Module: TensorStream::Utils

Included in:
TensorStream, Images, Train::LearningRateDecay, Train::SlotCreator
Defined in:
lib/tensor_stream/utils.rb

Instance Method Summary collapse

Instance Method Details

#__v_scope_name ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 126

def __v_scope_name
  Thread.current[:tensor_stream_variable_scope].map(&:name).compact.reject(&:empty?).join("/")
end

#apply_data_type_coercion(*args) ⇒ Object

Auto cast ruby constant data types to the same tensor types of other operands



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# File 'lib/tensor_stream/utils.rb', line 299

def apply_data_type_coercion(*args)
  coerced_type = check_data_types(*args)
  args.map { |a| a.is_a?(Tensor) ? a : convert_to_tensor(a, dtype: coerced_type) }
end

#assign(ref, value, name: nil) ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 200

def assign(ref, value, name: nil)
  raise "#{ref.name} not a variable" unless ref.is_a?(Variable)
  ref.assign(value, name: name)
end

#check_allowed_types(input, types) ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 279

def check_allowed_types(input, types)
  return input unless input.is_a?(Tensor)
  return input if input.data_type.nil?

  raise "#{input.source}: Parameter data type #{input.data_type} passed not in #{types.join(",")}" unless types.include?(input.data_type.to_sym)
end

#check_data_types(*args) ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 286

def check_data_types(*args)
  unique_types = args.select { |a| a.is_a?(Tensor) }. map { |a| DataTypeUtils.norm_dtype(a.data_type) }.uniq

  if unique_types.size > 1
    raise TensorStream::ValueError, "Value Error: Tensor conversion requested dtypes are different -> #{unique_types}"
  end

  unique_types.first
end

#check_if_dense(value, expected_shape = nil) ⇒ Object

Check to make sure passed array is dense

Raises:



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# File 'lib/tensor_stream/utils.rb', line 263

def check_if_dense(value, expected_shape = nil)
  return unless value.is_a?(Array)
  return if value.empty?

  expected_shape ||= shape_eval(value)

  s = expected_shape.shift
  raise TensorStream::ValueError, "Argument must be a dense tensor: #{value}, expected size #{s} got #{value.size}" if value.size != s

  return if expected_shape.empty?

  value.each do |item|
    check_if_dense(item, expected_shape.dup)
  end
end

#colocate_with(op, ignore_existing: false) ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 147

def colocate_with(op, ignore_existing: false)
  # noop for now
  yield
end

#constant(value, dtype: nil, shape: nil, internal: false, name: "Const") ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 160

def constant(value, dtype: nil, shape: nil, internal: false, name: "Const")
  shared_options = {const: true, value: value, name: name, internal: internal}

  if value.is_a?(Float)
    TensorStream::Constant.new(dtype || :float32, 0, shape || [], shared_options)
  elsif value.is_a?(Integer)
    TensorStream::Constant.new(dtype || :int32, 0, shape || [], shared_options)
  elsif value.is_a?(String)
    TensorStream::Constant.new(dtype || :string, 0, shape || [], shared_options)
  elsif !!value == value
    TensorStream::Constant.new(dtype || :boolean, 0, shape || [], shared_options)
  elsif value.is_a?(Array)
    dimension = shape || shape_eval(value)
    rank = dimension.size
    TensorStream.check_if_dense(value)

    cur_dtype = dtype || Tensor.detect_type(value.flatten.last)
    value = Tensor.cast_dtype(value, cur_dtype) unless dtype.nil?

    shared_options[:value] = value
    TensorStream::Constant.new(cur_dtype, rank, dimension, shared_options)
  end
end

#control_dependencies(control_inputs, &block) ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 237

def control_dependencies(control_inputs, &block)
  TensorStream.get_default_graph.control_dependencies(control_inputs, &block)
end

#convert_to_tensor(value, dtype: nil, name: nil) ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 241

def convert_to_tensor(value, dtype: nil, name: nil)
  return value if value.is_a?(Tensor)
  return convert_to_tensor(value.call) if value.is_a?(Proc)
  # raise "Invalid tensor value" if value.nil?

  if value.is_a?(Array) && value.detect { |v| v.is_a?(Tensor) }
    return TensorStream.stack(value) if value.size > 1

    return TensorStream.expand_dims(value[0], 0)
  end

  if value.is_a?(TensorShape)
    value = value.shape
  end

  check_if_dense(value)
  i_cons(value, dtype: dtype || Tensor.detect_type(value), name: name)
end

#device(device_uri, &block) ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 101

def device(device_uri, &block)
  get_default_graph.device(device_uri, &block)
end

#disable_eager_execution ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 23

def disable_eager_execution
  TensorStream::Graph.get_default_graph.disable_eager_execution
end

#dynamic_stitch(indices, data, name: nil) ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 188

def dynamic_stitch(indices, data, name: nil)
  TensorStream::DynamicStitch.new(:dynamic_stitch, [indices, data], name: name)
end

#enable_eager_execution ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 19

def enable_eager_execution
  TensorStream::Graph.get_default_graph.enable_eager_execution
end

#executing_eagerly? ⇒ Boolean

Returns:

  • (Boolean) 


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# File 'lib/tensor_stream/utils.rb', line 27

def executing_eagerly?
  TensorStream::Graph.get_default_graph.executing_eagerly?
end

#float32 ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 3

def float32
  Types.float32
end

#get_collection(name, options = {}) ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 196

def get_collection(name, options = {})
  Graph.get_default_graph.get_collection(name, options)
end

#get_default_graph ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 11

def get_default_graph
  TensorStream::Graph.get_default_graph
end

#get_variable(name, dtype: nil, shape: nil, initializer: nil, trainable: true, collections: nil, validate_shape: false) ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 192

def get_variable(name, dtype: nil, shape: nil, initializer: nil, trainable: true, collections: nil, validate_shape: false)
  get_variable_scope.get_variable(name, dtype: dtype, shape: shape, initializer: initializer, trainable: trainable, collections: collections)
end

#get_variable_scope ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 116

def get_variable_scope
  unless Thread.current[:tensor_stream_variable_scope]
    variable_scope = VariableScope.new
    Thread.current[:tensor_stream_variable_scope] = [variable_scope]
    return variable_scope
  end

  Thread.current[:tensor_stream_variable_scope].last
end

#global_variables_initializer ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 211

def global_variables_initializer
  TensorStream::Variable.global_variables_initializer
end

#graph ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 7

def graph
  TensorStream::Graph.new
end

#group(inputs, name: nil) ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 184

def group(inputs, name: nil)
  TensorStream::ControlFlow.new(:group, inputs, nil, name: name)
end

#image ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 223

def image
  TensorStream::Images
end

#layers ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 156

def layers
  TensorStream::Layers
end

#list_local_devices ⇒ Object

List available evaluators + devices in the current local environment Returns:

  • An array containing the names of those devices



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# File 'lib/tensor_stream/utils.rb', line 35

def list_local_devices
  local_name = "job:localhost"
  TensorStream::Evaluator.evaluators.collect { |k, v|
    v[:class].query_supported_devices.collect do |device_str|
      [local_name, "ts:#{k}:#{device_str.name}"].join("/")
    end
  }.flatten
end

#math ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 219

def math
  TensorStream::Maths
end

#name_scope(name, default_name = nil, default: nil, values: nil) ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 105

def name_scope(name, default_name = nil, default: nil, values: nil)
  if values
    graph_count = values.select { |v| v.is_a?(Tensor) }.map(&:graph).map(&:object_id).uniq.size
    raise "values are not on the same graph" if graph_count > 1
  end

  get_default_graph.name_scope(name || default_name || default) do |scope|
    yield scope if block_given?
  end
end

#placeholder(dtype, shape: nil, name: nil) ⇒ Object

Inserts a placeholder for a tensor that will be always fed.



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# File 'lib/tensor_stream/utils.rb', line 207

def placeholder(dtype, shape: nil, name: nil)
  TensorStream::Placeholder.new(dtype, nil, shape, name: name)
end

#program {|_self| ... } ⇒ Object

Yields:

  • (_self)

Yield Parameters:



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# File 'lib/tensor_stream/utils.rb', line 152

def program
  yield self
end

#reset_default_graph ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 15

def reset_default_graph
  TensorStream::Graph.get_default_graph.reset
end

#session(evaluator = nil, thread_pool_class: Concurrent::ImmediateExecutor, log_device_placement: false, profile_enabled: false) {|session| ... } ⇒ Object

Creates a session context where operations can be executed

Args:

evaluator: Specific evaluator to use, otherwise the best evaluator will automatically be determined

Options:

thread_pool_class: Class to use to manage thread pooling
log_device_placement: Show assigned device/evalutor for each tensor op
profile_enabled: Log performance metrics for each operation

Yields:



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# File 'lib/tensor_stream/utils.rb', line 140

def session(evaluator = nil, thread_pool_class: Concurrent::ImmediateExecutor, log_device_placement: false, profile_enabled: false)
  session = TensorStream::Session.new(evaluator, thread_pool_class: thread_pool_class, log_device_placement: log_device_placement, profile_enabled: profile_enabled)
  yield session if block_given?

  session
end

#set_random_seed(seed) ⇒ Object

Sets random seed to use for the default graph



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# File 'lib/tensor_stream/utils.rb', line 233

def set_random_seed(seed)
  TensorStream.get_default_graph.random_seed = seed
end

#train ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 215

def train
  TensorStream::Trainer
end

#trainable_variables ⇒ Object 



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# File 'lib/tensor_stream/utils.rb', line 227

def trainable_variables
  TensorStream.get_default_graph.get_collection(TensorStream::GraphKeys::TRAINABLE_VARIABLES)
end

#variable(value, name: nil, initializer: nil, graph: nil, dtype: nil, trainable: true) ⇒ Object

Creates a variable A variable maintains state across sessions



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# File 'lib/tensor_stream/utils.rb', line 47

def variable(value, name: nil, initializer: nil, graph: nil, dtype: nil, trainable: true)
  common_options = {
    initializer: TensorStream.convert_to_tensor(initializer || value),
    name: name,
    graph: graph,
    dtype: dtype,
    trainable: trainable,
  }
  tensor = if value.is_a?(String)
             i_var(dtype || :string, 0, [], get_variable_scope, common_options)
           elsif value.is_a?(Integer)
             i_var(dtype || :int32, 0, [], get_variable_scope, common_options)
           elsif value.is_a?(Float)
             i_var(dtype || :float32, 0, [], get_variable_scope, common_options)
           else
             i_var(dtype || :float32, 0, nil, get_variable_scope, common_options)
           end
  tensor
end

#variable_scope(scope = nil, default_name = nil, reuse: nil, initializer: nil) ⇒ Object

Defines a variable context manager



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# File 'lib/tensor_stream/utils.rb', line 69

def variable_scope(scope = nil, default_name = nil, reuse: nil, initializer: nil)
  Thread.current[:tensor_stream_variable_scope] ||= [VariableScope.new]

  # uniquenifier
  if scope.nil? && default_name
    same_names = get_variable_scope.used_names.select { |s| s.start_with?(default_name) }
    new_name = default_name
    index = 1
    while same_names.include?(new_name)
      new_name = "#{default_name}_#{index}"
      index += 1
    end
    scope = new_name
  end

  variable_scope = VariableScope.new(name: scope, reuse: reuse, initializer: initializer)
  get_variable_scope.register_name(scope || "")
  Thread.current[:tensor_stream_variable_scope] << variable_scope
  scope_name = __v_scope_name
  if block_given?
    begin
      TensorStream.get_default_graph.name_scope(scope) do
        yield(scope_name)
      end
    ensure
      Thread.current[:tensor_stream_variable_scope].pop
    end
  else
    variable_scope
  end
end