Python variables.initialize_variables函数代码示例

 def test_local_variable(self):
   with self.test_session() as sess:
     self.assertEquals([], variables_lib.local_variables())
     value0 = 42
     variables_lib2.local_variable(value0)
     value1 = 43
     variables_lib2.local_variable(value1)
     variables = variables_lib.local_variables()
     self.assertEquals(2, len(variables))
     self.assertRaises(errors_impl.OpError, sess.run, variables)
     variables_lib.initialize_variables(variables).run()
     self.assertAllEqual(set([value0, value1]), set(sess.run(variables)))

  def testConvertVariablesToConsts(self):
    with ops.Graph().as_default():
      variable_node = variables.Variable(1.0, name="variable_node")
      _ = variables.Variable(1.0, name="unused_variable_node")
      output_node = math_ops_lib.multiply(
          variable_node, 2.0, name="output_node")
      with session.Session() as sess:
        init = variables.initialize_variables([variable_node])
        sess.run(init)
        output = sess.run(output_node)
        self.assertNear(2.0, output, 0.00001)
        variable_graph_def = sess.graph.as_graph_def()
        # First get the constant_graph_def when variable_names_whitelist is set,
        # note that if variable_names_whitelist is not set an error will be
        # thrown because unused_variable_node is not initialized.
        constant_graph_def = graph_util.convert_variables_to_constants(
            sess,
            variable_graph_def, ["output_node"],
            variable_names_whitelist=set(["variable_node"]))

        # Then initialize the unused variable, and get another
        # constant_graph_def when variable_names_whitelist is not set.
        sess.run(variables.global_variables_initializer())
        constant_graph_def_without_variable_whitelist = (
            graph_util.convert_variables_to_constants(sess, variable_graph_def,
                                                      ["output_node"]))

        # The unused variable should be cleared so the two graphs should be
        # equivalent.
        self.assertEqual(
            str(constant_graph_def),
            str(constant_graph_def_without_variable_whitelist))

        # Test variable name black list. This should result in the variable not
        # being a const.
        sess.run(variables.global_variables_initializer())
        constant_graph_def_with_blacklist = (
            graph_util.convert_variables_to_constants(
                sess,
                variable_graph_def, ["output_node"],
                variable_names_blacklist=set(["variable_node"])))
        variable_node = None
        for node in constant_graph_def_with_blacklist.node:
          if node.name == "variable_node":
            variable_node = node
        self.assertIsNotNone(variable_node)
        self.assertEqual(variable_node.op, "VariableV2")

    # Now we make sure the variable is now a constant, and that the graph still
    # produces the expected result.
    with ops.Graph().as_default():
      _ = importer.import_graph_def(constant_graph_def, name="")
      self.assertEqual(4, len(constant_graph_def.node))
      for node in constant_graph_def.node:
        self.assertNotEqual("Variable", node.op)
        self.assertNotEqual("VariableV2", node.op)
      with session.Session() as sess:
        output_node = sess.graph.get_tensor_by_name("output_node:0")
        output = sess.run(output_node)
        self.assertNear(2.0, output, 0.00001)

  def testConvertVariablesToConstsWithFunctions(self):
    @function.Defun(dtypes.float32)
    def plus_one(x):
      return x + 1.0

    with ops.Graph().as_default():
      variable_node = variables.Variable(1.0, name="variable_node")
      _ = variables.Variable(1.0, name="unused_variable_node")
      defun_node = plus_one(variable_node)
      output_node = math_ops_lib.multiply(
          defun_node, 2.0, name="output_node")

      with session.Session() as sess:
        init = variables.initialize_variables([variable_node])
        sess.run(init)
        output = sess.run(output_node)
        self.assertNear(4.0, output, 0.00001)
        variable_graph_def = sess.graph.as_graph_def()

        # First get the constant_graph_def when variable_names_whitelist is set,
        # note that if variable_names_whitelist is not set an error will be
        # thrown because unused_variable_node is not initialized.
        constant_graph_def = graph_util.convert_variables_to_constants(
            sess,
            variable_graph_def, ["output_node"],
            variable_names_whitelist=set(["variable_node"]))

        self.assertEqual(variable_graph_def.library,
                         constant_graph_def.library)

 def __setitem__(self, index, value):
   for use_gpu in [False, True]:
     with self.test.test_session(use_gpu=use_gpu) as sess:
       var = variables.Variable(self.x)
       sess.run(variables.initialize_variables([var]))
       val = sess.run(var[index].assign(
           constant_op.constant(
               value, dtype=self.tensor_type)))
       valnp = np.copy(self.x_np)
       valnp[index] = np.array(value)
       self.test.assertAllEqual(val, valnp)

  def __setitem__(self, index, value):
    value = np.array(value).astype(self.tensor_type.as_numpy_dtype)
    # Give the value a non-zero imaginary component for complex types.
    if self.tensor_type.is_complex:
      value -= 1j * value

    with self.test.test_session(use_gpu=True) as sess:
      var = variables.Variable(self.x)
      sess.run(variables.initialize_variables([var]))
      val = sess.run(var[index].assign(
          constant_op.constant(
              value, dtype=self.tensor_type)))
      valnp = np.copy(self.x_np)
      valnp[index] = np.array(value)
      self.test.assertAllEqual(val, valnp)

  def __setitem__(self, index, value):
    value = np.array(value).astype(self.tensor_type.as_numpy_dtype)
    # Give the value a non-zero imaginary component for complex types.
    if self.tensor_type.is_complex:
      value -= 1j * value

    with self.test.test_session(use_gpu=True) as sess:
      var = variables.Variable(self.x)
      sess.run(variables.initialize_variables([var]))
      val = sess.run(var[index].assign(value))
      # val_copy is used to check that tf.assign works equivalently to the
      # assign method above.
      val_copy = sess.run(state_ops.assign(var[index], value))
      valnp = np.copy(self.x_np)
      valnp[index] = np.array(value)
      self.test.assertAllEqual(val, valnp)
      self.test.assertAllEqual(val_copy, valnp)

  def _testIntegrated(self, batch_size, model_dim, num_timesteps, ff_fun, sim_fun):
    """
    Test the simplest possible transition sequence on a batch of random inputs.
    """

    tf.reset_default_graph()

    embedding_dim = model_dim
    num_tokens = (num_timesteps + 1) / 2

    with self.test_session(use_gpu=self.use_gpu) as s:
      stack = Variable(np.zeros((batch_size * num_timesteps, model_dim), dtype=np.float32), name="stack")
      buffer = Variable(np.random.random((batch_size * num_tokens, embedding_dim)).astype(np.float32), name="buffer")
      queue = Variable(np.zeros((batch_size * num_timesteps,), dtype=np.float32), name="queue")
      cursors = Variable(np.zeros((batch_size,), dtype=np.float32) - 1., name="cursors")
      buffer_cursors = Variable(np.zeros((batch_size,), dtype=np.float32), name="buffer_cursors")

      ######## Fprop test.
      top = ff_fun(batch_size, stack, buffer, queue, cursors, buffer_cursors)
      top_sim = sim_fun(buffer)

      s.run(initialize_variables(tf.all_variables()))

      ######## Bprop test.
      # Get some scalar error signal for grad calculation
      top, top_sim = tf.reduce_sum(top), tf.reduce_sum(top_sim)
      with tf.control_dependencies([top]):
        grad = tf.gradients(top, buffer)[0]
      grad_sim = tf.gradients(top_sim, buffer)[0]

      ######## Run fetches.
      ret = s.run([top, top_sim, grad, grad_sim])
      top_, top_sim_, grad_, grad_sim_ = ret[:4]

    self.assertAllClose(top_, top_sim_)
    self.assertAllClose(grad_, grad_sim_)

  def testIntermediateLookupGrad(self):
    """
    Test the gradient of a standard lookup somewhere in the middle of a stack
    recurrence.
    """

    batch_size = 2
    model_dim = 5
    embedding_dim = 5
    num_timesteps = 5

    num_tokens = (num_timesteps + 1) / 2

    with self.test_session(use_gpu=self.use_gpu) as s:
      # Example 1: S S R S
      # Example 2: S S S R
      #                  ^
      # we are running lookup at the above timestep
      stack = Variable([[-1., -1., -1., -1., -1.],
                        [ 1.,  1.,  1.,  1.,  1.],
                        [-2., -2., -2., -2., -2.],
                        [ 2.,  2.,  2.,  2.,  2.],
                        [-3., -3., -3., -3., -3.],
                        [ 3.,  3.,  3.,  3.,  3.],
                        [ 0.,  0.,  0.,  0.,  0.],
                        [ 0.,  0.,  0.,  0.,  0.],
                        [ 0.,  0.,  0.,  0.,  0.],
                        [ 0.,  0.,  0.,  0.,  0.]])
      buffer = Variable([[-1., -1., -1., -1., -1.],
                         [ 1.,  1.,  1.,  1.,  1.],
                         [-2., -2., -2., -2., -2.],
                         [ 2.,  2.,  2.,  2.,  2.],
                         [-3., -3., -3., -3., -3.],
                         [ 3.,  3.,  3.,  3.,  3.]])
      queue = Variable([2., 0.,
                        0., 1.,
                        0., 2.,
                        0., 0.,
                        0., 0.])
      cursors = Variable([0., 2.])
      buffer_cursors = Variable([2., 3.])

      s.run(initialize_variables([stack, buffer, queue, cursors, buffer_cursors]))

      stack_val = stack.eval()
      buffer_val = buffer.eval()

      lookup = ts.thin_stack_lookup(stack, buffer, queue, cursors, buffer_cursors, timestep=3)

      #### GRADIENT

      stack1_grad = tf.random_uniform((batch_size, model_dim))
      stack2_grad = tf.random_uniform((batch_size, model_dim))
      buf_top_grad = tf.random_uniform((batch_size, model_dim))
      in_grads = (stack1_grad, stack2_grad, buf_top_grad, None)

      # HACK: Zero out stack and buffer before invoking this op.
      # In a real / full bprop, things would have been zeroed out
      # at the start of the bprop algorithm.
      zero_stack = tf.assign(stack, stack * 0.)
      zero_buffer = tf.assign(buffer, buffer * 0.)

      # Enforce computation order: lookup, then zero out, then grad
      with tf.control_dependencies(lookup + (zero_stack, zero_buffer)):
        out_grads = ts._thin_stack_lookup_gradient(lookup[0].op, in_grads)
      out_grads = out_grads[:2]

      fetch = out_grads + (stack1_grad, stack2_grad, buf_top_grad)

      ret = s.run(fetch)

    grad_stack, grad_buffer, stack1_grad, stack2_grad, buf_top_grad = ret

    grad_stack_expected = np.zeros_like(stack_val)

  def testIntermediateUpdate(self):
    """Test a standard update somewhere in the middle of a stack recurrence."""
    batch_size = 2
    model_dim = 5
    embedding_dim = 5
    num_timesteps = 5

    num_tokens = (num_timesteps + 1) / 2

    with self.test_session(use_gpu=self.use_gpu) as s:
      # Example 1: S S R S
      # Example 2: S S S R
      #                  ^
      # we are running lookup at the above timestep

      stack = Variable([[-1., -1., -1., -1., -1.],
                        [ 1.,  1.,  1.,  1.,  1.],
                        [-2., -2., -2., -2., -2.],
                        [ 2.,  2.,  2.,  2.,  2.],
                        [-3., -3., -3., -3., -3.],
                        [ 3.,  3.,  3.,  3.,  3.],
                        [ 0.,  0.,  0.,  0.,  0.],
                        [ 0.,  0.,  0.,  0.,  0.],
                        [ 0.,  0.,  0.,  0.,  0.],
                        [ 0.,  0.,  0.,  0.,  0.]])
      buffer = Variable([[-1., -1., -1., -1., -1.],
                         [ 1.,  1.,  1.,  1.,  1.],
                         [-2., -2., -2., -2., -2.],
                         [ 2.,  2.,  2.,  2.,  2.],
                         [-3., -3., -3., -3., -3.],
                         [ 3.,  3.,  3.,  3.,  3.]])
      queue = Variable([2., 0.,
                        0., 1.,
                        0., 2.,
                        0., 0.,
                        0., 0.])
      cursors = Variable([0., 2.])
      buffer_cursors = constant_op.constant([2., 3.])
      t = 3

      s.run(initialize_variables([stack, buffer, queue, cursors]))

      stack_val = stack.eval()
      buffer_val = buffer.eval()

      shift_in = constant_op.constant(np.array([buffer_val[4], buffer_val[5]]))
      reduce_in = constant_op.constant(np.array([stack_val[4] + stack_val[0],
                                                 stack_val[5] + stack_val[3]]))
      transitions = tf.expand_dims(constant_op.constant([0., 1.]), 1)
      input_val = transitions * reduce_in + (1. - transitions) * shift_in

      ret = ts.thin_stack_update(input_val, transitions,
                                 stack, queue, cursors, buffer_cursors, t)
      stack_next, queue_next, cursors_next, buffer_cursors_next = s.run(ret)

    stack_expected = np.copy(stack_val)
    stack_expected[6] = buffer_val[4]
    stack_expected[7] = stack_val[5] + stack_val[3]

    queue_expected = np.array([2., 0.,
                               3., 3.,
                               0., 2., # NB: we didn't erase this, but it's okay
                               0., 0.,
                               0., 0.])
    cursors_expected = np.array([1., 1.])
    buffer_cursors_expected = np.array([3., 3.])

    self.assertAllEqual(stack_next, stack_expected)
    self.assertAllEqual(queue_next, queue_expected)
    self.assertAllEqual(cursors_next, cursors_expected)
    self.assertAllEqual(buffer_cursors_next, buffer_cursors_expected)