Python math_ops.argmax方法代码示例

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import argmax [as 别名]
def sample(self, time, outputs, state, name=None):
        """Gets a sample for one step."""
        del time, state  # unused by sample_fn
        # Outputs are logits, we sample instead of argmax (greedy).
        if not isinstance(outputs, ops.Tensor):
            raise TypeError("Expected outputs to be a single Tensor, got: %s" %
                            type(outputs))
        if self._softmax_temperature is None:
            logits = outputs
        else:
            logits = outputs / self._softmax_temperature

        sample_id_sampler = categorical.Categorical(logits=logits)
        sample_ids = sample_id_sampler.sample(seed=self._seed)

        return sample_ids 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import argmax [as 别名]
def hardmax(logits, name=None):
  """Returns batched one-hot vectors.

  The depth index containing the `1` is that of the maximum logit value.

  Args:
    logits: A batch tensor of logit values.
    name: Name to use when creating ops.
  Returns:
    A batched one-hot tensor.
  """
  with ops.name_scope(name, "Hardmax", [logits]):
    logits = ops.convert_to_tensor(logits, name="logits")
    if logits.get_shape()[-1].value is not None:
      depth = logits.get_shape()[-1].value
    else:
      depth = array_ops.shape(logits)[-1]
    return array_ops.one_hot(
        math_ops.argmax(logits, -1), depth, dtype=logits.dtype) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import argmax [as 别名]
def __init__(self, embedding, start_tokens, end_token, seed=None):
    """Initializer.

    Args:
      embedding: A callable that takes a vector tensor of `ids` (argmax ids),
        or the `params` argument for `embedding_lookup`. The returned tensor
        will be passed to the decoder input.
      start_tokens: `int32` vector shaped `[batch_size]`, the start tokens.
      end_token: `int32` scalar, the token that marks end of decoding.
      seed: The sampling seed.

    Raises:
      ValueError: if `start_tokens` is not a 1D tensor or `end_token` is not a
        scalar.
    """
    super(SampleEmbeddingHelper, self).__init__(
        embedding, start_tokens, end_token)
    self._seed = seed 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import argmax [as 别名]
def testTrainEvalWithReuse(self):
    train_batch_size = 2
    eval_batch_size = 1
    train_height, train_width = 231, 231
    eval_height, eval_width = 281, 281
    num_classes = 1000
    with self.test_session():
      train_inputs = random_ops.random_uniform(
          (train_batch_size, train_height, train_width, 3))
      logits, _ = overfeat.overfeat(train_inputs)
      self.assertListEqual(logits.get_shape().as_list(),
                           [train_batch_size, num_classes])
      variable_scope.get_variable_scope().reuse_variables()
      eval_inputs = random_ops.random_uniform(
          (eval_batch_size, eval_height, eval_width, 3))
      logits, _ = overfeat.overfeat(
          eval_inputs, is_training=False, spatial_squeeze=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [eval_batch_size, 2, 2, num_classes])
      logits = math_ops.reduce_mean(logits, [1, 2])
      predictions = math_ops.argmax(logits, 1)
      self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import argmax [as 别名]
def testTrainEvalWithReuse(self):
    train_batch_size = 2
    eval_batch_size = 1
    train_height, train_width = 224, 224
    eval_height, eval_width = 300, 400
    num_classes = 1000
    with self.test_session():
      train_inputs = random_ops.random_uniform(
          (train_batch_size, train_height, train_width, 3))
      logits, _ = alexnet.alexnet_v2(train_inputs)
      self.assertListEqual(logits.get_shape().as_list(),
                           [train_batch_size, num_classes])
      variable_scope.get_variable_scope().reuse_variables()
      eval_inputs = random_ops.random_uniform(
          (eval_batch_size, eval_height, eval_width, 3))
      logits, _ = alexnet.alexnet_v2(
          eval_inputs, is_training=False, spatial_squeeze=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [eval_batch_size, 4, 7, num_classes])
      logits = math_ops.reduce_mean(logits, [1, 2])
      predictions = math_ops.argmax(logits, 1)
      self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import argmax [as 别名]
def testTrainEvalWithReuse(self):
    train_batch_size = 2
    eval_batch_size = 1
    train_height, train_width = 224, 224
    eval_height, eval_width = 256, 256
    num_classes = 1000
    with self.test_session():
      train_inputs = random_ops.random_uniform(
          (train_batch_size, train_height, train_width, 3))
      logits, _ = vgg.vgg_16(train_inputs)
      self.assertListEqual(logits.get_shape().as_list(),
                           [train_batch_size, num_classes])
      variable_scope.get_variable_scope().reuse_variables()
      eval_inputs = random_ops.random_uniform(
          (eval_batch_size, eval_height, eval_width, 3))
      logits, _ = vgg.vgg_16(
          eval_inputs, is_training=False, spatial_squeeze=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [eval_batch_size, 2, 2, num_classes])
      logits = math_ops.reduce_mean(logits, [1, 2])
      predictions = math_ops.argmax(logits, 1)
      self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import argmax [as 别名]
def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 150, 150
    num_classes = 1000

    train_inputs = random_ops.random_uniform(
        (train_batch_size, height, width, 3))
    inception_v3.inception_v3(train_inputs, num_classes)
    eval_inputs = random_ops.random_uniform((eval_batch_size, height, width, 3))
    logits, _ = inception_v3.inception_v3(
        eval_inputs, num_classes, is_training=False, reuse=True)
    predictions = math_ops.argmax(logits, 1)

    with self.test_session() as sess:
      sess.run(variables.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,)) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import argmax [as 别名]
def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 224, 224
    num_classes = 1000

    train_inputs = random_ops.random_uniform(
        (train_batch_size, height, width, 3))
    inception_v1.inception_v1(train_inputs, num_classes)
    eval_inputs = random_ops.random_uniform((eval_batch_size, height, width, 3))
    logits, _ = inception_v1.inception_v1(eval_inputs, num_classes, reuse=True)
    predictions = math_ops.argmax(logits, 1)

    with self.test_session() as sess:
      sess.run(variables.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,)) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import argmax [as 别名]
def predict(
      self, x=None, input_fn=None, axis=None, batch_size=None):
    """Returns predictions for given features.

    Args:
      x: features.
      input_fn: Input function. If set, x must be None.
      axis: Axis on which to argmax (for classification).
            Last axis is used by default.
      batch_size: Override default batch size.

    Returns:
      Numpy array of predicted classes or regression values (or an iterable of
      predictions if as_iterable is True).
    """
    predict_name = (eval_metrics.INFERENCE_PROB_NAME if self.params.regression
                    else eval_metrics.INFERENCE_PRED_NAME)
    if x is not None:
      results = self._skcompat.predict(x, batch_size=batch_size)
      return results[predict_name]
    else:
      results = self._estimator.predict(input_fn=input_fn, as_iterable=True)
      return (x[predict_name] for x in results) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import argmax [as 别名]
def __call__(self, inputs, state, scope=None):
    """Build the CrfDecodeForwardRnnCell.

    Args:
      inputs: A [batch_size, num_tags] matrix of unary potentials.
      state: A [batch_size, num_tags] matrix containing the previous step's
            score values.
      scope: Unused variable scope of this cell.

    Returns:
      backpointers: [batch_size, num_tags], containing backpointers.
      new_state: [batch_size, num_tags], containing new score values.
    """
    # For simplicity, in shape comments, denote:
    # 'batch_size' by 'B', 'max_seq_len' by 'T' , 'num_tags' by 'O' (output).
    state = array_ops.expand_dims(state, 2)                         # [B, O, 1]

    # This addition op broadcasts self._transitions_params along the zeroth
    # dimension and state along the second dimension.
    # [B, O, 1] + [1, O, O] -> [B, O, O]
    transition_scores = state + self._transition_params             # [B, O, O]
    new_state = inputs + math_ops.reduce_max(transition_scores, [1])  # [B, O]
    backpointers = math_ops.argmax(transition_scores, 1)
    backpointers = math_ops.cast(backpointers, dtype=dtypes.int32)    # [B, O]
    return backpointers, new_state 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import argmax [as 别名]
def testTrainEvalWithReuse(self):
    train_batch_size = 2
    eval_batch_size = 1
    train_height, train_width = 231, 231
    eval_height, eval_width = 281, 281
    num_classes = 1000
    with self.cached_session():
      train_inputs = random_ops.random_uniform(
          (train_batch_size, train_height, train_width, 3))
      logits, _ = overfeat.overfeat(train_inputs)
      self.assertListEqual(logits.get_shape().as_list(),
                           [train_batch_size, num_classes])
      variable_scope.get_variable_scope().reuse_variables()
      eval_inputs = random_ops.random_uniform(
          (eval_batch_size, eval_height, eval_width, 3))
      logits, _ = overfeat.overfeat(
          eval_inputs, is_training=False, spatial_squeeze=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [eval_batch_size, 2, 2, num_classes])
      logits = math_ops.reduce_mean(logits, [1, 2])
      predictions = math_ops.argmax(logits, 1)
      self.assertEqual(predictions.get_shape().as_list(), [eval_batch_size]) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import argmax [as 别名]
def testTrainEvalWithReuse(self):
    train_batch_size = 2
    eval_batch_size = 1
    train_height, train_width = 224, 224
    eval_height, eval_width = 300, 400
    num_classes = 1000
    with self.cached_session():
      train_inputs = random_ops.random_uniform(
          (train_batch_size, train_height, train_width, 3))
      logits, _ = alexnet.alexnet_v2(train_inputs)
      self.assertListEqual(logits.get_shape().as_list(),
                           [train_batch_size, num_classes])
      variable_scope.get_variable_scope().reuse_variables()
      eval_inputs = random_ops.random_uniform(
          (eval_batch_size, eval_height, eval_width, 3))
      logits, _ = alexnet.alexnet_v2(
          eval_inputs, is_training=False, spatial_squeeze=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [eval_batch_size, 4, 7, num_classes])
      logits = math_ops.reduce_mean(logits, [1, 2])
      predictions = math_ops.argmax(logits, 1)
      self.assertEqual(predictions.get_shape().as_list(), [eval_batch_size]) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import argmax [as 别名]
def testTrainEvalWithReuse(self):
    train_batch_size = 2
    eval_batch_size = 1
    train_height, train_width = 224, 224
    eval_height, eval_width = 256, 256
    num_classes = 1000
    with self.cached_session():
      train_inputs = random_ops.random_uniform(
          (train_batch_size, train_height, train_width, 3))
      logits, _ = vgg.vgg_16(train_inputs)
      self.assertListEqual(logits.get_shape().as_list(),
                           [train_batch_size, num_classes])
      variable_scope.get_variable_scope().reuse_variables()
      eval_inputs = random_ops.random_uniform(
          (eval_batch_size, eval_height, eval_width, 3))
      logits, _ = vgg.vgg_16(
          eval_inputs, is_training=False, spatial_squeeze=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [eval_batch_size, 2, 2, num_classes])
      logits = math_ops.reduce_mean(logits, [1, 2])
      predictions = math_ops.argmax(logits, 1)
      self.assertEqual(predictions.get_shape().as_list(), [eval_batch_size]) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import argmax [as 别名]
def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 150, 150
    num_classes = 1000

    train_inputs = random_ops.random_uniform(
        (train_batch_size, height, width, 3))
    inception_v3.inception_v3(train_inputs, num_classes)
    eval_inputs = random_ops.random_uniform((eval_batch_size, height, width, 3))
    logits, _ = inception_v3.inception_v3(
        eval_inputs, num_classes, is_training=False, reuse=True)
    predictions = math_ops.argmax(logits, 1)

    with self.cached_session() as sess:
      sess.run(variables.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEqual(output.shape, (eval_batch_size,)) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import argmax [as 别名]
def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 224, 224
    num_classes = 1000

    train_inputs = random_ops.random_uniform(
        (train_batch_size, height, width, 3))
    inception_v1.inception_v1(train_inputs, num_classes)
    eval_inputs = random_ops.random_uniform((eval_batch_size, height, width, 3))
    logits, _ = inception_v1.inception_v1(eval_inputs, num_classes, reuse=True)
    predictions = math_ops.argmax(logits, 1)

    with self.cached_session() as sess:
      sess.run(variables.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEqual(output.shape, (eval_batch_size,))