y = self.mlp.apply(self.x)
graph = ComputationGraph([y])
numpy.testing.assert_raises(ValueError,
get_batch_normalization_updates, graph)
def test_get_batch_normalization_updates_duplicates_error(self):
"""Test that we get an error by default on multiple apply."""
with batch_normalization(self.mlp):
y = self.mlp.apply(self.x)
y2 = self.mlp.apply(self.x)
graph = ComputationGraph([y, y2])
numpy.testing.assert_raises(ValueError,
get_batch_normalization_updates, graph)
def test_get_batch_normalization_updates_allow_duplicates(self):
"""Test get_batch_normalization_updates(allow_duplicates=True)."""
with batch_normalization(self.mlp):
y = self.mlp.apply(self.x)
y2 = self.mlp.apply(self.x)
graph = ComputationGraph([y, y2])
updates = get_batch_normalization_updates(graph, allow_duplicates=True)
self.simple_assertions(updates, num_bricks=2, num_updates=8)
def test_batch_normalized_mlp_construction():
"""Test that BatchNormalizedMLP performs construction correctly."""
mlp = BatchNormalizedMLP([Tanh(), Tanh()], [5, 7, 9])
assert all(isinstance(a, Sequence) for a in mlp.activations)
assert all(
isinstance(a.children[0], BatchNormalization) for a in mlp.activations)
assert all(isinstance(a.children[1], Tanh) for a in mlp.activations)
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 __init__(self, image_dimension, **kwargs):
layers = []
#############################################
# a first block with 2 convolutions of 32 (3, 3) filters
layers.append(Convolutional((3, 3), 32, border_mode='half'))
layers.append(Rectifier())
layers.append(Convolutional((3, 3), 32, border_mode='half'))
layers.append(Rectifier())
# maxpool with size=(2, 2)
layers.append(MaxPooling((2, 2)))
#############################################
# a 2nd block with 3 convolutions of 64 (3, 3) filters
layers.append(Convolutional((3, 3), 64, border_mode='half'))
layers.append(Rectifier())
layers.append(Convolutional((3, 3), 64, border_mode='half'))
layers.append(Rectifier())
layers.append(Convolutional((3, 3), 64, border_mode='half'))
layers.append(Rectifier())
# maxpool with size=(2, 2)
layers.append(MaxPooling((2, 2)))
#############################################
# a 3rd block with 4 convolutions of 128 (3, 3) filters
layers.append(Convolutional((3, 3), 128, border_mode='half'))
layers.append(Rectifier())
layers.append(Convolutional((3, 3), 128, border_mode='half'))
layers.append(Rectifier())
layers.append(Convolutional((3, 3), 128, border_mode='half'))
layers.append(Rectifier())
layers.append(Convolutional((3, 3), 128, border_mode='half'))
layers.append(Rectifier())
# maxpool with size=(2, 2)
layers.append(MaxPooling((2, 2)))
self.conv_sequence = ConvolutionalSequence(layers, 3, image_size=image_dimension)
flattener = Flattener()
self.top_mlp = BatchNormalizedMLP(activations=[Rectifier(), Logistic()], dims=[500, 1])
application_methods = [self.conv_sequence.apply, flattener.apply, self.top_mlp.apply]
super(VGGNet, self).__init__(application_methods, biases_init=Constant(0), weights_init=Uniform(width=.1), **kwargs)
def setUp(self):
self.mlp = BatchNormalizedMLP([Tanh(), Tanh()], [5, 7, 9])
self.x = tensor.matrix()
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]
def __init__(
self,
dim,
emb_dim,
vocab,
def_emb_translate_dim=-1,
def_dim=-1,
encoder='bilstm',
bn=True,
def_reader=None,
def_combiner=None,
dropout=0.5,
num_input_words=-1,
# Others
**kwargs):
self._dropout = dropout
self._vocab = vocab
self._emb_dim = emb_dim
self._def_reader = def_reader
self._def_combiner = def_combiner
if encoder != 'bilstm':
raise NotImplementedError()
if def_emb_translate_dim < 0:
self.def_emb_translate_dim = emb_dim
else:
self.def_emb_translate_dim = def_emb_translate_dim
if def_dim < 0:
self._def_dim = emb_dim
else:
self._def_dim = def_dim
if num_input_words > 0:
logger.info("Restricting vocab to " + str(num_input_words))
self._num_input_words = num_input_words
else:
self._num_input_words = vocab.size()
children = []
if self.def_emb_translate_dim != self._emb_dim:
self._translate_pre_def = Linear(input_dim=emb_dim,
output_dim=def_emb_translate_dim)
children.append(self._translate_pre_def)
else:
self._translate_pre_def = None
## Embedding
self._lookup = LookupTable(self._num_input_words,
emb_dim,
weights_init=GlorotUniform())
children.append(self._lookup)
if def_reader:
self._final_emb_dim = self._def_dim
self._def_reader = def_reader
self._def_combiner = def_combiner
children.extend([self._def_reader, self._def_combiner])
else:
self._final_emb_dim = self._emb_dim
## BiLSTM
self._hyp_bidir_fork = Linear(
self._def_dim if def_reader else self._emb_dim,
4 * dim,
name='hyp_bidir_fork')
self._hyp_bidir = Bidirectional(LSTM(dim), name='hyp_bidir')
self._prem_bidir_fork = Linear(
self._def_dim if def_reader else self._emb_dim,
4 * dim,
name='prem_bidir_fork')
self._prem_bidir = Bidirectional(LSTM(dim), name='prem_bidir')
children.extend([self._hyp_bidir_fork, self._hyp_bidir])
children.extend([self._prem_bidir, self._prem_bidir_fork])
## BiLSTM no. 2 (encoded attentioned embeddings)
self._hyp_bidir_fork2 = Linear(8 * dim,
4 * dim,
name='hyp_bidir_fork2')
self._hyp_bidir2 = Bidirectional(LSTM(dim), name='hyp_bidir2')
self._prem_bidir_fork2 = Linear(8 * dim,
4 * dim,
name='prem_bidir_fork2')
self._prem_bidir2 = Bidirectional(LSTM(dim), name='prem_bidir2')
children.extend([self._hyp_bidir_fork2, self._hyp_bidir2])
children.extend([self._prem_bidir2, self._prem_bidir_fork2])
self._rnns = [
self._prem_bidir2, self._hyp_bidir2, self._prem_bidir,
self._hyp_bidir
]
## MLP
if bn:
self._mlp = BatchNormalizedMLP([Tanh()], [8 * dim, dim],
conserve_memory=False,
name="mlp")
self._pred = BatchNormalizedMLP([Softmax()], [dim, 3],
conserve_memory=False,
name="pred_mlp")
else:
self._mlp = MLP([Tanh()], [8 * dim, dim], name="mlp")
self._pred = MLP([Softmax()], [dim, 3], name="pred_mlp")
children.append(self._mlp)
children.append(self._pred)
## Softmax
self._ndim_softmax = NDimensionalSoftmax()
children.append(self._ndim_softmax)
super(ESIM, self).__init__(children=children, **kwargs)
def setUp(self):
self.mlp = BatchNormalizedMLP([Tanh(), Tanh()], [5, 7, 9])
self.x = tensor.matrix()
def training(runname, rnnType, maxPackets, packetTimeSteps, packetReverse, padOldTimeSteps, wtstd,
lr, decay, clippings, dimIn, dim, attentionEnc, attentionContext, numClasses, batch_size, epochs,
trainPercent, dataPath, loadPrepedData, channel): # pragma: no cover
print locals()
print
X = T.tensor4('inputs')
Y = T.matrix('targets')
linewt_init = IsotropicGaussian(wtstd)
line_bias = Constant(1.0)
rnnwt_init = IsotropicGaussian(wtstd)
rnnbias_init = Constant(0.0)
classifierWts = IsotropicGaussian(wtstd)
learning_rateClass = theano.shared(np.array(lr, dtype=theano.config.floatX))
learning_decay = np.array(decay, dtype=theano.config.floatX)
###DATA PREP
print 'loading data'
if loadPrepedData:
hexSessions = loadFile(dataPath)
else:
sessioner = sessionizer.HexSessionizer(dataPath)
hexSessions = sessioner.read_pcap()
hexSessions = removeBadSessionizer(hexSessions)
numSessions = len(hexSessions)
print str(numSessions) + ' sessions found'
hexSessionsKeys = order_keys(hexSessions)
hexDict = hexTokenizer()
print 'creating dictionary of ip communications'
comsDict, uniqIPs = srcIpDict(hexSessions)
comsDict = dictUniquerizer(comsDict)
print 'initializing network graph'
###ENCODER
if rnnType == 'gru':
rnn = GatedRecurrent(dim=dim, weights_init = rnnwt_init, biases_init = rnnbias_init, name = 'gru')
dimMultiplier = 2
else:
rnn = LSTM(dim=dim, weights_init = rnnwt_init, biases_init = rnnbias_init, name = 'lstm')
dimMultiplier = 4
fork = Fork(output_names=['linear', 'gates'],
name='fork', input_dim=dimIn, output_dims=[dim, dim * dimMultiplier],
weights_init = linewt_init, biases_init = line_bias)
###CONTEXT
if rnnType == 'gru':
rnnContext = GatedRecurrent(dim=dim, weights_init = rnnwt_init,
biases_init = rnnbias_init, name = 'gruContext')
else:
rnnContext = LSTM(dim=dim, weights_init = rnnwt_init, biases_init = rnnbias_init,
name = 'lstmContext')
forkContext = Fork(output_names=['linearContext', 'gatesContext'],
name='forkContext', input_dim=dim, output_dims=[dim, dim * dimMultiplier],
weights_init = linewt_init, biases_init = line_bias)
forkDec = Fork(output_names=['linear', 'gates'],
name='forkDec', input_dim=dim, output_dims=[dim, dim*dimMultiplier],
weights_init = linewt_init, biases_init = line_bias)
#CLASSIFIER
bmlp = BatchNormalizedMLP( activations=[Tanh(),Tanh()],
dims=[dim, dim, numClasses],
weights_init=classifierWts,
biases_init=Constant(0.0001) )
#initialize the weights in all the functions
fork.initialize()
rnn.initialize()
forkContext.initialize()
rnnContext.initialize()
forkDec.initialize()
bmlp.initialize()
def onestepEnc(X):
data1, data2 = fork.apply(X)
if rnnType == 'gru':
hEnc = rnn.apply(data1, data2)
else:
hEnc, _ = rnn.apply(data2)
return hEnc
hEnc, _ = theano.scan(onestepEnc, X) #(mini*numPackets, packetLen, 1, hexdictLen)
if attentionEnc:
attentionmlpEnc = MLP(activations=[Tanh()], dims = [dim, 1], weights_init=attnWts,
biases_init=Constant(1.0))
attentionmlpEnc.initialize()
hEncAttn = T.reshape(hEnc, (-1, packetTimeSteps, dim))
def onestepEncAttn(hEncAttn):
preEncattn = attentionmlpEnc.apply(hEncAttn)
attEncsoft = Softmax()
attEncpyx = attEncsoft.apply(preEncattn.flatten())
attEncpred = attEncpyx.flatten()
attenc = T.mul(hEncAttn.dimshuffle(1,0), attEncpred).dimshuffle(1,0)
return attenc
attenc, _ = theano.scan(onestepEncAttn, hEncAttn)
hEncReshape = T.reshape(T.sum(attenc, axis = 1), (-1, maxPackets, 1, dim))
else:
hEncReshape = T.reshape(hEnc[:,-1], (-1, maxPackets, 1, dim)) #[:,-1] takes the last rep for each packet
#(mini, numPackets, 1, dimReduced) #[:,-1] takes the last rep for each packet
#(mini, numPackets, 1, dimReduced)
def onestepContext(hEncReshape):
data3, data4 = forkContext.apply(hEncReshape)
if rnnType == 'gru':
hContext = rnnContext.apply(data3, data4)
else:
hContext, _ = rnnContext.apply(data4)
return hContext
hContext, _ = theano.scan(onestepContext, hEncReshape)
if attentionContext:
attentionmlpContext = MLP(activations=[Tanh()], dims = [dim, 1], weights_init=attnWts,
biases_init=Constant(1.0))
attentionmlpContext.initialize()
hContextAttn = T.reshape(hContext, (-1,maxPackets,dim))
def onestepContextAttn(hContextAttn):
preContextatt = attentionmlpContext.apply(hContextAttn)
attContextsoft = Softmax()
attContextpyx = attContextsoft.apply(preContextatt.flatten())
attContextpred = attContextpyx.flatten()
attcontext = T.mul(hContextAttn.dimshuffle(1,0), attContextpred).dimshuffle(1,0)
return attcontext
attcontext, _ = theano.scan(onestepContextAttn, hContextAttn)
hContextReshape = T.sum(attcontext, axis = 1)
else:
hContextReshape = T.reshape(hContext[:,-1], (-1,dim))
data5, _ = forkDec.apply(hContextReshape)
pyx = bmlp.apply(data5)
softmax = Softmax()
softoutClass = softmax.apply(pyx)
costClass = T.mean(CategoricalCrossEntropy().apply(Y, softoutClass))
#CREATE GRAPH
cgClass = ComputationGraph([costClass])
paramsClass = VariableFilter(roles = [PARAMETER])(cgClass.variables)
learning = learningfunctions.Learning(costClass,paramsClass,learning_rateClass,l1=0.,l2=0.,maxnorm=0.,c=clippings)
updatesClass = learning.Adam()
module_logger.info('starting graph compilation')
classifierTrain = theano.function([X,Y], [costClass, hEnc, hContext, pyx, softoutClass],
updates=updatesClass, allow_input_downcast=True)
classifierPredict = theano.function([X], softoutClass, allow_input_downcast=True)
module_logger.info('graph compilation finished')
print 'finished graph compilation'
trainIndex = int(len(hexSessionsKeys)*trainPercent)
epochCost = []
gradNorms = []
trainAcc = []
testAcc = []
costCollect = []
trainCollect = []
module_logger.info('beginning training')
iteration = 0
#epoch
for epoch in xrange(epochs):
#iteration/minibatch
for start, end in zip(range(0, trainIndex,batch_size),
range(batch_size, trainIndex, batch_size)):
trainingTargets = []
trainingSessions = []
#create one minibatch with 0.5 normal and 0.5 abby normal traffic
for trainKey in range(start, end):
sessionForEncoding = list(hexSessions[hexSessions.keys()[trainKey]][0])
adfun = adversarialfunctions.Adversary(sessionForEncoding)
adversaryList = [sessionForEncoding,
adfun.dstIpSwapOut(comsDict, uniqIPs),
adfun.portDirSwitcher(),
adfun.ipDirSwitcher()]
abbyIndex = random.sample(range(len(adversaryList)), 1)[0]
targetClasses = [0]*numClasses
targetClasses[abbyIndex] = 1
abbyTarget = np.array(targetClasses, dtype=theano.config.floatX)
trainingSessions.append(abbyOneHotSes[0])
trainingTargets.append(abbyTarget)
sessionsMinibatch = np.asarray(trainingSessions).reshape((-1, packetTimeSteps, 1, dimIn))
targetsMinibatch = np.asarray(trainingTargets)
costfun = classifierTrain(sessionsMinibatch, targetsMinibatch)
if iteration % (numSessions / (10 * batch_size)) == 0:
costCollect.append(costfun[0])
trainCollect.append(np.mean(np.argmax(costfun[-1],axis=1) == np.argmax(targetsMinibatch, axis=1)))
module_logger.info(' Iteration: ', iteration)
module_logger.info(' Cost: ', np.mean(costCollect))
module_logger.info(' TRAIN accuracy: ', np.mean(trainCollect))
print ' Iteration: ', iteration
print ' Cost: ', np.mean(costCollect)
print ' TRAIN accuracy: ', np.mean(trainCollect)
iteration+=1
#testing accuracy
if iteration % (numSessions / (2 * batch_size)) == 0:
predtar, acttar, testCollect = predictClass(classifierPredict, hexSessions, comsDict, uniqIPs, hexDict,
hexSessionsKeys,
numClasses, trainPercent, dimIn, maxPackets, packetTimeSteps,
padOldTimeSteps)
binaryPrecisionRecall(predtar, acttar, numClasses)
module_logger.info(str(testCollect))
#save the models
if iteration % (numSessions / (5 * batch_size)) == 0:
save_model(classifierPredict)
epochCost.append(np.mean(costCollect))
trainAcc.append(np.mean(trainCollect))
module_logger.info('Epoch: ', epoch)
module_logger.info('Epoch cost average: ', epochCost[-1])
module_logger.info('Epoch TRAIN accuracy: ', trainAcc[-1])
print 'Epoch: ', epoch
print 'Epoch cost average: ', epochCost[-1]
print 'Epoch TRAIN accuracy: ', trainAcc[-1]
return classifierTrain, classifierPredict