module TensorStream::OpHelper
module that contains helper functions useful for ops
Public Instance Methods
_op(code, *args)
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# File lib/tensor_stream/helpers/op_helper.rb, line 4 def _op(code, *args) default_graph = Graph.get_default_graph op = default_graph.add_op!(code.to_sym, *args) if !default_graph.get_dependency_scope.nil? i_op(:identity, op, default_graph.get_dependency_scope, name: [op.name, "tuple", "control_dependency"].join("/")) else op end end
cons(value, options = {})
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# File lib/tensor_stream/helpers/op_helper.rb, line 35 def cons(value, options = {}) TensorStream.constant(value, options) end
format_source(trace)
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# File lib/tensor_stream/helpers/op_helper.rb, line 66 def format_source(trace) grad_source = trace.detect { |c| c.to_s.include?(File.join("lib", "tensor_stream", "math_gradients")) } source = trace.reject { |c| c.to_s.include?(File.join("lib", "tensor_stream")) }.first [grad_source, trace].compact.join("\n") end
fp_type?(type)
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# File lib/tensor_stream/helpers/op_helper.rb, line 58 def fp_type?(type) TensorStream::Ops::FLOATING_POINT_TYPES.include?(type) end
i_cons(value, options = {})
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# File lib/tensor_stream/helpers/op_helper.rb, line 39 def i_cons(value, options = {}) TensorStream.constant(value, options.merge(internal: true)) end
i_op(code, *args)
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same as op but with a marker that it was internal generated
# File lib/tensor_stream/helpers/op_helper.rb, line 16 def i_op(code, *args) options = if args.last.is_a?(Hash) args.pop else {} end args << options.merge(internal: true) Graph.get_default_graph.add_op!(code.to_sym, *args) end
i_var(data_type, rank, shape, variable_scope, options = {})
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# File lib/tensor_stream/helpers/op_helper.rb, line 27 def i_var(data_type, rank, shape, variable_scope, options = {}) new_var = Variable.new(data_type) new_var.prepare(rank, shape, variable_scope, options) new_var.op = new_var.graph.add_variable!(new_var, options.merge(shape: @shape, data_type: data_type)) new_var end
int_type?(type)
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# File lib/tensor_stream/helpers/op_helper.rb, line 62 def int_type?(type) TensorStream::Ops::INTEGER_TYPES.include?(type) end
reduced_shape(input_shape, axes)
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# File lib/tensor_stream/helpers/op_helper.rb, line 87 def reduced_shape(input_shape, axes) input_shape = TensorStream.convert_to_tensor(input_shape) axes = TensorStream.convert_to_tensor(axes) input_rank = i_op(:size, input_shape) axes = TensorStream.range(0, input_rank) if axes.nil? axes = (axes + input_rank) % input_rank axes_shape = i_op(:shape, axes) TensorStream.dynamic_stitch([TensorStream.range(0, input_rank), axes], [input_shape, i_op(:fill, axes_shape, 1)]) end
shape_eval(input, output_type = :int32)
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# File lib/tensor_stream/helpers/op_helper.rb, line 43 def shape_eval(input, output_type = :int32) return [] unless input.is_a?(Array) arr = [] arr_ptr = input Kernel.loop do arr << (TensorStream::Ops::FLOATING_POINT_TYPES.include?(output_type) ? arr_ptr.size.to_f : arr_ptr.size) arr_ptr = arr_ptr[0] break unless arr_ptr.is_a?(Array) end arr end
shape_full_specified(tensor)
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# File lib/tensor_stream/helpers/op_helper.rb, line 79 def shape_full_specified(tensor) return false if tensor.shape.nil? return false if tensor.shape.shape.nil? tensor.shape.shape.each { |s| return false if s.nil? || (s < 0) } true end
shapes_fully_specified_and_equal(x, y)
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# File lib/tensor_stream/helpers/op_helper.rb, line 72 def shapes_fully_specified_and_equal(x, y) return false if !shape_full_specified(x) || !shape_full_specified(y) return false if x.shape.shape != y.shape.shape true end