Module: TensorStream::OpHelper
- Included in:
- TensorStream, Evaluator::RubyEvaluator, MathGradients, Tensor
- Defined in:
- lib/tensor_stream/helpers/op_helper.rb
Overview
module that contains helper functions useful for ops
Instance Method Summary collapse
- #cons(value, options = {}) ⇒ Object
- #dtype_eval(rank, value) ⇒ Object
- #fp_type?(type) ⇒ Boolean
- #i_cons(value, options = {}) ⇒ Object
-
#i_op(code, t_a, t_b = nil, options = {}) ⇒ Object
same as op but with a marker that it was internal generated.
- #op(code, t_a, t_b = nil, options = {}) ⇒ Object
- #shape_eval(input, output_type = :int32) ⇒ Object
- #val_to_dtype(value) ⇒ Object
Instance Method Details
#cons(value, options = {}) ⇒ Object
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# File 'lib/tensor_stream/helpers/op_helper.rb', line 13 def cons(value, = {}) TensorStream.constant(value, ) end |
#dtype_eval(rank, value) ⇒ Object
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# File 'lib/tensor_stream/helpers/op_helper.rb', line 36 def dtype_eval(rank, value) dtype = Tensor.detect_type(value[0]) rank += 1 if dtype == :array [dtype, rank, value[0], value.size] end |
#fp_type?(type) ⇒ Boolean
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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 = {}) ⇒ Object
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# File 'lib/tensor_stream/helpers/op_helper.rb', line 17 def i_cons(value, = {}) TensorStream.constant(value, .merge(internal: true)) end |
#i_op(code, t_a, t_b = nil, options = {}) ⇒ Object
same as op but with a marker that it was internal generated
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# File 'lib/tensor_stream/helpers/op_helper.rb', line 9 def i_op(code, t_a, t_b = nil, = {}) Operation.new(code.to_sym, t_a, t_b, .merge(internal: true)) end |
#op(code, t_a, t_b = nil, options = {}) ⇒ Object
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# File 'lib/tensor_stream/helpers/op_helper.rb', line 4 def op(code, t_a, t_b = nil, = {}) Operation.new(code.to_sym, t_a, t_b, ) end |
#shape_eval(input, output_type = :int32) ⇒ Object
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# File 'lib/tensor_stream/helpers/op_helper.rb', line 21 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 |
#val_to_dtype(value) ⇒ Object
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# File 'lib/tensor_stream/helpers/op_helper.rb', line 44 def val_to_dtype(value) if value.is_a?(String) :string elsif value.is_a?(Float) :float32 elsif value.is_a?(Integer) :int32 elsif value.is_a?(Array) :array else :float32 end end |