Python function.Defun方法代碼示例

# 需要導入模塊: from tensorflow.python.framework import function [as 別名]
# 或者: from tensorflow.python.framework.function import Defun [as 別名]
def BatchMatMul(a, b):
  use_fp32_batch_matmul = (os.environ["use_fp32_batch_matmul"] == "true")
  xla_compile = (os.environ["xla_compile"] == "true")
  if use_fp32_batch_matmul:
    def DoFn(a, b):
      dtype = a.dtype
      a = tf.to_float(a)
      b = tf.to_float(b)
      return tf.cast(tf.matmul(a, b), dtype)
    # If using xla_compile, the fwd and bak per tower are wrapped in xla_compile
    if not xla_compile:
      DoFn = function.Defun(noinline=True)(DoFn)
      res = DoFn(a, b)
      res.set_shape((None, None, b.shape[-1].value))
    else:
      # If xla_compile, leave to xla to handle the casts.
      res = DoFn(a, b)
  else:
    res = tf.matmul(a, b)
  return res 

# 需要導入模塊: from tensorflow.python.framework import function [as 別名]
# 或者: from tensorflow.python.framework.function import Defun [as 別名]
def testWhileFuncBasic(self):
    @function.Defun(tf.float32)
    def func(x):
      return tf.square(tf.square(x))

    with self.test_session():
      x = tf.constant(2.0, tf.float32)
      r = tf.while_loop(
          lambda i, v: i < 2,
          lambda i, v: [i + 1, func(v)],
          [tf.constant(0), x],
          [tensor_shape.unknown_shape(), tensor_shape.unknown_shape()])
      self.assertEqual(r[1].eval(), 65536.0)

      r = tf.gradients(r, x)[0]
      self.assertEqual(r.eval(), 524288.0)
      self.assertEqual(len([op for op in x.graph.get_operations()
                            if op.type == "Stack"]),
                       1) 

# 需要導入模塊: from tensorflow.python.framework import function [as 別名]
# 或者: from tensorflow.python.framework.function import Defun [as 別名]
def testGradientFunc(self):

    @function.Defun(tf.float32, func_name="XSquarePlusOneFn")
    def XSquarePlusOne(x):
      return x * x + 1.0

    @function.Defun(tf.float32, tf.float32)
    def XSquarePlusOneGrad(x, dy):
      dx = functional_ops._symbolic_gradient(
          input=[x, dy], Tout=[tf.float32], f="XSquarePlusOneFn", name="dx")
      return dx

    g = tf.Graph()
    with g.as_default():
      call_f = XSquarePlusOne([2.0])
      call_g = XSquarePlusOneGrad([2.0], [0.1])

      with tf.Session() as sess:
        self.assertAllClose([5.0], sess.run(call_f))
        self.assertAllClose([0.4], sess.run(call_g)) 

# 需要導入模塊: from tensorflow.python.framework import function [as 別名]
# 或者: from tensorflow.python.framework.function import Defun [as 別名]
def testTanhSymGrad(self):

    @function.Defun(tf.float32)
    def Forward(x):
      return tf.reduce_sum(tf.tanh(x))

    g = tf.Graph()
    with g.as_default():
      x = tf.placeholder(tf.float32)
      y = Forward(x)
      dx = tf.gradients([y], [x])

    inp = np.array([-1, 1, 2, -2], dtype=np.float32)
    feed = {x: inp}
    cfg = tf.ConfigProto(graph_options=tf.GraphOptions(
        optimizer_options=tf.OptimizerOptions(
            opt_level=tf.OptimizerOptions.L1, do_function_inlining=True)))
    with tf.Session(graph=g, config=cfg) as sess:
      out, = sess.run(dx, feed)
    self.assertAllClose(1 - np.square(np.tanh(inp)), out) 

# 需要導入模塊: from tensorflow.python.framework import function [as 別名]
# 或者: from tensorflow.python.framework.function import Defun [as 別名]
def testCustomGradientError(self):
    dtype = tf.float32

    @function.Defun(dtype, dtype, dtype)
    def Grad(x, dy, dz):
      # Should have returned 1 result.
      return x, dy + dz

    @function.Defun(dtype, grad_func=Grad)
    def Forward(x):
      return x, x

    g = tf.Graph()
    with g.as_default():
      inp = tf.placeholder(dtype)
      out = tf.add_n(Forward(inp))
      dinp = tf.gradients(out, [inp])

    x = np.random.uniform(-10., 10., size=(4, 9)).astype(np.float32)
    with tf.Session(graph=g) as sess:
      with self.assertRaisesRegexp(
          tf.errors.InvalidArgumentError,
          "SymGrad expects to return 1.*but get 2.*instead"):
        _ = sess.run(dinp, {inp: x}) 

# 需要導入模塊: from tensorflow.python.framework import function [as 別名]
# 或者: from tensorflow.python.framework.function import Defun [as 別名]
def testSymGradAttr(self):

    @function.Defun(noinline=True)
    def Foo(x):
      return x * 2

    self.assertTrue(
        Foo.instantiate([tf.float32]).definition.attr["_noinline"].b)

    g = tf.Graph()
    with g.as_default():
      x = tf.constant(3.0)
      y = Foo(x)
      dx, = tf.gradients(y, [x])

    cfg = tf.ConfigProto(graph_options=tf.GraphOptions(
        optimizer_options=tf.OptimizerOptions(
            opt_level=tf.OptimizerOptions.L0,
            do_common_subexpression_elimination=True,
            do_function_inlining=True,
            do_constant_folding=True)))

    with self.test_session(graph=g, config=cfg):
      self.assertAllClose(y.eval(), 6.)
      self.assertAllClose(dx.eval(), 2.) 

# 需要導入模塊: from tensorflow.python.framework import function [as 別名]
# 或者: from tensorflow.python.framework.function import Defun [as 別名]
def testDefineFunctionNames(self):

    @function.Defun(tf.float32, func_name="Foo")
    def Foo(a):
      return a + 1

    with tf.Graph().as_default():
      call1 = Foo([1.0])
      self.assertEqual("Foo", call1.op.name)
      call2 = Foo([1.0])
      self.assertEqual("Foo_1", call2.op.name)
      # pylint: disable=unexpected-keyword-arg
      call3 = Foo([1.0], name="mine")
      self.assertEqual("mine", call3.op.name)
      with tf.name_scope("my"):
        call4 = Foo([1.0], name="precious")
        self.assertEqual("my/precious", call4.op.name) 

# 需要導入模塊: from tensorflow.python.framework import function [as 別名]
# 或者: from tensorflow.python.framework.function import Defun [as 別名]
def testFunctionDecorator(self):

    @function.Defun(tf.float32, func_name="Minus1")
    def Minus1(b):
      return b - 1.0

    with tf.Graph().as_default():
      call1 = Minus1([2.])
      self.assertTrue(isinstance(Minus1, function._DefinedFunction))
      self.assertEqual(Minus1.name, "Minus1")
      # pylint: disable=unexpected-keyword-arg
      call2 = Minus1(call1, name="next")
      # pylint: enable=unexpected-keyword-arg
      self.assertEqual("next", call2.op.name)
      with tf.Session() as sess:
        self.assertAllEqual([1], sess.run(call1))
        self.assertAllEqual([0], sess.run(call2)) 

# 需要導入模塊: from tensorflow.python.framework import function [as 別名]
# 或者: from tensorflow.python.framework.function import Defun [as 別名]
def testCapture(self):
    g = tf.Graph()
    with g.as_default():
      w = tf.Variable(tf.constant([[1.0]]))
      b = tf.Variable(tf.constant([2.0]))

      # Foo() captures w and b.
      @function.Defun(tf.float32)
      def Foo(x):

        # Plus() captures b.
        @function.Defun(tf.float32)
        def Plus(y):
          return y + b

        return Plus(tf.matmul(w, x))

      y = Foo(tf.constant([[10.]]))

    with self.test_session(graph=g):
      tf.global_variables_initializer().run()
      self.assertAllEqual(y.eval(), [[12.0]]) 

# 需要導入模塊: from tensorflow.python.framework import function [as 別名]
# 或者: from tensorflow.python.framework.function import Defun [as 別名]
def testGradient(self):

    @function.Defun(func_name="Spec")
    def G(x, dy):
      return x * dy

    @function.Defun(grad_func=G)
    def F(x):
      return tf.exp(x) - tf.exp(-x)

    for dtype in [tf.float32, tf.float64]:
      g = tf.Graph()
      with g.as_default():
        x = tf.constant(0.25, dtype)
        y = F(x)
        dx, = tf.gradients(y, x)

        with self.test_session(graph=g):
          self.assertAllClose(dx.eval(), 0.25) 

# 需要導入模塊: from tensorflow.python.framework import function [as 別名]
# 或者: from tensorflow.python.framework.function import Defun [as 別名]
def testTPUModuleInitializeOnceWithDefun(self):
    spec = hub.create_module_spec(stateful_random_rv_module_fn)

    @function.Defun()
    def import_computation():
      context = TPUReplicateContext()
      context.Enter()
      m = hub.Module(spec, name="module_", trainable=True)
      return [m(), m()]

    with tf_v1.Graph().as_default(), tf_v1.Session() as sess:
      x = import_computation()
      sess.run(tf_v1.global_variables_initializer())
      got = sess.run(x)
      # Check the values are equal. If the initializer ran on each call,
      # the values would be different.
      self.assertEqual(got[0], got[1]) 

# 需要導入模塊: from tensorflow.python.framework import function [as 別名]
# 或者: from tensorflow.python.framework.function import Defun [as 別名]
def testTPUPruneWithUnusedInput(self):
    spec = hub.create_module_spec(unused_input_module_fn)

    @function.Defun()
    def import_computation(x):
      context = TPUReplicateContext()
      context.Enter()
      m = hub.Module(spec, name="module_", trainable=True)
      return m({
          "x": tf.cast(x, dtype=tf.int64),
          "unused": tf.constant(2, dtype=tf.int64)
      })

    with tf_v1.Graph().as_default(), tf_v1.Session() as sess:
      x = import_computation(5)
      got = sess.run(x)
      self.assertEqual(got, 25) 

# 需要導入模塊: from tensorflow.python.framework import function [as 別名]
# 或者: from tensorflow.python.framework.function import Defun [as 別名]
def testTPUModuleDoesntPruneControlDependencies(self):
    spec = hub.create_module_spec(control_dependency_module_fn)

    @function.Defun()
    def import_computation():
      context = TPUReplicateContext()
      context.Enter()
      m = hub.Module(spec, name="module_", trainable=True)
      return m()

    with tf_v1.Graph().as_default(), tf_v1.Session() as sess:
      x = import_computation()
      got = sess.run(x)
      self.assertEqual(got, 5.0)
      # If the op got pruned, the following get_operation_by_name should fail
      # with a dependency error.
      tf_v1.get_default_graph().get_operation_by_name("module_/dependency_op") 

# 需要導入模塊: from tensorflow.python.framework import function [as 別名]
# 或者: from tensorflow.python.framework.function import Defun [as 別名]
def _test_spop_placeholder_without_shape_info():
    with tf.Graph().as_default():

        @function.Defun(*[tf.int32]*2)
        def Forward(x,y):
            print(x.name)
            print(y.name)
            b = tf.add(x, y)
            return b
        pl1 = tf.placeholder(tf.int32,name="pl1")
        pl2 = tf.placeholder(tf.int32,name="pl2")
        pl3 = tf.placeholder(tf.int32, name="pl3")
        data = np.array([[-1, 1], [2, -2]], dtype=np.int32)
        data2 = np.array([[-2, 3], [4, -6]], dtype=np.int32)
        data3 = np.array([[-2, 3], [4, -6]], dtype=np.int32)
        z1 = gen_functional_ops.StatefulPartitionedCall(args=[pl1,pl2], Tout=[tf.int32],f=Forward)
        z2 = z1 + pl3
        compare_tf_with_tvm([data, data2, data3], ['pl1:0', 'pl2:0', 'pl3:0'],
                            ['StatefulPartitionedCall:0',z2.name],  mode='vm', init_global_variables=True) 

# 需要導入模塊: from tensorflow.python.framework import function [as 別名]
# 或者: from tensorflow.python.framework.function import Defun [as 別名]
def _test_spop_function_invocation_defun():
    with tf.Graph().as_default():

        def fun1(a):
            return tf.multiply(a,a)

        def fun2(b):
            return tf.multiply(b,b)

        @function.Defun(dtypes.float32, dtypes.float32, func_name="Fun3")
        def fun3(x,y):
            x = fun2(x)
            y = fun1(y)
            z = tf.add(x,y)
            return z

        op = gen_functional_ops.StatefulPartitionedCall(args=[tf.constant(10.5),tf.constant(20.4)],
                                                        Tout=[dtypes.float32], f=fun3, name="SpopFnInvocation")
        compare_tf_with_tvm([],[], 'SpopFnInvocation:0', mode='vm', init_global_variables=True)