python类float16()的实例源码

cifar10.py 文件源码 项目:visual-interaction-networks_tensorflow 作者: jaesik817 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def inputs(eval_data):
  """Construct input for CIFAR evaluation using the Reader ops.

  Args:
    eval_data: bool, indicating if one should use the train or eval data set.

  Returns:
    images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
    labels: Labels. 1D tensor of [batch_size] size.

  Raises:
    ValueError: If no data_dir
  """
  if not FLAGS.data_dir:
    raise ValueError('Please supply a data_dir')
  data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
  images, labels = cifar10_input.inputs(eval_data=eval_data,
                                        data_dir=data_dir,
                                        batch_size=FLAGS.batch_size)
  if FLAGS.use_fp16:
    images = tf.cast(images, tf.float16)
    labels = tf.cast(labels, tf.float16)
  return images, labels
diet.py 文件源码 项目:tefla 作者: openAGI 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def _quantize(x, params, randomize=True):
    """Quantize x according to params, optionally randomizing the rounding."""
    if not params.quantize:
        return x

    if not randomize:
        return tf.bitcast(
            tf.cast(x / params.quantization_scale, tf.int16), tf.float16)

    abs_x = tf.abs(x)
    sign_x = tf.sign(x)
    y = abs_x / params.quantization_scale
    y = tf.floor(y + tf.random_uniform(tf.shape(x)))
    y = tf.minimum(y, tf.int16.max) * sign_x
    q = tf.bitcast(tf.cast(y, tf.int16), tf.float16)
    return q
rnn_cell.py 文件源码 项目:deepSpeech 作者: fordDeepDSP 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def _variable_on_cpu(name, shape, initializer=None, use_fp16=False):
    """Helper to create a Variable stored on cpu memory.

    Args:
      name: name of the variable
      shape: list of ints
      initializer: initializer for Variable

    Returns:
      Variable Tensor
    """
    with tf.device('/cpu'):
        dtype = tf.float16 if use_fp16 else tf.float32
        var = tf.get_variable(name, shape=shape, initializer=initializer,
                              dtype=dtype)
    return var
helper_routines.py 文件源码 项目:deepSpeech 作者: fordDeepDSP 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def _variable_on_cpu(name, shape, initializer, use_fp16):
    """Helper to create a Variable stored on cpu memory.

    Args:
      name: name of the variable
      shape: list of ints
      initializer: initializer for Variable

    Returns:
      Variable Tensor
    """
    with tf.device('/cpu'):
        dtype = tf.float16 if use_fp16 else tf.float32
        var = tf.get_variable(name, shape,
                              initializer=initializer, dtype=dtype)
    return var
cifar10.py 文件源码 项目:DeepLearningAndTensorflow 作者: azheng333 项目源码 文件源码 阅读 205 收藏 0 点赞 0 评论 0
def _variable_with_weight_decay(name, shape, stddev, wd):
    """Helper to create an initialized Variable with weight decay.

    Note that the Variable is initialized with a truncated normal distribution.
    A weight decay is added only if one is specified.

    Args:
      name: name of the variable
      shape: list of ints
      stddev: standard deviation of a truncated Gaussian
      wd: add L2Loss weight decay multiplied by this float. If None, weight
          decay is not added for this Variable.

    Returns:
      Variable Tensor
    """
    dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
    var = _variable_on_cpu(
        name,
        shape,
        tf.truncated_normal_initializer(stddev=stddev, dtype=dtype))
    if wd is not None:
        weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')
        tf.add_to_collection('losses', weight_decay)
    return var
cifar10.py 文件源码 项目:DeepLearningAndTensorflow 作者: azheng333 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def distorted_inputs():
    """Construct distorted input for CIFAR training using the Reader ops.

    Returns:
      images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
      labels: Labels. 1D tensor of [batch_size] size.

    Raises:
      ValueError: If no data_dir
    """
    if not FLAGS.data_dir:
        raise ValueError('Please supply a data_dir')
    data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
    images, labels = cifar10_input.distorted_inputs(data_dir=data_dir,
                                                    batch_size=FLAGS.batch_size)
    if FLAGS.use_fp16:
        images = tf.cast(images, tf.float16)
        labels = tf.cast(labels, tf.float16)
    return images, labels
cifar10.py 文件源码 项目:DeepLearningAndTensorflow 作者: azheng333 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def inputs(eval_data):
    """Construct input for CIFAR evaluation using the Reader ops.

    Args:
      eval_data: bool, indicating if one should use the train or eval data set.

    Returns:
      images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
      labels: Labels. 1D tensor of [batch_size] size.

    Raises:
      ValueError: If no data_dir
    """
    if not FLAGS.data_dir:
        raise ValueError('Please supply a data_dir')
    data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
    images, labels = cifar10_input.inputs(eval_data=eval_data,
                                          data_dir=data_dir,
                                          batch_size=FLAGS.batch_size)
    if FLAGS.use_fp16:
        images = tf.cast(images, tf.float16)
        labels = tf.cast(labels, tf.float16)
    return images, labels
s2s.py 文件源码 项目:Seq2Seq_Chatbot_QA 作者: qhduan 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def create_model(session, forward_only):
    """????"""
    dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
    model = s2s_model.S2SModel(
        data_utils.dim,
        data_utils.dim,
        buckets,
        FLAGS.size,
        FLAGS.dropout,
        FLAGS.num_layers,
        FLAGS.max_gradient_norm,
        FLAGS.batch_size,
        FLAGS.learning_rate,
        FLAGS.num_samples,
        forward_only,
        dtype
    )
    return model
cifar10.py 文件源码 项目:pathnet 作者: jaesik817 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def _variable_with_weight_decay(name, shape, stddev, wd):
  """Helper to create an initialized Variable with weight decay.

  Note that the Variable is initialized with a truncated normal distribution.
  A weight decay is added only if one is specified.

  Args:
    name: name of the variable
    shape: list of ints
    stddev: standard deviation of a truncated Gaussian
    wd: add L2Loss weight decay multiplied by this float. If None, weight
        decay is not added for this Variable.

  Returns:
    Variable Tensor
  """
  dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
  var = _variable_on_cpu(
      name,
      shape,
      tf.truncated_normal_initializer(stddev=stddev, dtype=dtype))
  if wd is not None:
    weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')
    tf.add_to_collection('losses', weight_decay)
  return var
cifar10.py 文件源码 项目:pathnet 作者: jaesik817 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def distorted_inputs():
  """Construct distorted input for CIFAR training using the Reader ops.

  Returns:
    images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
    labels: Labels. 1D tensor of [batch_size] size.

  Raises:
    ValueError: If no data_dir
  """
  if not FLAGS.data_dir:
    raise ValueError('Please supply a data_dir')
  data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
  images, labels = cifar10_input.distorted_inputs(data_dir=data_dir,
                                                  batch_size=FLAGS.batch_size)
  if FLAGS.use_fp16:
    images = tf.cast(images, tf.float16)
    labels = tf.cast(labels, tf.float16)
  return images, labels
cifar10.py 文件源码 项目:pathnet 作者: jaesik817 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def inputs(eval_data):
  """Construct input for CIFAR evaluation using the Reader ops.

  Args:
    eval_data: bool, indicating if one should use the train or eval data set.

  Returns:
    images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
    labels: Labels. 1D tensor of [batch_size] size.

  Raises:
    ValueError: If no data_dir
  """
  if not FLAGS.data_dir:
    raise ValueError('Please supply a data_dir')
  data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
  images, labels = cifar10_input.inputs(eval_data=eval_data,
                                        data_dir=data_dir,
                                        batch_size=FLAGS.batch_size)
  if FLAGS.use_fp16:
    images = tf.cast(images, tf.float16)
    labels = tf.cast(labels, tf.float16)
  return images, labels
tensorflow_backend.py 文件源码 项目:keras-customized 作者: ambrite 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def _convert_string_dtype(dtype):
    if dtype == 'float16':
        return tf.float16
    if dtype == 'float32':
        return tf.float32
    elif dtype == 'float64':
        return tf.float64
    elif dtype == 'int16':
        return tf.int16
    elif dtype == 'int32':
        return tf.int32
    elif dtype == 'int64':
        return tf.int64
    elif dtype == 'uint8':
        return tf.int8
    elif dtype == 'uint16':
        return tf.uint16
    else:
        raise ValueError('Unsupported dtype:', dtype)
translate_create_model.py 文件源码 项目:savchenko 作者: JuleLaryushina 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def create_model(session, forward_only):
  """Create translation model and initialize or load parameters in session."""
  dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
  model = seq2seq_model.Seq2SeqModel(
      FLAGS.en_vocab_size,
      FLAGS.fr_vocab_size,
      _buckets,
      FLAGS.size,
      FLAGS.num_layers,
      FLAGS.max_gradient_norm,
      FLAGS.batch_size,
      FLAGS.learning_rate,
      FLAGS.learning_rate_decay_factor,
      forward_only=forward_only)
  ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
  if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path):
    print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
    model.saver.restore(session, ckpt.model_checkpoint_path)
  else:
    print("Created model with fresh parameters.")
    session.run(tf.initialize_all_variables())
  return model
translate2.py 文件源码 项目:MyCommentOnTensorFlowModel 作者: guotong1988 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def create_model(session, forward_only):
  """Create translation model and initialize or load parameters in session."""
  dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
  model = seq2seq_model.Seq2SeqModel(
      FLAGS.from_vocab_size,
      FLAGS.to_vocab_size,
      _buckets,
      FLAGS.size,
      FLAGS.num_layers,
      FLAGS.max_gradient_norm,
      FLAGS.batch_size,
      FLAGS.learning_rate,
      FLAGS.learning_rate_decay_factor,
      forward_only=forward_only,
      dtype=dtype)
  ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
  if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
    print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
    model.saver.restore(session, ckpt.model_checkpoint_path)
  else:
    print("Created model with fresh parameters.")
    session.run(tf.global_variables_initializer())
  return model
translate.py 文件源码 项目:MyCommentOnTensorFlowModel 作者: guotong1988 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def create_model(session, forward_only):
  """Create translation model and initialize or load parameters in session."""
  dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
  model = seq2seq_model.Seq2SeqModel(
      FLAGS.from_vocab_size,
      FLAGS.to_vocab_size,
      _buckets,
      FLAGS.size,
      FLAGS.num_layers,
      FLAGS.max_gradient_norm,
      FLAGS.batch_size,
      FLAGS.learning_rate,
      FLAGS.learning_rate_decay_factor,
      forward_only=forward_only,
      dtype=dtype)
  ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
  if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
    print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
    model.saver.restore(session, ckpt.model_checkpoint_path)
  else:
    print("Created model with fresh parameters.")
    session.run(tf.global_variables_initializer())
  return model
translate.py 文件源码 项目:JokeGeneratorSeq2Seq 作者: jiriroz 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def create_model(session, forward_only):
  """Create translation model and initialize or load parameters in session."""
  dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
  model = seq2seq_model.Seq2SeqModel(
      FLAGS.en_vocab_size,
      FLAGS.fr_vocab_size,
      _buckets,
      FLAGS.size,
      FLAGS.num_layers,
      FLAGS.max_gradient_norm,
      FLAGS.batch_size,
      FLAGS.learning_rate,
      FLAGS.learning_rate_decay_factor,
      forward_only=forward_only,
      dtype=dtype)
  ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
  if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path):
    print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
    model.saver.restore(session, ckpt.model_checkpoint_path)
  else:
    print("Created model with fresh parameters.")
    session.run(tf.initialize_all_variables())
  return model
cifar10.py 文件源码 项目:tf-variational-dropout 作者: BayesWatch 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def distorted_inputs():
  """Construct distorted input for CIFAR training using the Reader ops.

  Returns:
    images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
    labels: Labels. 1D tensor of [batch_size] size.

  Raises:
    ValueError: If no data_dir
  """
  if not FLAGS.data_dir:
    raise ValueError('Please supply a data_dir')
  data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
  images, labels = cifar10_input.distorted_inputs(data_dir=data_dir,
                                                  batch_size=FLAGS.batch_size)
  if FLAGS.use_fp16:
    images = tf.cast(images, tf.float16)
    labels = tf.cast(labels, tf.float16)
  return images, labels
cifar10.py 文件源码 项目:tf-variational-dropout 作者: BayesWatch 项目源码 文件源码 阅读 33 收藏 0 点赞 0 评论 0
def inputs(eval_data):
  """Construct input for CIFAR evaluation using the Reader ops.

  Args:
    eval_data: bool, indicating if one should use the train or eval data set.

  Returns:
    images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
    labels: Labels. 1D tensor of [batch_size] size.

  Raises:
    ValueError: If no data_dir
  """
  if not FLAGS.data_dir:
    raise ValueError('Please supply a data_dir')
  data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
  images, labels = cifar10_input.inputs(eval_data=eval_data,
                                        data_dir=data_dir,
                                        batch_size=FLAGS.batch_size)
  if FLAGS.use_fp16:
    images = tf.cast(images, tf.float16)
    labels = tf.cast(labels, tf.float16)
  return images, labels
model.py 文件源码 项目:My-TensorFlow-tutorials 作者: kevin28520 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def evaluation(logits, labels):
  """Evaluate the quality of the logits at predicting the label.
  Args:
    logits: Logits tensor, float - [batch_size, NUM_CLASSES].
    labels: Labels tensor, int32 - [batch_size], with values in the
      range [0, NUM_CLASSES).
  Returns:
    A scalar int32 tensor with the number of examples (out of batch_size)
    that were predicted correctly.
  """
  with tf.variable_scope('accuracy') as scope:
      correct = tf.nn.in_top_k(logits, labels, 1)
      correct = tf.cast(correct, tf.float16)
      accuracy = tf.reduce_mean(correct)
      tf.summary.scalar(scope.name+'/accuracy', accuracy)
  return accuracy

#%%
model.py 文件源码 项目:My-TensorFlow-tutorials 作者: kevin28520 项目源码 文件源码 阅读 40 收藏 0 点赞 0 评论 0
def evaluation(logits, labels):
  """Evaluate the quality of the logits at predicting the label.
  Args:
    logits: Logits tensor, float - [batch_size, NUM_CLASSES].
    labels: Labels tensor, int32 - [batch_size], with values in the
      range [0, NUM_CLASSES).
  Returns:
    A scalar int32 tensor with the number of examples (out of batch_size)
    that were predicted correctly.
  """
  with tf.variable_scope('accuracy') as scope:
      correct = tf.nn.in_top_k(logits, labels, 1)
      correct = tf.cast(correct, tf.float16)
      accuracy = tf.reduce_mean(correct)
      tf.summary.scalar(scope.name+'/accuracy', accuracy)
  return accuracy

#%%


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