Function ExamplePartialRun

tensorflow/go/session_test.go:187–250

()

}

func ExamplePartialRun() {
	var (
		// Create a graph: a + 2 + 3 + b.
		//
		// Skipping error handling for brevity of this example.
		// The 'op' package can be used to make graph construction code
		// with error handling more succinct.
		g        = NewGraph()
		a, _     = Placeholder(g, "a", Int32)
		b, _     = Placeholder(g, "b", Int32)
		two, _   = Const(g, "Two", int32(2))
		three, _ = Const(g, "Three", int32(3))

		plus2, _ = Add(g, "plus2", a, two)       // a + 2
		plus3, _ = Add(g, "plus3", plus2, three) // (a + 2) + 3
		plusB, _ = Add(g, "plusB", plus3, b)     // ((a + 2) + 3) + b

	)
	sess, err := NewSession(g, nil)
	if err != nil {
		panic(err)
	}
	defer sess.Close()

	// All the feeds, fetches and targets for subsequent PartialRun.Run
	// calls must be provided at setup.
	pr, err := sess.NewPartialRun(
		[]Output{a, b},
		[]Output{plus2, plusB},
		[]*Operation{plus3.Op},
	)
	if err != nil {
		panic(err)
	}

	// Feed 'a=1', fetch 'plus2', and compute (but do not fetch) 'plus3'.
	// Imagine this to be the forward pass of unsupervised neural network
	// training of a robot.
	val, _ := NewTensor(int32(1))
	fetches, err := pr.Run(
		map[Output]*Tensor{a: val},
		[]Output{plus2},
		nil)
	if err != nil {
		panic(err)
	}
	v1 := fetches[0].Value().(int32)

	// Now, feed 'b=4', fetch 'plusB=a+2+3+b'
	// Imagine this to be the result of actuating the robot to determine
	// the error produced by the current state of the neural network.
	val, _ = NewTensor(int32(4))
	fetches, err = pr.Run(
		map[Output]*Tensor{b: val},
		[]Output{plusB},
		nil)
	if err != nil {
		panic(err)