Module: TensorStream::OpHelper

Overview

module that contains helper functions useful for ops

Instance Method Summary collapse

Instance Method Details

#_op(code, *args) ⇒ Object


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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 = {}) ⇒ Object


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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) ⇒ Object


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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) ⇒ Boolean

Returns:

  • (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 39

def i_cons(value, options = {})
  TensorStream.constant(value, options.merge(internal: true))
end

#i_op(code, *args) ⇒ 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 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 = {}) ⇒ Object


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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) ⇒ Boolean

Returns:

  • (Boolean)

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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) ⇒ Object


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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) ⇒ Object


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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) ⇒ Object


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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) ⇒ Object


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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