def BuildModel(modelSpecs, forTrain=True):
rng = np.random.RandomState()
## x is for sequential features and y for matrix (or pairwise) features
x = T.tensor3('x')
y = T.tensor4('y')
## mask for x and y, respectively
xmask = T.bmatrix('xmask')
ymask = T.btensor3('ymask')
xem = None
##if any( k in modelSpecs['seq2matrixMode'] for k in ('SeqOnly', 'Seq+SS') ):
if config.EmbeddingUsed(modelSpecs):
xem = T.tensor3('xem')
distancePredictor = ResNet4DistMatrix( rng, seqInput=x,
matrixInput=y, mask_seq=xmask, mask_matrix=ymask,
embedInput=xem, modelSpecs=modelSpecs )
else:
distancePredictor = ResNet4DistMatrix( rng, seqInput=x,
matrixInput=y, mask_seq=xmask, mask_matrix=ymask,
modelSpecs=modelSpecs )
## labelList is a list of label tensors, each having shape (batchSize, seqLen, seqLen) or (batchSize, seqLen, seqLen, valueDims[response] )
labelList = []
if forTrain:
## when this model is used for training. We need to define the label variable
for response in modelSpecs['responses']:
labelType = Response2LabelType(response)
rValDims = config.responseValueDims[labelType]
if labelType.startswith('Discrete'):
if rValDims > 1:
## if one response is a vector, then we use a 4-d tensor
## wtensor is for 16bit integer
labelList.append( T.wtensor4('Tlabel4' + response ) )
else:
labelList.append( T.wtensor3('Tlabel4' + response ) )
else:
if rValDims > 1:
labelList.append( T.tensor4('Tlabel4' + response ) )
else:
labelList.append( T.tensor3('Tlabel4' + response ) )
## weightList is a list of label weight tensors, each having shape (batchSize, seqLen, seqLen)
weightList = []
if len(labelList)>0 and modelSpecs['UseSampleWeight']:
weightList = [ T.tensor3('Tweight4'+response) for response in modelSpecs['responses'] ]
## for prediction, both labelList and weightList are empty
return distancePredictor, x, y, xmask, ymask, xem, labelList, weightList
# 3-dimensional ndarray v = T.tensor3(name=None, dtype=T.config.floatX) report(v) # 4-dimensional ndarray v = T.tensor4(name=None, dtype=T.config.floatX) report(v) # constructors with fixed data type. (examples with tensor4) # b: byte, w: word(16bit), l: int64, i: int32 # d:float64, f: float32, c: complex64, z: complex128 v = T.btensor4(name="v") report(v) v = T.wtensor4(name="v") report(v) v = T.itensor4(name="v") report(v) v = T.ltensor4(name="v") report(v) v = T.dtensor4(name="v") report(v) v = T.ftensor4(name="v") report(v) v = T.ctensor4(name="v")
def BuildModel(modelSpecs, forTrain=True):
rng = np.random.RandomState()
## x is for sequential features and y for matrix (or pairwise) features
x = T.tensor3('x')
y = T.tensor4('y')
## mask for x and y, respectively
xmask = T.bmatrix('xmask')
ymask = T.btensor3('ymask')
xem = None
##if any( k in modelSpecs['seq2matrixMode'] for k in ('SeqOnly', 'Seq+SS') ):
if config.EmbeddingUsed(modelSpecs):
xem = T.tensor3('xem')
## bounding box for crop of a big protein distance matrix. This box allows crop at any position.
box = None
if forTrain:
box = T.ivector('boundingbox')
## trainByRefLoss can be either 1 or -1. When this variable exists, we train the model using both reference loss and the loss of real data
trainByRefLoss = None
if forTrain and config.TrainByRefLoss(modelSpecs):
trainByRefLoss = T.iscalar('trainByRefLoss')
distancePredictor = ResNet4DistMatrix(rng,
seqInput=x,
matrixInput=y,
mask_seq=xmask,
mask_matrix=ymask,
embedInput=xem,
boundingbox=box,
modelSpecs=modelSpecs)
## labelList is a list of label tensors, each having shape (batchSize, seqLen, seqLen) or (batchSize, seqLen, seqLen, valueDims[response] )
labelList = []
if forTrain:
## when this model is used for training. We need to define the label variable
for response in modelSpecs['responses']:
labelType = Response2LabelType(response)
rValDims = GetResponseValueDims(response)
if labelType.startswith('Discrete'):
if rValDims > 1:
## if one response is a vector, then we use a 4-d tensor
## wtensor is for 16bit integer
labelList.append(T.wtensor4('Tlabel4' + response))
else:
labelList.append(T.wtensor3('Tlabel4' + response))
else:
if rValDims > 1:
labelList.append(T.tensor4('Tlabel4' + response))
else:
labelList.append(T.tensor3('Tlabel4' + response))
## weightList is a list of label weight tensors, each having shape (batchSize, seqLen, seqLen)
weightList = []
if len(labelList) > 0 and config.UseSampleWeight(modelSpecs):
weightList = [
T.tensor3('Tweight4' + response)
for response in modelSpecs['responses']
]
## for prediction, both labelList and weightList are empty
if forTrain:
return distancePredictor, x, y, xmask, ymask, xem, labelList, weightList, box, trainByRefLoss
else:
return distancePredictor, x, y, xmask, ymask, xem
# 3-dimensional ndarray v = T.tensor3(name=None, dtype=T.config.floatX) report(v) # 4-dimensional ndarray v = T.tensor4(name=None, dtype=T.config.floatX) report(v) # constructors with fixed data type. (examples with tensor4) # b: byte, w: word(16bit), l: int64, i: int32 # d:float64, f: float32, c: complex64, z: complex128 v = T.btensor4(name='v') report(v) v = T.wtensor4(name='v') report(v) v = T.itensor4(name='v') report(v) v = T.ltensor4(name='v') report(v) v = T.dtensor4(name='v') report(v) v = T.ftensor4(name='v') report(v) v = T.ctensor4(name='v')