Python tensorflow.equal方法代码示例

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import equal [as 别名]
def check_tensor_shape(tensor_tf, target_shape):
    """ Return a Tensorflow boolean graph that indicates whether
    sample[features_key] has the specified target shape. Only check
    not None entries of target_shape.

    :param tensor_tf: Tensor to check shape for.
    :param target_shape: Target shape to compare tensor to.
    :returns: True if shape is valid, False otherwise (as TF boolean).
    """
    result = tf.constant(True)
    for i, target_length in enumerate(target_shape):
        if target_length:
            result = tf.logical_and(
                result,
                tf.equal(tf.constant(target_length), tf.shape(tensor_tf)[i]))
    return result 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import equal [as 别名]
def apply_with_random_selector(x, func, num_cases):
  """Computes func(x, sel), with sel sampled from [0...num_cases-1].

  Args:
    x: input Tensor.
    func: Python function to apply.
    num_cases: Python int32, number of cases to sample sel from.

  Returns:
    The result of func(x, sel), where func receives the value of the
    selector as a python integer, but sel is sampled dynamically.
  """
  sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32)
  # Pass the real x only to one of the func calls.
  return control_flow_ops.merge([
      func(control_flow_ops.switch(x, tf.equal(sel, case))[1], case)
      for case in range(num_cases)])[0] 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import equal [as 别名]
def matched_iou(boxlist1, boxlist2, scope=None):
  """Compute intersection-over-union between corresponding boxes in boxlists.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding N boxes
    scope: name scope.

  Returns:
    a tensor with shape [N] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'MatchedIOU'):
    intersections = matched_intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = areas1 + areas2 - intersections
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions)) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import equal [as 别名]
def filter_field_value_equals(boxlist, field, value, scope=None):
  """Filter to keep only boxes with field entries equal to the given value.

  Args:
    boxlist: BoxList holding N boxes.
    field: field name for filtering.
    value: scalar value.
    scope: name scope.

  Returns:
    a BoxList holding M boxes where M <= N

  Raises:
    ValueError: if boxlist not a BoxList object or if it does not have
      the specified field.
  """
  with tf.name_scope(scope, 'FilterFieldValueEquals'):
    if not isinstance(boxlist, box_list.BoxList):
      raise ValueError('boxlist must be a BoxList')
    if not boxlist.has_field(field):
      raise ValueError('boxlist must contain the specified field')
    filter_field = boxlist.get_field(field)
    gather_index = tf.reshape(tf.where(tf.equal(filter_field, value)), [-1])
    return gather(boxlist, gather_index) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import equal [as 别名]
def _apply_with_random_selector(x, func, num_cases):
  """Computes func(x, sel), with sel sampled from [0...num_cases-1].

  Args:
    x: input Tensor.
    func: Python function to apply.
    num_cases: Python int32, number of cases to sample sel from.

  Returns:
    The result of func(x, sel), where func receives the value of the
    selector as a python integer, but sel is sampled dynamically.
  """
  rand_sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32)
  # Pass the real x only to one of the func calls.
  return control_flow_ops.merge([func(
      control_flow_ops.switch(x, tf.equal(rand_sel, case))[1], case)
                                 for case in range(num_cases)])[0] 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import equal [as 别名]
def subtract_channel_mean(image, means=None):
  """Normalizes an image by subtracting a mean from each channel.

  Args:
    image: A 3D tensor of shape [height, width, channels]
    means: float list containing a mean for each channel
  Returns:
    normalized_images: a tensor of shape [height, width, channels]
  Raises:
    ValueError: if images is not a 4D tensor or if the number of means is not
      equal to the number of channels.
  """
  with tf.name_scope('SubtractChannelMean', values=[image, means]):
    if len(image.get_shape()) != 3:
      raise ValueError('Input must be of size [height, width, channels]')
    if len(means) != image.get_shape()[-1]:
      raise ValueError('len(means) must match the number of channels')
    return image - [[means]] 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import equal [as 别名]
def test_not_done_batch(self):
    step = tf.Variable(0, False, dtype=tf.int32, name='step')
    done = tf.equal([step % 3, step % 4], 0)
    score = tf.cast([step, step ** 2], tf.float32)
    loop = tools.Loop(None, step)
    loop.add_phase(
        'phase_1', done, score, summary='', steps=1, report_every=8)
    # Step:    0  2  4  6
    # Score 1: 0  2  4  6
    # Done 1:  x        x
    # Score 2: 0  4 16 32
    # Done 2:  x     x
    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      scores = list(loop.run(sess, saver=None, max_step=8))
      self.assertEqual(8, sess.run(step))
    self.assertAllEqual([(0 + 0 + 16 + 6) / 4], scores) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import equal [as 别名]
def _apply_cond(self, apply_fn, grad, var, *args, **kwargs):
    """Apply conditionally if counter is zero."""
    grad_acc = self.get_slot(var, "grad_acc")

    def apply_adam(grad_acc, apply_fn, grad, var, *args, **kwargs):
      total_grad = (grad_acc + grad) / tf.cast(self._n_t, grad.dtype)
      adam_op = apply_fn(total_grad, var, *args, **kwargs)
      with tf.control_dependencies([adam_op]):
        grad_acc_to_zero_op = grad_acc.assign(tf.zeros_like(grad_acc),
                                              use_locking=self._use_locking)
      return tf.group(adam_op, grad_acc_to_zero_op)

    def accumulate_gradient(grad_acc, grad):
      assign_op = tf.assign_add(grad_acc, grad, use_locking=self._use_locking)
      return tf.group(assign_op)  # Strip return value

    return tf.cond(
        tf.equal(self._get_iter_variable(), 0),
        lambda: apply_adam(grad_acc, apply_fn, grad, var, *args, **kwargs),
        lambda: accumulate_gradient(grad_acc, grad)) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import equal [as 别名]
def _finish(self, update_ops, name_scope):
    """Updates beta_power variables every n batches and incrs counter."""
    iter_ = self._get_iter_variable()
    beta1_power, beta2_power = self._get_beta_accumulators()
    with tf.control_dependencies(update_ops):
      with tf.colocate_with(iter_):

        def update_beta_op():
          update_beta1 = beta1_power.assign(
              beta1_power * self._beta1_t,
              use_locking=self._use_locking)
          update_beta2 = beta2_power.assign(
              beta2_power * self._beta2_t,
              use_locking=self._use_locking)
          return tf.group(update_beta1, update_beta2)
        maybe_update_beta = tf.cond(
            tf.equal(iter_, 0), update_beta_op, tf.no_op)
        with tf.control_dependencies([maybe_update_beta]):
          update_iter = iter_.assign(tf.mod(iter_ + 1, self._n_t),
                                     use_locking=self._use_locking)
    return tf.group(
        *update_ops + [update_iter, maybe_update_beta], name=name_scope) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import equal [as 别名]
def pool(inputs, window_size, pooling_type, padding, strides=(1, 1)):
  """Pooling (supports "LEFT")."""
  with tf.name_scope("pool", values=[inputs]):
    static_shape = inputs.get_shape()
    if not static_shape or len(static_shape) != 4:
      raise ValueError("Inputs to conv must have statically known rank 4.")
    # Add support for left padding.
    if padding == "LEFT":
      assert window_size[0] % 2 == 1 and window_size[1] % 2 == 1
      if len(static_shape) == 3:
        width_padding = 2 * (window_size[1] // 2)
        padding_ = [[0, 0], [width_padding, 0], [0, 0]]
      else:
        height_padding = 2 * (window_size[0] // 2)
        cond_padding = tf.cond(
            tf.equal(shape_list(inputs)[2], 1), lambda: tf.constant(0),
            lambda: tf.constant(2 * (window_size[1] // 2)))
        width_padding = 0 if static_shape[2] == 1 else cond_padding
        padding_ = [[0, 0], [height_padding, 0], [width_padding, 0], [0, 0]]
      inputs = tf.pad(inputs, padding_)
      inputs.set_shape([static_shape[0], None, None, static_shape[3]])
      padding = "VALID"

  return tf.nn.pool(inputs, window_size, pooling_type, padding, strides=strides) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import equal [as 别名]
def top_1_tpu(inputs):
  """find max and argmax over the last dimension.

  Works well on TPU

  Args:
    inputs: A tensor with shape [..., depth]

  Returns:
    values: a Tensor with shape [...]
    indices: a Tensor with shape [...]
  """
  inputs_max = tf.reduce_max(inputs, axis=-1, keepdims=True)
  mask = tf.to_int32(tf.equal(inputs_max, inputs))
  index = tf.range(tf.shape(inputs)[-1]) * mask
  return tf.squeeze(inputs_max, -1), tf.reduce_max(index, axis=-1) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import equal [as 别名]
def build_train_op(global_step, encdec_variables, discri_variables):
  """Returns the training Op.

  When global_step % 2 == 0, it minimizes l_final and updates encdec_variables.
  Otherwise, it minimizes l_d and updates discri_variables.

  Args:
    global_step: The training step.
    encdec_variables: The list of variables of the encoder/decoder model.
    discri_variables: The list of variables of the discriminator.

  Returns:
    The training op.
  """
  encdec_opt = tf.train.AdamOptimizer(learning_rate=0.0003, beta1=0.5)
  discri_opt = tf.train.RMSPropOptimizer(0.0005)
  encdec_gradients = encdec_opt.compute_gradients(l_final, var_list=encdec_variables)
  discri_gradients = discri_opt.compute_gradients(l_d, var_list=discri_variables)
  return tf.cond(
      tf.equal(tf.mod(global_step, 2), 0),
      true_fn=lambda: encdec_opt.apply_gradients(encdec_gradients, global_step=global_step),
      false_fn=lambda: discri_opt.apply_gradients(discri_gradients, global_step=global_step)) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import equal [as 别名]
def sgd(training_data, training_labels, test_data, test_labels):
    # model
    with tf.variable_scope("regression"):
        x = tf.placeholder(tf.float32, [None, 901])
        y, variables = regression(x)

    # train
    y_ = tf.placeholder("float", [None, 2])
    cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
    train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        sess.run(train_step, feed_dict={x: training_data, y_: training_labels})
        print(sess.run(accuracy, feed_dict={x: test_data, y_: test_labels})) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import equal [as 别名]
def iou(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-union between box collections.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'IOU'):
    intersections = intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = (
        tf.expand_dims(areas1, 1) + tf.expand_dims(areas2, 0) - intersections)
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions)) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import equal [as 别名]
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""

    # This is a simple heuristic which will always truncate the longer sequence
    # one token at a time. This makes more sense than truncating an equal percent
    # of tokens from each, since if one sequence is very short then each token
    # that's truncated likely contains more information than a longer sequence.
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop()
        else:
            tokens_b.pop()