Python random_seed.set_random_seed函数代码示例

 def testNegativeMinLogits(self):
   random_seed.set_random_seed(78844)
   with self.test_session(use_gpu=True):
     logits = constant_op.constant([[np.finfo(np.float32).min] * 1023 + [0]])
     num_samples = 1000
     samples = random_ops.multinomial(logits, num_samples).eval()
     self.assertAllEqual([[1023] * num_samples], samples)

  def testNoneGlobalStep(self):
    with ops.Graph().as_default():
      random_seed.set_random_seed(0)
      tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
      tf_labels = constant_op.constant(self._labels, dtype=dtypes.float32)

      tf_predictions = batchnorm_classifier(tf_inputs)
      loss_ops.log_loss(tf_predictions, tf_labels)
      total_loss = loss_ops.get_total_loss()
      optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=1.0)

      train_op = training.create_train_op(
          total_loss, optimizer, global_step=None)

      global_step = variables_lib.get_or_create_global_step()

      with session_lib.Session() as sess:
        # Initialize all variables
        sess.run(variables_lib2.global_variables_initializer())

        for _ in range(10):
          sess.run([train_op])
        global_step = global_step.eval()
        # Since train_op don't use global_step it shouldn't change.
        self.assertAllClose(global_step, 0)

  def _infer_model(
      self, input_fn, feed_fn=None, outputs=None, as_iterable=False):
    # Check that model has been trained.
    checkpoint_path = saver.latest_checkpoint(self._model_dir)
    if not checkpoint_path:
      raise NotFittedError("Couldn't find trained model at %s."
                           % self._model_dir)

    with ops.Graph().as_default() as g:
      random_seed.set_random_seed(self._config.tf_random_seed)
      contrib_framework.create_global_step(g)
      features = self._get_features_from_input_fn(input_fn)
      predictions = self._get_predict_ops(features)
      # If predictions is single output - wrap it into dict, and remember to
      # return not a dict.
      return_dict = isinstance(predictions, dict)
      if not return_dict:
        predictions = {'predictions': predictions}

      # Filter what to run predictions on, if outputs provided.
      if outputs:
        existing_keys = predictions.keys()
        predictions = {
            key: value for key, value in predictions.items() if key in outputs
        }
        if not predictions:
          raise ValueError('Expected to run at least one output from %s, '
                           'provided %s.' % (existing_keys, outputs))

      if as_iterable:
        return self._infer_model_as_iterable(
            checkpoint_path, predictions, feed_fn, return_dict)
      else:
        return self._infer_model_single(
            checkpoint_path, predictions, feed_fn, return_dict)

 def _runSamplingBenchmark(self, name, create_distribution, use_gpu,
                           num_components, batch_size, num_features,
                           sample_size):
   config = config_pb2.ConfigProto()
   config.allow_soft_placement = True
   np.random.seed(127)
   with session.Session(config=config, graph=ops.Graph()) as sess:
     random_seed.set_random_seed(0)
     with ops.device("/device:GPU:0" if use_gpu else "/cpu:0"):
       mixture = create_distribution(
           num_components=num_components,
           batch_size=batch_size,
           num_features=num_features)
       sample_op = mixture.sample(sample_size).op
       sess.run(variables.global_variables_initializer())
       reported = self.run_op_benchmark(
           sess,
           sample_op,
           min_iters=10,
           name=("%s_%s_components_%d_batch_%d_features_%d_sample_%d" %
                 (name, use_gpu, num_components, batch_size, num_features,
                  sample_size)))
       logging.vlog(2, "\t".join(["%s", "%d", "%d", "%d", "%d", "%g"]) % (
           use_gpu, num_components, batch_size, num_features, sample_size,
           reported["wall_time"]))

  def testRegisterBlocks(self):
    with ops.Graph().as_default():
      random_seed.set_random_seed(200)
      lc = layer_collection.LayerCollection()
      lc.register_fully_connected(
          array_ops.constant(1), array_ops.constant(2), array_ops.constant(3))
      lc.register_fully_connected(
          array_ops.constant(1),
          array_ops.constant(2),
          array_ops.constant(3),
          approx=layer_collection.APPROX_DIAGONAL_NAME)
      lc.register_conv2d(
          array_ops.constant(4), [1, 1, 1, 1], 'SAME',
          array_ops.ones((1, 1, 1, 1)), array_ops.constant(3))
      lc.register_conv2d(
          array_ops.constant(4), [1, 1, 1, 1],
          'SAME',
          array_ops.ones((1, 1, 1, 1)),
          array_ops.constant(3),
          approx=layer_collection.APPROX_DIAGONAL_NAME)
      lc.register_generic(
          array_ops.constant(5), 16, approx=layer_collection.APPROX_FULL_NAME)
      lc.register_generic(
          array_ops.constant(6),
          16,
          approx=layer_collection.APPROX_DIAGONAL_NAME)

      self.assertEqual(6, len(lc.get_blocks()))

  def testResumeTrainAchievesRoughlyTheSameLoss(self):
    logdir = os.path.join(
        tempfile.mkdtemp(prefix=self.get_temp_dir()), 'tmp_logs')
    number_of_steps = [300, 301, 305]

    for i in range(len(number_of_steps)):
      with ops.Graph().as_default():
        random_seed.set_random_seed(i)
        tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
        tf_labels = constant_op.constant(self._labels, dtype=dtypes.float32)

        tf_predictions = LogisticClassifier(tf_inputs)
        loss_ops.log_loss(tf_predictions, tf_labels)
        total_loss = loss_ops.get_total_loss()

        optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=1.0)

        train_op = learning.create_train_op(total_loss, optimizer)

        loss = learning.train(
            train_op,
            logdir,
            number_of_steps=number_of_steps[i],
            log_every_n_steps=10)
        self.assertIsNotNone(loss)
        self.assertLess(loss, .015)

  def testGradient(self):
    if not test.is_gpu_available(cuda_only=True):
      self.skipTest('GPU required')

    random_seed.set_random_seed(0)
    x = random_ops.truncated_normal([1, 200, 200, 3], seed=0)
    y = conv_layers.conv2d(x, 32, [3, 3])
    z = conv_layers.conv2d(y, 32, [3, 3])
    optimizer = gradient_descent.GradientDescentOptimizer(1e-4)
    loss = math_ops.reduce_mean(z)
    train_op = optimizer.minimize(loss)
    graph = ops.get_default_graph()
    graph.add_to_collection('train_op', train_op)
    meta_graph = saver_lib.export_meta_graph(graph_def=graph.as_graph_def())

    rewrite_options = rewriter_config_pb2.RewriterConfig(
        optimize_tensor_layout=True)
    optimized_graph = tf_optimizer.OptimizeGraph(rewrite_options, meta_graph)

    found = 0
    for node in optimized_graph.node:
      if node.op in ['Conv2D', 'Conv2DBackpropFilter', 'Conv2DBackpropInput']:
        found += 1
        self.assertEqual(node.attr['data_format'].s, 'NCHW')
    self.assertEqual(found, 5)

  def testConcatWithControlDependency(self):
    if test.is_gpu_available(cuda_only=True):
      random_seed.set_random_seed(0)
      x = random_ops.truncated_normal([1, 784], seed=0)
      conv = _two_layer_model(x)
      axis = constant_op.constant(3)
      var = variables.Variable(3)
      assign = state_ops.assign(var, 6)
      with ops.control_dependencies([assign]):
        concat = array_ops.concat([conv, conv], axis)
      output = array_ops.identity(concat)

      with session.Session() as sess:
        output_val_ref = sess.run(output)

      with session.Session(config=_get_config()) as sess:
        metadata = config_pb2.RunMetadata()
        output_val = sess.run(output, run_metadata=metadata)

      nodes = []
      num_transposes = 0
      for node in metadata.cost_graph.node:
        if _is_transpose(node.name):
          num_transposes += 1
        nodes.append(node.name)

      # Four transposes were initially added in the Expand phase of
      # LayoutOptimizer; two of them are cancelled out in the Collapse phase.
      expected_num_transposes = 2
      self.assertEqual(expected_num_transposes, num_transposes)
      self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)
      self._assert_trans_nchw_to_nhwc('concat-0-0', nodes)
      self.assertIn('concat-2-LayoutOptimizer', nodes)
      self.assertAllClose(output_val_ref, output_val, atol=1e-3)

  def testReverseWithConstDims(self):
    if test.is_gpu_available(cuda_only=True):
      random_seed.set_random_seed(0)
      x = random_ops.truncated_normal([1, 784], seed=0)
      conv = _two_layer_model(x)
      dims = constant_op.constant([3, 1], name='DimsConst')
      reverse = array_ops.reverse(conv, dims)
      output = array_ops.identity(reverse)

      with session.Session() as sess:
        output_val_ref = sess.run(output)

      with session.Session(config=_get_config()) as sess:
        metadata = config_pb2.RunMetadata()
        output_val = sess.run(output, run_metadata=metadata)

      nodes = []
      num_transposes = 0
      for node in metadata.cost_graph.node:
        if _is_transpose(node.name):
          num_transposes += 1
        nodes.append(node.name)

      # Four transposes were initially added in the Expand phase of
      # LayoutOptimizer; two of them are cancelled out in the Collapse phase.
      expected_num_transposes = 2
      self.assertEqual(expected_num_transposes, num_transposes)
      self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)
      self._assert_trans_nchw_to_nhwc('ReverseV2-0-0', nodes)
      self.assertIn('ReverseV2-1-LayoutOptimizer', nodes)
      self.assertAllClose(output_val_ref, output_val, atol=1e-3)

  def testSplitVWithNonConstAxis(self):
    if test.is_gpu_available(cuda_only=True):
      random_seed.set_random_seed(0)
      x = random_ops.truncated_normal([1, 784], seed=0)
      conv = _two_layer_model(x)
      dim = array_ops.placeholder(dtype='int32')
      sizes = constant_op.constant([50, 10, 4], shape=[3])
      split = gen_array_ops._split_v(
          value=conv, size_splits=sizes, axis=dim, num_split=3)
      output = math_ops.reduce_sum(split[0])

      with session.Session() as sess:
        output_val_ref = sess.run(output, feed_dict={dim: 3})

      with session.Session(config=_get_config()) as sess:
        metadata = config_pb2.RunMetadata()
        output_val = sess.run(output, run_metadata=metadata, feed_dict={dim: 3})

      nodes = []
      num_transposes = 0
      for node in metadata.cost_graph.node:
        if _is_transpose(node.name):
          num_transposes += 1
        nodes.append(node.name)

      # Four transposes were initially added in the Expand phase of
      # LayoutOptimizer; two of them are cancelled out in the Collapse phase.
      expected_num_transposes = 2
      self.assertEqual(expected_num_transposes, num_transposes)
      self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)
      self._assert_trans_nchw_to_nhwc('SplitV-0-0', nodes)
      self._assert_map_nhwc_to_nchw('SplitV-2', nodes)
      self.assertAllClose(output_val_ref, output_val, atol=1e-3)

  def testReduceSumAlongC(self):
    if test.is_gpu_available(cuda_only=True):
      random_seed.set_random_seed(0)
      x = random_ops.truncated_normal([1, 784], seed=0)
      conv = _two_layer_model(x)
      reduce_sum = math_ops.reduce_sum(conv, axis=[3])
      output = array_ops.identity(reduce_sum)

      with session.Session() as sess:
        output_val_ref = sess.run(output)

      with session.Session(config=_get_config()) as sess:
        metadata = config_pb2.RunMetadata()
        output_val = sess.run(output, run_metadata=metadata)

      nodes = []
      num_transposes = 0
      for node in metadata.cost_graph.node:
        if _is_transpose(node.name):
          num_transposes += 1
        nodes.append(node.name)

      # Three transposes were initially added in the Expand phase of
      # LayoutOptimizer; two of them are cancelled out in the Collapse phase.
      expected_num_transposes = 1
      self.assertEqual(expected_num_transposes, num_transposes)
      self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)
      self.assertAllClose(output_val_ref, output_val, atol=1e-3)

  def testSplitWithNonConstAxis(self):
    if test.is_gpu_available(cuda_only=True):
      random_seed.set_random_seed(0)
      x = random_ops.truncated_normal([1, 784], seed=0)
      conv = _two_layer_model(x)
      dim = array_ops.placeholder(dtype='int32')
      split = array_ops.split(conv, 2, axis=dim)
      scale = constant_op.constant(0.1, shape=[32])
      offset = constant_op.constant(0.3, shape=[32])
      bn0 = nn.fused_batch_norm(split[0], scale, offset)
      bn1 = nn.fused_batch_norm(split[1], scale, offset)
      add = bn0[0] + bn1[0]
      output = array_ops.identity(add)

      with session.Session() as sess:
        output_val_ref = sess.run(output, feed_dict={dim: 3})

      with session.Session(config=_get_config()) as sess:
        metadata = config_pb2.RunMetadata()
        output_val = sess.run(output, run_metadata=metadata, feed_dict={dim: 3})

      nodes = []
      num_transposes = 0
      for node in metadata.cost_graph.node:
        if _is_transpose(node.name):
          num_transposes += 1
        nodes.append(node.name)

      expected_num_transposes = 2
      self.assertEqual(expected_num_transposes, num_transposes)
      self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)
      self._assert_trans_nchw_to_nhwc('add_2-0-0', nodes)
      self._assert_map_nhwc_to_nchw('split-0', nodes)
      self.assertAllClose(output_val_ref, output_val, atol=1e-3)

def _save_first_checkpoint(keras_model, estimator, custom_objects,
                           keras_weights):
  """Save first checkpoint for the keras Estimator.

  Args:
    keras_model: an instance of compiled keras model.
    estimator: keras estimator.
    custom_objects: Dictionary for custom objects.
    keras_weights: A flat list of Numpy arrays for weights of given keras_model.

  Returns:
    The model_fn for a keras Estimator.
  """
  # Load weights and save to checkpoint if there is no checkpoint
  latest_path = saver_lib.latest_checkpoint(estimator.model_dir)
  if not latest_path:
    with ops.Graph().as_default():
      random_seed.set_random_seed(estimator.config.tf_random_seed)
      training_util.create_global_step()
      model = _clone_and_build_model(model_fn_lib.ModeKeys.TRAIN, keras_model,
                                     custom_objects)
      # save to checkpoint
      with session.Session(config=estimator._session_config) as sess:
        if keras_weights:
          model.set_weights(keras_weights)
        # Make update ops and initialize all variables.
        if not model.train_function:
          # pylint: disable=protected-access
          model._make_train_function()
          K._initialize_variables(sess)
          # pylint: enable=protected-access
        saver = saver_lib.Saver()
        saver.save(sess, os.path.join(estimator.model_dir, 'keras_model.ckpt'))

 def testAtrousFullyConvolutionalValues(self):
   """Verify dense feature extraction with atrous convolution."""
   nominal_stride = 32
   for output_stride in [4, 8, 16, 32, None]:
     with arg_scope(resnet_utils.resnet_arg_scope()):
       with ops.Graph().as_default():
         with self.test_session() as sess:
           random_seed.set_random_seed(0)
           inputs = create_test_input(2, 81, 81, 3)
           # Dense feature extraction followed by subsampling.
           output, _ = self._resnet_small(
               inputs,
               None,
               is_training=False,
               global_pool=False,
               output_stride=output_stride)
           if output_stride is None:
             factor = 1
           else:
             factor = nominal_stride // output_stride
           output = resnet_utils.subsample(output, factor)
           # Make the two networks use the same weights.
           variable_scope.get_variable_scope().reuse_variables()
           # Feature extraction at the nominal network rate.
           expected, _ = self._resnet_small(
               inputs, None, is_training=False, global_pool=False)
           sess.run(variables.global_variables_initializer())
           self.assertAllClose(
               output.eval(), expected.eval(), atol=1e-4, rtol=1e-4)

  def testTrainWithSessionWrapper(self):
    """Test that slim.learning.train can take `session_wrapper` args.

    One of the applications of `session_wrapper` is the wrappers of TensorFlow
    Debugger (tfdbg), which intercept methods calls to `tf.Session` (e.g., run)
    to achieve debugging. `DumpingDebugWrapperSession` is used here for testing
    purpose.
    """
    dump_root = tempfile.mkdtemp()

    def dumping_wrapper(sess):  # pylint: disable=invalid-name
      return dumping_wrapper_lib.DumpingDebugWrapperSession(sess, dump_root)

    with ops.Graph().as_default():
      random_seed.set_random_seed(0)
      tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
      tf_labels = constant_op.constant(self._labels, dtype=dtypes.float32)

      tf_predictions = LogisticClassifier(tf_inputs)
      loss_ops.log_loss(tf_predictions, tf_labels)
      total_loss = loss_ops.get_total_loss()

      optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=1.0)

      train_op = learning.create_train_op(total_loss, optimizer)

      loss = learning.train(
          train_op, None, number_of_steps=1, session_wrapper=dumping_wrapper)
    self.assertIsNotNone(loss)

    run_root = glob.glob(os.path.join(dump_root, 'run_*'))[-1]
    dump = debug_data.DebugDumpDir(run_root)
    self.assertAllEqual(0,
                        dump.get_tensors('global_step', 0, 'DebugIdentity')[0])