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is_enum_namestack(self.nameStack)
self.evaluate_enum_stack()
self.evaluate_property_stack()
elif (self.braceDepth < 1)
debug_print( "trace" )
debug_print( "Global stuff: %s"%self.nameStack )
elif (self.braceDepth > len(self.nameSpaces)
debug_print( "trace" )
self.nameStackHistory.append((nameStackCopy, self.curClass)
evaluate_enum_stack(self)
debug_print( "evaluating enum" )
CppEnum(self.nameStack)
len(list(newEnum.keys()
len(self.curClass)
self.cur_namespace(False)
append(newEnum)
self.cur_namespace(True)
self.enums.append(newEnum)
self.evaluate_property_stack()
strip_parent_keys(self)
len(obj_queue)
obj_queue.pop()
trace_print("pop %s type %s"%(obj, type(obj)
obj.keys()
trace_print("Stripped parent from %s"%obj.keys()
hasattr(obj, 'keys')
obj.keys()
trace_print("-Try key %s"%(k)
trace_print("-type %s"%(type(obj[k])
type(obj[k])
trace_print("push l %s"%i)
obj_queue.append(i)
type(obj[k])
len(obj)
trace_print("push d %s"%obj[k])
obj_queue.append(obj[k])
type(obj[k])
type(type(0)
type(obj[k])
type(obj[k])
trace_print("next key\n")
trace_print("Exception")
toJSON(self, indent=4)
self.strip_parent_keys()
json.dumps(self.__dict__, indent=indent)
__repr__(self)
repr(rtn)
__str__(self)
list(self.classes.keys()
resource_loader.get_path_to_datafile("_lstm_ops.so")
sigmoid(cs_prev * wci + i)
sigmoid(cs_prev * wcf + f)
tanh(ci)
clip(cs, cell_clip)
sigmoid(cs * wco + o)
tanh(cs)
shape (batch_size, num_inputs)
operation (optional)
objects (i, cs, f, o, ci, co, h)
cs_prev.get_shape()
with_rank(2)
ValueError("cell_size from `cs_prev` should not be None.")
array_ops.constant(0, dtype=dtypes.float32, shape=[cell_size])
TODO(williamchan)
operation (optional)
objects (i, cs, f, o, ci, co, h)
get_shape()
with_rank(2)
b.get_shape()
with_rank(1)
ValueError("`b` shape must not be None.")
array_ops.constant(0, dtype=dtypes.float32, shape=[cell_size])
array_ops.stack(x)
array_ops.unstack(i)
array_ops.unstack(cs)
array_ops.unstack(o)
array_ops.unstack(ci)
array_ops.unstack(h)
ops.RegisterGradient("LSTMBlockCell")
_LSTMBlockCellGrad(op, *grad)
op.inputs (i, cs, f, o, ci, co, _)
op.outputs (_, cs_grad, _, _, _, _, h_grad)
x.get_shape()
with_rank(2)
x.get_shape()
with_rank(2)
ValueError("input_size from `x` should not be None.")
cs_prev.get_shape()
with_rank(2)
ValueError("cell_size from `cs_prev` should not be None.")
op.get_attr("use_peephole")
math_ops.matmul(dicfo, w, transpose_b=True)
array_ops.slice(xh_grad, (0, 0)
x_grad.get_shape()
merge_with(x.get_shape()
array_ops.slice(xh_grad, (0, input_size)
h_prev_grad.get_shape()
merge_with(h_prev.get_shape()
array_ops.concat([x, h_prev], 1)
math_ops.matmul(xh, dicfo, transpose_a=True)