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Python v1.squeeze方法代码示例

本文整理汇总了Python中tensorflow.compat.v1.squeeze方法的典型用法代码示例。如果您正苦于以下问题:Python v1.squeeze方法的具体用法?Python v1.squeeze怎么用?Python v1.squeeze使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在tensorflow.compat.v1的用法示例。


在下文中一共展示了v1.squeeze方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: two_class_log_likelihood

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import squeeze [as 别名]
def two_class_log_likelihood(predictions, labels, weights_fn=None):
  """Log-likelihood for two class classification with 0/1 labels.

  Args:
    predictions: A float valued tensor of shape [`batch_size`].  Each
      component should be between 0 and 1.
    labels: An int valued tensor of shape [`batch_size`].  Each component
      should either be 0 or 1.
    weights_fn: unused.

  Returns:
    A pair, with the average log likelihood in the first component.
  """
  del weights_fn
  float_predictions = tf.cast(tf.squeeze(predictions), dtype=tf.float64)
  batch_probs = tf.stack([1. - float_predictions, float_predictions], axis=-1)
  int_labels = tf.cast(tf.squeeze(labels), dtype=tf.int32)
  onehot_targets = tf.cast(tf.one_hot(int_labels, 2), dtype=tf.float64)
  chosen_probs = tf.einsum(
      "ij,ij->i", batch_probs, onehot_targets, name="chosen_probs")
  avg_log_likelihood = tf.reduce_mean(tf.log(chosen_probs))
  return avg_log_likelihood, tf.constant(1.0) 
开发者ID:tensorflow,项目名称:tensor2tensor,代码行数:24,代码来源:metrics.py

示例2: set_precision

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import squeeze [as 别名]
def set_precision(predictions, labels,
                  weights_fn=common_layers.weights_nonzero):
  """Precision of set predictions.

  Args:
    predictions : A Tensor of scores of shape [batch, nlabels].
    labels: A Tensor of int32s giving true set elements,
      of shape [batch, seq_length].
    weights_fn: A function to weight the elements.

  Returns:
    hits: A Tensor of shape [batch, nlabels].
    weights: A Tensor of shape [batch, nlabels].
  """
  with tf.variable_scope("set_precision", values=[predictions, labels]):
    labels = tf.squeeze(labels, [2, 3])
    weights = weights_fn(labels)
    labels = tf.one_hot(labels, predictions.shape[-1])
    labels = tf.reduce_max(labels, axis=1)
    labels = tf.cast(labels, tf.bool)
    return tf.to_float(tf.equal(labels, predictions)), weights 
开发者ID:tensorflow,项目名称:tensor2tensor,代码行数:23,代码来源:metrics.py

示例3: set_recall

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import squeeze [as 别名]
def set_recall(predictions, labels, weights_fn=common_layers.weights_nonzero):
  """Recall of set predictions.

  Args:
    predictions : A Tensor of scores of shape [batch, nlabels].
    labels: A Tensor of int32s giving true set elements,
      of shape [batch, seq_length].
    weights_fn: A function to weight the elements.

  Returns:
    hits: A Tensor of shape [batch, nlabels].
    weights: A Tensor of shape [batch, nlabels].
  """
  with tf.variable_scope("set_recall", values=[predictions, labels]):
    labels = tf.squeeze(labels, [2, 3])
    weights = weights_fn(labels)
    labels = tf.one_hot(labels, predictions.shape[-1])
    labels = tf.reduce_max(labels, axis=1)
    labels = tf.cast(labels, tf.bool)
    return tf.to_float(tf.equal(labels, predictions)), weights 
开发者ID:tensorflow,项目名称:tensor2tensor,代码行数:22,代码来源:metrics.py

示例4: rouge_l_fscore

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import squeeze [as 别名]
def rouge_l_fscore(predictions, labels, **unused_kwargs):
  """ROUGE scores computation between labels and predictions.

  This is an approximate ROUGE scoring method since we do not glue word pieces
  or decode the ids and tokenize the output.

  Args:
    predictions: tensor, model predictions
    labels: tensor, gold output.

  Returns:
    rouge_l_fscore: approx rouge-l f1 score.
  """
  outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
  # Convert the outputs and labels to a [batch_size, input_length] tensor.
  outputs = tf.squeeze(outputs, axis=[-1, -2])
  labels = tf.squeeze(labels, axis=[-1, -2])
  rouge_l_f_score = tf.py_func(rouge_l_sentence_level, (outputs, labels),
                               tf.float32)
  return rouge_l_f_score, tf.constant(1.0) 
开发者ID:tensorflow,项目名称:tensor2tensor,代码行数:22,代码来源:rouge.py

示例5: rouge_2_fscore

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import squeeze [as 别名]
def rouge_2_fscore(predictions, labels, **unused_kwargs):
  """ROUGE-2 F1 score computation between labels and predictions.

  This is an approximate ROUGE scoring method since we do not glue word pieces
  or decode the ids and tokenize the output.

  Args:
    predictions: tensor, model predictions
    labels: tensor, gold output.

  Returns:
    rouge2_fscore: approx rouge-2 f1 score.
  """

  outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
  # Convert the outputs and labels to a [batch_size, input_length] tensor.
  outputs = tf.squeeze(outputs, axis=[-1, -2])
  labels = tf.squeeze(labels, axis=[-1, -2])
  rouge_2_f_score = tf.py_func(rouge_n, (outputs, labels), tf.float32)
  return rouge_2_f_score, tf.constant(1.0) 
开发者ID:tensorflow,项目名称:tensor2tensor,代码行数:22,代码来源:rouge.py

示例6: bleu_score

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import squeeze [as 别名]
def bleu_score(predictions, labels, **unused_kwargs):
  """BLEU score computation between labels and predictions.

  An approximate BLEU scoring method since we do not glue word pieces or
  decode the ids and tokenize the output. By default, we use ngram order of 4
  and use brevity penalty. Also, this does not have beam search.

  Args:
    predictions: tensor, model predictions
    labels: tensor, gold output.

  Returns:
    bleu: int, approx bleu score
  """
  outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
  # Convert the outputs and labels to a [batch_size, input_length] tensor.
  outputs = tf.squeeze(outputs, axis=[-1, -2])
  labels = tf.squeeze(labels, axis=[-1, -2])

  bleu = tf.py_func(compute_bleu, (labels, outputs), tf.float32)
  return bleu, tf.constant(1.0) 
开发者ID:tensorflow,项目名称:tensor2tensor,代码行数:23,代码来源:bleu_hook.py

示例7: decode

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import squeeze [as 别名]
def decode(self, bottleneck):
    """Auto-decode from the bottleneck and return the result."""
    # Get the shape from bottleneck and num channels.
    shape = common_layers.shape_list(bottleneck)
    try:
      num_channels = self.hparams.problem.num_channels
    except AttributeError:
      num_channels = 1
    dummy_targets = tf.zeros(shape[:-1] + [num_channels])
    # Set the bottleneck to decode.
    if len(shape) > 4:
      bottleneck = tf.squeeze(bottleneck, axis=[1])
    bottleneck = 2 * bottleneck - 1  # Be -1/1 instead of 0/1.
    self._cur_bottleneck_tensor = bottleneck
    # Run decoding.
    res = self.infer({"targets": dummy_targets})
    self._cur_bottleneck_tensor = None
    return res 
开发者ID:tensorflow,项目名称:tensor2tensor,代码行数:20,代码来源:autoencoders.py

示例8: body

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import squeeze [as 别名]
def body(self, features):
    # Remove dropout if not training
    hparams = self._hparams
    targets = features["targets"]
    targets = tf.squeeze(targets, 2)

    (decoder_input, decoder_self_attention_bias) = attention_lm_prepare_decoder(
        targets, hparams)

    decoder_input = tf.nn.dropout(decoder_input,
                                  1.0 - hparams.layer_prepostprocess_dropout)
    decoder_output = attention_lm_decoder(decoder_input,
                                          decoder_self_attention_bias, hparams)
    decoder_output = tf.expand_dims(decoder_output, 2)

    return decoder_output 
开发者ID:tensorflow,项目名称:tensor2tensor,代码行数:18,代码来源:attention_lm.py

示例9: infer

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import squeeze [as 别名]
def infer(self,
            features=None,
            decode_length=1,
            beam_size=1,
            top_beams=1,
            alpha=0.0,
            use_tpu=False):
    """Predict."""
    del decode_length, beam_size, top_beams, alpha, use_tpu
    assert features is not None
    logits, _ = self(features)
    assert len(logits.get_shape()) == 5
    logits = tf.squeeze(logits, [1, 2, 3])
    log_probs = common_layers.log_prob_from_logits(logits)
    predictions, scores = common_layers.argmax_with_score(log_probs)
    return {
        "outputs": predictions,
        "scores": scores,
    } 
开发者ID:tensorflow,项目名称:tensor2tensor,代码行数:21,代码来源:vqa_attention.py

示例10: infer

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import squeeze [as 别名]
def infer(self,
            features=None,
            decode_length=50,
            beam_size=1,
            top_beams=1,
            alpha=0.0,
            use_tpu=False):
    """Predict."""
    del decode_length, beam_size, top_beams, alpha, use_tpu
    assert features is not None
    logits, _ = self(features)  # pylint: disable=not-callable
    assert len(logits.get_shape()) == 5
    logits = tf.squeeze(logits, [1, 2, 3])
    log_probs = common_layers.log_prob_from_logits(logits)
    predictions, scores = common_layers.argmax_with_score(log_probs)
    return {
        "outputs": predictions,
        "scores": scores,
    } 
开发者ID:tensorflow,项目名称:tensor2tensor,代码行数:21,代码来源:resnet.py

示例11: encode_knowledge_bottom

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import squeeze [as 别名]
def encode_knowledge_bottom(self, features):
    tf.logging.info("Encoding knowledge " + str(self.triple_num))
    # Make sure this is embeddings for triples
    # <tf.float32>[batch_size, triple_num*max_triple_length, 1, emb_dim]
    fact_embedding = features["encoded_triples"]
    # [batch_size, triple_num*max_triple_length, emb_dim]
    fact_embedding = tf.squeeze(fact_embedding, 2)

    kb_shape = common_layers.shape_list(fact_embedding)
    batch_size = kb_shape[0]
    embed_dim = kb_shape[2]
    # <tf.float32>[batch_size*triple_num, max_triple_length, emb_dim]
    re_fact_embedding = tf.reshape(
        fact_embedding, [batch_size * self.triple_num, -1, embed_dim],
        name="reshape_fact_embedding")

    # <tf.int64>[batch_size, triple_num]
    input_fact_lengths = features["triple_lens"]
    # Stack the fact lengths.
    # <tf.int64>[batch_size*max_triple_num]
    re_fact_lengths = tf.reshape(
        input_fact_lengths, [batch_size * self.triple_num, 1],
        name="reshape_fact_lengths")

    return re_fact_embedding, re_fact_lengths 
开发者ID:tensorflow,项目名称:tensor2tensor,代码行数:27,代码来源:neural_assistant.py

示例12: compute_last_embedding

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import squeeze [as 别名]
def compute_last_embedding(input_embeddings, input_lengths, hparams):
  """Computes average of last K embedding.

  Args:
    input_embeddings: <tf.float32>[bs, max_seq_len, emb_dim]
    input_lengths: <tf.int64>[bs, 1]
    hparams: model hparams

  Returns:
    last_k_embedding: <tf.float32>[bs, emb_dim]
  """
  max_seq_len = tf.shape(input_embeddings)[1]
  # <tf.float32>[bs, 1, max_seq_len]
  mask = tf.sequence_mask(input_lengths, max_seq_len, dtype=tf.float32)
  del_mask = tf.sequence_mask(
      input_lengths - hparams.last_k, max_seq_len, dtype=tf.float32)
  final_mask = mask - del_mask
  # <tf.float32>[bs, 1, emb_dim]
  sum_embedding = tf.matmul(final_mask, input_embeddings)
  # <tf.float32>[bs, 1, emb_dim]
  last_k_embedding = sum_embedding / tf.to_float(
      tf.expand_dims(
          tf.ones([tf.shape(input_embeddings)[0], 1]) * hparams.last_k, 2))
  # <tf.float32>[bs, dim]
  return tf.squeeze(last_k_embedding, 1) 
开发者ID:tensorflow,项目名称:tensor2tensor,代码行数:27,代码来源:neural_assistant.py

示例13: compute_max_pool_embedding

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import squeeze [as 别名]
def compute_max_pool_embedding(input_embeddings, input_lengths):
  """Computes max pool embedding.

  Args:
    input_embeddings: <tf.float32>[bs, max_seq_len, emb_dim]
    input_lengths: <tf.int64>[bs, 1]

  Returns:
    max_pool_embedding: <tf.float32>[bs, emb_dim]
  """
  max_seq_len = tf.shape(input_embeddings)[1]
  # <tf.float32>[bs, max_seq_len]
  mask = 1.0 - tf.sequence_mask(input_lengths, max_seq_len, dtype=tf.float32)
  mask = tf.squeeze(mask * (-1e-6), 1)
  mask = tf.expand_dims(mask, 2)
  # <tf.float32>[bs, emb_dim]
  max_pool_embedding = tf.reduce_max(input_embeddings + mask, 1)
  # <tf.float32>[bs, dim]
  return max_pool_embedding 
开发者ID:tensorflow,项目名称:tensor2tensor,代码行数:21,代码来源:neural_assistant.py

示例14: _apply_logic

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import squeeze [as 别名]
def _apply_logic(self, input_tensor, output_depth, hparams, var_scope_suffix,
                   nonpadding, mask_future, **unused_kwargs):
    """Applies conv logic to `input_tensor`."""
    with tf.variable_scope("%s_conv_%s" % (self._conv_type, var_scope_suffix)):
      if mask_future:
        # Pad shift the inputs so that temporal information does not leak. This
        # must be used in tandem with VALID padding.
        pad_amount = int(self._conv_width - 1) * self._dilation_rate
        logic_output = tf.pad(
            input_tensor, paddings=[[0, 0], [pad_amount, 0], [0, 0]])
        padding = "VALID"
      else:
        logic_output = input_tensor
        padding = "SAME"

      logic_output = tf.expand_dims(logic_output, 2)
      logic_output = self._conv_function(logic_output, output_depth, padding)

      logic_output = tf.squeeze(logic_output, 2)
    return logic_output 
开发者ID:tensorflow,项目名称:tensor2tensor,代码行数:22,代码来源:nas_layers.py

示例15: _create_greedy_infer_model

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import squeeze [as 别名]
def _create_greedy_infer_model(self):
    """Creates model for greedy inference testing.

    Returns:
      model: A t2t model.
      features: An map of string to tensor.
    """
    model, features = get_model(transformer.transformer_tiny())

    out_logits, _ = model(features)
    out_logits = tf.squeeze(out_logits, axis=[2, 3])
    loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
        logits=tf.reshape(out_logits, [-1, VOCAB_SIZE]),
        labels=tf.reshape(features["targets"], [-1]))
    loss = tf.reduce_mean(loss)
    apply_grad = tf.train.AdamOptimizer(0.001).minimize(loss)

    with self.test_session():
      tf.global_variables_initializer().run()
      for _ in range(10):
        apply_grad.run()

    model.set_mode(tf.estimator.ModeKeys.PREDICT)

    return model, features 
开发者ID:tensorflow,项目名称:tensor2tensor,代码行数:27,代码来源:evolved_transformer_test.py


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