Python tensorflow.python.summary.summary.merge_all() Examples

def testTrainWithNoneAsLogdirWhenUsingSummariesRaisesError(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 = LogisticClassifier(tf_inputs)
      loss_ops.log_loss(tf_predictions, tf_labels)
      total_loss = loss_ops.get_total_loss()
      summary.scalar('total_loss', total_loss)

      optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=1.0)

      train_op = learning.create_train_op(total_loss, optimizer)
      summary_op = summary.merge_all()

      with self.assertRaises(ValueError):
        learning.train(
            train_op, None, number_of_steps=300, summary_op=summary_op) 

def verify_scalar_summary_is_written(self, print_summary):
    value = 3
    tensor = array_ops.ones([]) * value
    name = 'my_score'
    prefix = 'eval'
    summaries.add_scalar_summary(tensor, name, prefix, print_summary)

    output_dir = tempfile.mkdtemp('scalar_summary_no_print_test')
    summary_op = summary.merge_all()

    summary_writer = summary.FileWriter(output_dir)
    with self.cached_session() as sess:
      new_summary = sess.run(summary_op)
      summary_writer.add_summary(new_summary, 1)
      summary_writer.flush()

    self.assert_scalar_summary(output_dir, {
        '%s/%s' % (prefix, name): value
    }) 

def testTrainWithNoneAsLogdirWhenUsingSummariesRaisesError(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 = LogisticClassifier(tf_inputs)
      loss_ops.log_loss(tf_predictions, tf_labels)
      total_loss = loss_ops.get_total_loss()
      summary.scalar('total_loss', total_loss)

      optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=1.0)

      train_op = learning.create_train_op(total_loss, optimizer)
      summary_op = summary.merge_all()

      with self.assertRaises(ValueError):
        learning.train(
            train_op, None, number_of_steps=300, summary_op=summary_op) 

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

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

      optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=1.0)

      train_op = learning.create_train_op(total_loss, optimizer)
      summary_op = summary.merge_all()

      with self.assertRaises(ValueError):
        learning.train(
            train_op, None, number_of_steps=300, summary_op=summary_op) 

def begin(self):
    if self._summary_op is None:
      self._summary_op = summary.merge_all() 

def _init_summary_op(self, summary_op=USE_DEFAULT):
    """Initializes summary_op.

    Args:
      summary_op: An Operation that returns a Summary for the event logs.
        If set to USE_DEFAULT, create an op that merges all the summaries.
    """
    if summary_op is Supervisor.USE_DEFAULT:
      summary_op = self._get_first_op_from_collection(ops.GraphKeys.SUMMARY_OP)
      if summary_op is None:
        summary_op = _summary.merge_all()
        if summary_op is not None:
          ops.add_to_collection(ops.GraphKeys.SUMMARY_OP, summary_op)
    self._summary_op = summary_op 

def _init_summary_op(self, summary_op=USE_DEFAULT):
    """Initializes summary_op.

    Args:
      summary_op: An Operation that returns a Summary for the event logs.
        If set to USE_DEFAULT, create an op that merges all the summaries.
    """
    if summary_op is Supervisor.USE_DEFAULT:
      summary_op = self._get_first_op_from_collection(ops.GraphKeys.SUMMARY_OP)
      if summary_op is None:
        summary_op = _summary.merge_all()
        if summary_op is not None:
          ops.add_to_collection(ops.GraphKeys.SUMMARY_OP, summary_op)
    self._summary_op = summary_op 

def _init_summary_op(self, summary_op=USE_DEFAULT):
    """Initializes summary_op.

    Args:
      summary_op: An Operation that returns a Summary for the event logs.
        If set to USE_DEFAULT, create an op that merges all the summaries.
    """
    if summary_op is Supervisor.USE_DEFAULT:
      summary_op = self._get_first_op_from_collection(ops.GraphKeys.SUMMARY_OP)
      if summary_op is None:
        summary_op = _summary.merge_all()
        if summary_op is not None:
          ops.add_to_collection(ops.GraphKeys.SUMMARY_OP, summary_op)
    self._summary_op = summary_op 

def begin(self):
    if self._replace_summary_op:
      # This can still remain None if there are no summaries.
      self._summary_op = summary.merge_all()
    self._global_step = training_util.get_or_create_global_step() 

def begin(self):
    if self._summary_op is None:
      self._summary_op = summary.merge_all() 

def _init_summary_op(self, summary_op=USE_DEFAULT):
    """Initializes summary_op.

    Args:
      summary_op: An Operation that returns a Summary for the event logs.
        If set to USE_DEFAULT, create an op that merges all the summaries.
    """
    if summary_op is Supervisor.USE_DEFAULT:
      summary_op = self._get_first_op_from_collection(ops.GraphKeys.SUMMARY_OP)
      if summary_op is None:
        summary_op = _summary.merge_all()
        if summary_op is not None:
          ops.add_to_collection(ops.GraphKeys.SUMMARY_OP, summary_op)
    self._summary_op = summary_op 

def begin(self):
    if self._replace_summary_op:
      self._summary_op = summary.merge_all()
    self._global_step = variables.get_or_create_global_step() 

def _init_summary_op(self, summary_op=USE_DEFAULT):
    """Initializes summary_op.

    Args:
      summary_op: An Operation that returns a Summary for the event logs.
        If set to USE_DEFAULT, create an op that merges all the summaries.
    """
    if summary_op is Supervisor.USE_DEFAULT:
      summary_op = self._get_first_op_from_collection(ops.GraphKeys.SUMMARY_OP)
      if summary_op is None:
        summary_op = _summary.merge_all()
        if summary_op is not None:
          ops.add_to_collection(ops.GraphKeys.SUMMARY_OP, summary_op)
    self._summary_op = summary_op 

def set_model(self, model):
    self.model = model
    self.sess = K.get_session()
    if self.histogram_freq and self.merged is None:
      for layer in self.model.layers:
        for weight in layer.weights:
          mapped_weight_name = weight.name.replace(':', '_')
          tf_summary.histogram(mapped_weight_name, weight)
          if self.write_grads:
            grads = model.optimizer.get_gradients(model.total_loss, weight)

            def is_indexed_slices(grad):
              return type(grad).__name__ == 'IndexedSlices'

            grads = [grad.values if is_indexed_slices(grad) else grad
                     for grad in grads]
            tf_summary.histogram('{}_grad'.format(mapped_weight_name), grads)
          if self.write_images:
            w_img = array_ops.squeeze(weight)
            shape = K.int_shape(w_img)
            if len(shape) == 2:  # dense layer kernel case
              if shape[0] > shape[1]:
                w_img = array_ops.transpose(w_img)
                shape = K.int_shape(w_img)
              w_img = array_ops.reshape(w_img, [1, shape[0], shape[1], 1])
            elif len(shape) == 3:  # convnet case
              if K.image_data_format() == 'channels_last':
                # switch to channels_first to display
                # every kernel as a separate image
                w_img = array_ops.transpose(w_img, perm=[2, 0, 1])
                shape = K.int_shape(w_img)
              w_img = array_ops.reshape(w_img,
                                        [shape[0], shape[1], shape[2], 1])
            elif len(shape) == 1:  # bias case
              w_img = array_ops.reshape(w_img, [1, shape[0], 1, 1])
            else:
              # not possible to handle 3D convnets etc.
              continue

            shape = K.int_shape(w_img)
            assert len(shape) == 4 and shape[-1] in [1, 3, 4]
            tf_summary.image(mapped_weight_name, w_img)

        if hasattr(layer, 'output'):
          tf_summary.histogram('{}_out'.format(layer.name), layer.output)
    self.merged = tf_summary.merge_all()

    if self.write_graph:
      self.writer = tf_summary.FileWriter(self.log_dir, self.sess.graph)
    else:
      self.writer = tf_summary.FileWriter(self.log_dir) 

def finalize(self):
    """Creates operations if needed and finalizes the graph."""
    if self._init_op is None:
      def default_init_op():
        return control_flow_ops.group(
            variables.global_variables_initializer(),
            resources.initialize_resources(resources.shared_resources()))
      self._init_op = Scaffold.get_or_default(
          'init_op',
          ops.GraphKeys.INIT_OP,
          default_init_op)
    if self._ready_op is None:
      def default_ready_op():
        return array_ops.concat([
            variables.report_uninitialized_variables(),
            resources.report_uninitialized_resources()
        ], 0)
      self._ready_op = Scaffold.get_or_default(
          'ready_op', ops.GraphKeys.READY_OP,
          default_ready_op)
    if self._ready_for_local_init_op is None:
      def default_ready_for_local_init_op():
        return variables.report_uninitialized_variables(
            variables.global_variables())
      self._ready_for_local_init_op = Scaffold.get_or_default(
          'ready_for_local_init_op', ops.GraphKeys.READY_FOR_LOCAL_INIT_OP,
          default_ready_for_local_init_op)
    if self._local_init_op is None:
      self._local_init_op = Scaffold.get_or_default(
          'local_init_op', ops.GraphKeys.LOCAL_INIT_OP,
          Scaffold._default_local_init_op)
    if self._summary_op is None:
      self._summary_op = Scaffold.get_or_default('summary_op',
                                                 ops.GraphKeys.SUMMARY_OP,
                                                 summary.merge_all)
    # pylint: disable=g-long-lambda
    if self._saver is None:
      self._saver = training_saver._get_saver_or_default()  # pylint: disable=protected-access
    # pylint: enable=g-long-lambda
    self._saver.build()

    ops.get_default_graph().finalize()
    return self 

def finalize(self):
    """Creates operations if needed and finalizes the graph."""
    if self._init_op is None:
      def default_init_op():
        return control_flow_ops.group(
            variables.global_variables_initializer(),
            resources.initialize_resources(resources.shared_resources()))
      self._init_op = Scaffold.get_or_default(
          'init_op',
          ops.GraphKeys.INIT_OP,
          default_init_op)
    if self._ready_op is None:
      def default_ready_op():
        return array_ops.concat([
            variables.report_uninitialized_variables(),
            resources.report_uninitialized_resources()
        ], 0)
      self._ready_op = Scaffold.get_or_default(
          'ready_op', ops.GraphKeys.READY_OP,
          default_ready_op)
    if self._ready_for_local_init_op is None:
      def default_ready_for_local_init_op():
        return variables.report_uninitialized_variables(
            variables.global_variables())
      self._ready_for_local_init_op = Scaffold.get_or_default(
          'ready_for_local_init_op', ops.GraphKeys.READY_FOR_LOCAL_INIT_OP,
          default_ready_for_local_init_op)
    if self._local_init_op is None:
      self._local_init_op = Scaffold.get_or_default(
          'local_init_op', ops.GraphKeys.LOCAL_INIT_OP,
          Scaffold._default_local_init_op)
    if self._summary_op is None:
      self._summary_op = Scaffold.get_or_default('summary_op',
                                                 ops.GraphKeys.SUMMARY_OP,
                                                 summary.merge_all)
    # pylint: disable=g-long-lambda
    if self._saver is None:
      self._saver = Scaffold.get_or_default(
          'saver',
          ops.GraphKeys.SAVERS,
          lambda: training_saver.Saver(sharded=True, allow_empty=True,
                                       write_version=saver_pb2.SaverDef.V2))
    # pylint: enable=g-long-lambda
    self._saver.build()

    ops.get_default_graph().finalize()
    return self 

def finalize(self):
    """Creates operations if needed and finalizes the graph."""
    if self._init_op is None:
      def default_init_op():
        return control_flow_ops.group(
            variables.global_variables_initializer(),
            resources.initialize_resources(resources.shared_resources()))
      self._init_op = Scaffold.get_or_default(
          'init_op',
          ops.GraphKeys.INIT_OP,
          default_init_op)
    if self._ready_op is None:
      def default_ready_op():
        return array_ops.concat([
            variables.report_uninitialized_variables(),
            resources.report_uninitialized_resources()
        ], 0)
      self._ready_op = Scaffold.get_or_default(
          'ready_op', ops.GraphKeys.READY_OP,
          default_ready_op)
    if self._ready_for_local_init_op is None:
      def default_ready_for_local_init_op():
        return variables.report_uninitialized_variables(
            variables.global_variables())
      self._ready_for_local_init_op = Scaffold.get_or_default(
          'ready_for_local_init_op', ops.GraphKeys.READY_FOR_LOCAL_INIT_OP,
          default_ready_for_local_init_op)
    if self._local_init_op is None:
      self._local_init_op = Scaffold.get_or_default(
          'local_init_op', ops.GraphKeys.LOCAL_INIT_OP,
          Scaffold._default_local_init_op)
    if self._summary_op is None:
      self._summary_op = Scaffold.get_or_default('summary_op',
                                                 ops.GraphKeys.SUMMARY_OP,
                                                 summary.merge_all)
    # pylint: disable=g-long-lambda
    if self._saver is None:
      self._saver = Scaffold.get_or_default(
          'saver',
          ops.GraphKeys.SAVERS,
          lambda: training_saver.Saver(sharded=True, allow_empty=True,
                                       write_version=saver_pb2.SaverDef.V2))
    # pylint: enable=g-long-lambda
    self._saver.build()

    ops.get_default_graph().finalize()
    return self 

def finalize(self):
    """Creates operations if needed and finalizes the graph."""
    if self._init_op is None:
      def default_init_op():
        return control_flow_ops.group(
            variables.global_variables_initializer(),
            resources.initialize_resources(resources.shared_resources()))
      self._init_op = Scaffold.get_or_default(
          'init_op',
          ops.GraphKeys.INIT_OP,
          default_init_op)
    if self._ready_op is None:
      def default_ready_op():
        return array_ops.concat([
            variables.report_uninitialized_variables(),
            resources.report_uninitialized_resources()
        ], 0)
      self._ready_op = Scaffold.get_or_default(
          'ready_op', ops.GraphKeys.READY_OP,
          default_ready_op)
    if self._ready_for_local_init_op is None:
      def default_ready_for_local_init_op():
        return variables.report_uninitialized_variables(
            variables.global_variables())
      self._ready_for_local_init_op = Scaffold.get_or_default(
          'ready_for_local_init_op', ops.GraphKeys.READY_FOR_LOCAL_INIT_OP,
          default_ready_for_local_init_op)
    if self._local_init_op is None:
      self._local_init_op = Scaffold.get_or_default(
          'local_init_op', ops.GraphKeys.LOCAL_INIT_OP,
          Scaffold._default_local_init_op)
    if self._summary_op is None:
      self._summary_op = Scaffold.get_or_default('summary_op',
                                                 ops.GraphKeys.SUMMARY_OP,
                                                 summary.merge_all)
    # pylint: disable=g-long-lambda
    if self._saver is None:
      self._saver = training_saver._get_saver_or_default()  # pylint: disable=protected-access
    # pylint: enable=g-long-lambda
    self._saver.build()

    ops.get_default_graph().finalize()
    return self