Python utils.logits_to_log_prob() Examples

The following are 9 code examples of utils.logits_to_log_prob(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module utils , or try the search function .
Example #1
Source File: model.py    From DOTA_models with Apache License 2.0 6 votes vote down vote up 
def char_predictions(self, chars_logit):
    """Returns confidence scores (softmax values) for predicted characters.

    Args:
      chars_logit: chars logits, a tensor with shape
        [batch_size x seq_length x num_char_classes]

    Returns:
      A tuple (ids, log_prob, scores), where:
        ids - predicted characters, a int32 tensor with shape
          [batch_size x seq_length];
        log_prob - a log probability of all characters, a float tensor with
          shape [batch_size, seq_length, num_char_classes];
        scores - corresponding confidence scores for characters, a float
        tensor
          with shape [batch_size x seq_length].
    """
    log_prob = utils.logits_to_log_prob(chars_logit)
    ids = tf.to_int32(tf.argmax(log_prob, dimension=2), name='predicted_chars')
    mask = tf.cast(
        slim.one_hot_encoding(ids, self._params.num_char_classes), tf.bool)
    all_scores = tf.nn.softmax(chars_logit)
    selected_scores = tf.boolean_mask(all_scores, mask, name='char_scores')
    scores = tf.reshape(selected_scores, shape=(-1, self._params.seq_length))
    return ids, log_prob, scores
Example #2
Source File: model.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def char_predictions(self, chars_logit):
    """Returns confidence scores (softmax values) for predicted characters.

    Args:
      chars_logit: chars logits, a tensor with shape
        [batch_size x seq_length x num_char_classes]

    Returns:
      A tuple (ids, log_prob, scores), where:
        ids - predicted characters, a int32 tensor with shape
          [batch_size x seq_length];
        log_prob - a log probability of all characters, a float tensor with
          shape [batch_size, seq_length, num_char_classes];
        scores - corresponding confidence scores for characters, a float
        tensor
          with shape [batch_size x seq_length].
    """
    log_prob = utils.logits_to_log_prob(chars_logit)
    ids = tf.to_int32(tf.argmax(log_prob, axis=2), name='predicted_chars')
    mask = tf.cast(
      slim.one_hot_encoding(ids, self._params.num_char_classes), tf.bool)
    all_scores = tf.nn.softmax(chars_logit)
    selected_scores = tf.boolean_mask(all_scores, mask, name='char_scores')
    scores = tf.reshape(selected_scores, shape=(-1, self._params.seq_length))
    return ids, log_prob, scores
Example #3
Source File: model.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def char_predictions(self, chars_logit):
    """Returns confidence scores (softmax values) for predicted characters.

    Args:
      chars_logit: chars logits, a tensor with shape
        [batch_size x seq_length x num_char_classes]

    Returns:
      A tuple (ids, log_prob, scores), where:
        ids - predicted characters, a int32 tensor with shape
          [batch_size x seq_length];
        log_prob - a log probability of all characters, a float tensor with
          shape [batch_size, seq_length, num_char_classes];
        scores - corresponding confidence scores for characters, a float
        tensor
          with shape [batch_size x seq_length].
    """
    log_prob = utils.logits_to_log_prob(chars_logit)
    ids = tf.to_int32(tf.argmax(log_prob, axis=2), name='predicted_chars')
    mask = tf.cast(
      slim.one_hot_encoding(ids, self._params.num_char_classes), tf.bool)
    all_scores = tf.nn.softmax(chars_logit)
    selected_scores = tf.boolean_mask(all_scores, mask, name='char_scores')
    scores = tf.reshape(selected_scores, shape=(-1, self._params.seq_length))
    return ids, log_prob, scores
Example #4
Source File: model.py    From hands-detection with MIT License 6 votes vote down vote up 
def char_predictions(self, chars_logit):
    """Returns confidence scores (softmax values) for predicted characters.

    Args:
      chars_logit: chars logits, a tensor with shape
        [batch_size x seq_length x num_char_classes]

    Returns:
      A tuple (ids, log_prob, scores), where:
        ids - predicted characters, a int32 tensor with shape
          [batch_size x seq_length];
        log_prob - a log probability of all characters, a float tensor with
          shape [batch_size, seq_length, num_char_classes];
        scores - corresponding confidence scores for characters, a float
        tensor
          with shape [batch_size x seq_length].
    """
    log_prob = utils.logits_to_log_prob(chars_logit)
    ids = tf.to_int32(tf.argmax(log_prob, dimension=2), name='predicted_chars')
    mask = tf.cast(
        slim.one_hot_encoding(ids, self._params.num_char_classes), tf.bool)
    all_scores = tf.nn.softmax(chars_logit)
    selected_scores = tf.boolean_mask(all_scores, mask, name='char_scores')
    scores = tf.reshape(selected_scores, shape=(-1, self._params.seq_length))
    return ids, log_prob, scores
Example #5
Source File: model.py    From object_detection_kitti with Apache License 2.0 6 votes vote down vote up 
def char_predictions(self, chars_logit):
    """Returns confidence scores (softmax values) for predicted characters.

    Args:
      chars_logit: chars logits, a tensor with shape
        [batch_size x seq_length x num_char_classes]

    Returns:
      A tuple (ids, log_prob, scores), where:
        ids - predicted characters, a int32 tensor with shape
          [batch_size x seq_length];
        log_prob - a log probability of all characters, a float tensor with
          shape [batch_size, seq_length, num_char_classes];
        scores - corresponding confidence scores for characters, a float
        tensor
          with shape [batch_size x seq_length].
    """
    log_prob = utils.logits_to_log_prob(chars_logit)
    ids = tf.to_int32(tf.argmax(log_prob, dimension=2), name='predicted_chars')
    mask = tf.cast(
        slim.one_hot_encoding(ids, self._params.num_char_classes), tf.bool)
    all_scores = tf.nn.softmax(chars_logit)
    selected_scores = tf.boolean_mask(all_scores, mask, name='char_scores')
    scores = tf.reshape(selected_scores, shape=(-1, self._params.seq_length))
    return ids, log_prob, scores
Example #6
Source File: model.py    From object_detection_with_tensorflow with MIT License 6 votes vote down vote up 
def char_predictions(self, chars_logit):
    """Returns confidence scores (softmax values) for predicted characters.

    Args:
      chars_logit: chars logits, a tensor with shape
        [batch_size x seq_length x num_char_classes]

    Returns:
      A tuple (ids, log_prob, scores), where:
        ids - predicted characters, a int32 tensor with shape
          [batch_size x seq_length];
        log_prob - a log probability of all characters, a float tensor with
          shape [batch_size, seq_length, num_char_classes];
        scores - corresponding confidence scores for characters, a float
        tensor
          with shape [batch_size x seq_length].
    """
    log_prob = utils.logits_to_log_prob(chars_logit)
    ids = tf.to_int32(tf.argmax(log_prob, axis=2), name='predicted_chars')
    mask = tf.cast(
        slim.one_hot_encoding(ids, self._params.num_char_classes), tf.bool)
    all_scores = tf.nn.softmax(chars_logit)
    selected_scores = tf.boolean_mask(all_scores, mask, name='char_scores')
    scores = tf.reshape(selected_scores, shape=(-1, self._params.seq_length))
    return ids, log_prob, scores
Example #7
Source File: model.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def char_predictions(self, chars_logit):
    """Returns confidence scores (softmax values) for predicted characters.

    Args:
      chars_logit: chars logits, a tensor with shape
        [batch_size x seq_length x num_char_classes]

    Returns:
      A tuple (ids, log_prob, scores), where:
        ids - predicted characters, a int32 tensor with shape
          [batch_size x seq_length];
        log_prob - a log probability of all characters, a float tensor with
          shape [batch_size, seq_length, num_char_classes];
        scores - corresponding confidence scores for characters, a float
        tensor
          with shape [batch_size x seq_length].
    """
    log_prob = utils.logits_to_log_prob(chars_logit)
    ids = tf.to_int32(tf.argmax(log_prob, axis=2), name='predicted_chars')
    mask = tf.cast(
      slim.one_hot_encoding(ids, self._params.num_char_classes), tf.bool)
    all_scores = tf.nn.softmax(chars_logit)
    selected_scores = tf.boolean_mask(all_scores, mask, name='char_scores')
    scores = tf.reshape(selected_scores, shape=(-1, self._params.seq_length))
    return ids, log_prob, scores
Example #8
Source File: model.py    From models with Apache License 2.0 6 votes vote down vote up 
def char_predictions(self, chars_logit):
    """Returns confidence scores (softmax values) for predicted characters.

    Args:
      chars_logit: chars logits, a tensor with shape
        [batch_size x seq_length x num_char_classes]

    Returns:
      A tuple (ids, log_prob, scores), where:
        ids - predicted characters, a int32 tensor with shape
          [batch_size x seq_length];
        log_prob - a log probability of all characters, a float tensor with
          shape [batch_size, seq_length, num_char_classes];
        scores - corresponding confidence scores for characters, a float
        tensor
          with shape [batch_size x seq_length].
    """
    log_prob = utils.logits_to_log_prob(chars_logit)
    ids = tf.to_int32(tf.argmax(log_prob, axis=2), name='predicted_chars')
    mask = tf.cast(
      slim.one_hot_encoding(ids, self._params.num_char_classes), tf.bool)
    all_scores = tf.nn.softmax(chars_logit)
    selected_scores = tf.boolean_mask(all_scores, mask, name='char_scores')
    scores = tf.reshape(selected_scores, shape=(-1, self._params.seq_length))
    return ids, log_prob, scores
Example #9
Source File: model.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def char_predictions(self, chars_logit):
    """Returns confidence scores (softmax values) for predicted characters.

    Args:
      chars_logit: chars logits, a tensor with shape
        [batch_size x seq_length x num_char_classes]

    Returns:
      A tuple (ids, log_prob, scores), where:
        ids - predicted characters, a int32 tensor with shape
          [batch_size x seq_length];
        log_prob - a log probability of all characters, a float tensor with
          shape [batch_size, seq_length, num_char_classes];
        scores - corresponding confidence scores for characters, a float
        tensor
          with shape [batch_size x seq_length].
    """
    log_prob = utils.logits_to_log_prob(chars_logit)
    ids = tf.to_int32(tf.argmax(log_prob, axis=2), name='predicted_chars')
    mask = tf.cast(
      slim.one_hot_encoding(ids, self._params.num_char_classes), tf.bool)
    all_scores = tf.nn.softmax(chars_logit)
    selected_scores = tf.boolean_mask(all_scores, mask, name='char_scores')
    scores = tf.reshape(selected_scores, shape=(-1, self._params.seq_length))
    return ids, log_prob, scores