Python tensorflow.as_dtype方法代碼示例

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import as_dtype [as 別名]
def __init__(self, model, dtypestr='float32'):
        """
        :param model: An instance of the cleverhans.model.Model class.
        :param back: The backend to use. Inherited from AttackBase class.
        :param dtypestr: datatype of the input data samples and crafted
                        adversarial attacks.
        """
        # Validate the input arguments.
        if dtypestr != 'float32' and dtypestr != 'float64':
            raise ValueError("Unexpected input for argument dtypestr.")
        import tensorflow as tf
        tfe = tf.contrib.eager
        self.tf_dtype = tf.as_dtype(dtypestr)
        self.np_dtype = np.dtype(dtypestr)

        if not isinstance(model, Model):
            raise ValueError("The model argument should be an instance of"
                             " the cleverhans.model.Model class.")
        # Prepare attributes
        self.model = model
        self.dtypestr = dtypestr 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import as_dtype [as 別名]
def __init__(self,
                 dtype,
                 batch_shape=None,
                 value_shape=None,
                 group_ndims=0,
                 is_continuous=None,
                 **kwargs):
        dtype = tf.float32 if dtype is None else tf.as_dtype(dtype).base_dtype

        self.explicit_batch_shape = tf.TensorShape(batch_shape)

        self.explicit_value_shape = tf.TensorShape(value_shape)

        if is_continuous is None:
            is_continuous = dtype.is_floating

        super(Empirical, self).__init__(
            dtype=dtype,
            param_dtype=None,
            is_continuous=is_continuous,
            is_reparameterized=False,
            use_path_derivative=False,
            group_ndims=group_ndims,
            **kwargs) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import as_dtype [as 別名]
def _legacy_output_transform_func(*expr, out_mul=1.0, out_add=0.0, out_shrink=1, out_dtype=None):
    if out_mul != 1.0:
        expr = [x * out_mul for x in expr]

    if out_add != 0.0:
        expr = [x + out_add for x in expr]

    if out_shrink > 1:
        ksize = [1, 1, out_shrink, out_shrink]
        expr = [tf.nn.avg_pool(x, ksize=ksize, strides=ksize, padding="VALID", data_format="NCHW") for x in expr]

    if out_dtype is not None:
        if tf.as_dtype(out_dtype).is_integer:
            expr = [tf.round(x) for x in expr]
        expr = [tf.saturate_cast(x, out_dtype) for x in expr]
    return expr 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import as_dtype [as 別名]
def get_initializer(initializer, initializer_gain):
    tfdtype = tf.as_dtype(dtype.floatx())

    if initializer == "uniform":
        max_val = initializer_gain
        return tf.random_uniform_initializer(-max_val, max_val, dtype=tfdtype)
    elif initializer == "normal":
        return tf.random_normal_initializer(0.0, initializer_gain, dtype=tfdtype)
    elif initializer == "normal_unit_scaling":
        return tf.variance_scaling_initializer(initializer_gain,
                                               mode="fan_avg",
                                               distribution="normal",
                                               dtype=tfdtype)
    elif initializer == "uniform_unit_scaling":
        return tf.variance_scaling_initializer(initializer_gain,
                                               mode="fan_avg",
                                               distribution="uniform",
                                               dtype=tfdtype)
    else:
        tf.logging.warn("Unrecognized initializer: %s" % initializer)
        tf.logging.warn("Return to default initializer: glorot_uniform_initializer")
        return tf.glorot_uniform_initializer(dtype=tfdtype) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import as_dtype [as 別名]
def __init__(self, shape, dtype):
        """Creates a schema for a variable used in policy.
        Allows for symbolic definition of shape. Shape can consist of integers, as well as
        strings BATCH and TIMESTEPS. This is taken advantage of in the optimizers, to
        create placeholders or variables that asynchronously prefetch the inputs.

        Parameters
        ----------
        shape: [int, np.int64, np.int32, or str]
            shape of the variable, e.g. [12, 4], [BATCH, 12], [BATCH, 'timestep']
        dtype:
            tensorflow type of the variable, e.g. tf.float32, tf.int32
        """
        assert all(isinstance(s, (int, np.int64, np.int32)) or s in [BATCH, TIMESTEPS] for s in shape), 'Bad shape %s' % shape
        self.shape = shape
        self.dtype = tf.as_dtype(dtype) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import as_dtype [as 別名]
def read_summaries(event_dir, event_file_pattern="events.out.tfevents.*"):
  """Reads summaries from TensorFlow event files.

  Args:
    event_dir: Directory containing event files.
    event_file_pattern: The pattern to look for event files.

  Returns:
    A list of tuple (step, dict of summaries), sorted by step.
  """
  if not tf.io.gfile.exists(event_dir):
    return []
  summaries = collections.defaultdict(dict)
  for event_file in tf.io.gfile.glob(os.path.join(event_dir, event_file_pattern)):
    for event in tf.compat.v1.train.summary_iterator(event_file):
      if not event.HasField("summary"):
        continue
      for value in event.summary.value:
        tensor_proto = value.tensor
        tensor = tf.io.parse_tensor(
            tensor_proto.SerializeToString(), tf.as_dtype(tensor_proto.dtype))
        summaries[event.step][value.tag] = tf.get_static_value(tensor)
  return list(sorted(summaries.items(), key=lambda x: x[0])) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import as_dtype [as 別名]
def __init__(self,
               mean_initializer=tf.random_normal_initializer(stddev=0.1),
               stddev_initializer=tf.random_uniform_initializer(
                   minval=1e-5, maxval=0.1),
               mean_regularizer=None,
               stddev_regularizer=None,
               mean_constraint=None,
               stddev_constraint=positive(),
               seed=None,
               dtype=tf.float32):
    """Constructs the initializer."""
    super(TrainableNormal, self).__init__()
    self.mean_initializer = mean_initializer
    self.stddev_initializer = stddev_initializer
    self.mean_regularizer = mean_regularizer
    self.stddev_regularizer = stddev_regularizer
    self.mean_constraint = mean_constraint
    self.stddev_constraint = stddev_constraint
    self.seed = seed
    self.dtype = tf.as_dtype(dtype) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import as_dtype [as 別名]
def build(self, shape, dtype=None, add_variable_fn=None):
    """Builds the initializer, with the variables captured by the caller."""
    if dtype is None:
      dtype = self.dtype
    self.shape = shape
    self.dtype = tf.as_dtype(dtype)

    self.mean = add_variable_fn(
        'mean',
        shape=shape,
        initializer=self.mean_initializer,
        regularizer=self.mean_regularizer,
        constraint=self.mean_constraint,
        dtype=dtype,
        trainable=True)
    self.stddev = add_variable_fn(
        'stddev',
        shape=shape,
        initializer=self.stddev_initializer,
        regularizer=self.stddev_regularizer,
        constraint=self.stddev_constraint,
        dtype=dtype,
        trainable=True)
    self.built = True 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import as_dtype [as 別名]
def fprop(self, x, **kwargs):
        del kwargs
        with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE):
            w1 = tf.constant([[1.5, .3], [-2, 0.3]],
                             dtype=tf.as_dtype(x.dtype))
            w2 = tf.constant([[-2.4, 1.2], [0.5, -2.3]],
                             dtype=tf.as_dtype(x.dtype))
        h1 = tf.nn.sigmoid(tf.matmul(x, w1))
        res = tf.matmul(h1, w2)
        return {self.O_FEATURES: [h1, res],
                self.O_LOGITS: res,
                self.O_PROBS: tf.nn.softmax(res)} 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import as_dtype [as 別名]
def fprop(self, x, **kwargs):
        del kwargs
        with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE):
            w1 = tf.constant([[1.5, .3], [-2, 0.3]],
                             dtype=tf.as_dtype(x.dtype))
            w2 = tf.constant([[-2.4, 1.2], [0.5, -2.3]],
                             dtype=tf.as_dtype(x.dtype))
        h1 = tf.nn.sigmoid(tf.matmul(x, w1))
        res = tf.matmul(h1, w2)
        return {self.O_LOGITS: res,
                self.O_PROBS: tf.nn.softmax(res)} 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import as_dtype [as 別名]
def fprop(self, x, **kwargs):
        del kwargs
        with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE):
            w1 = tf.constant(
                [[1.5, .3], [-2, 0.3]], dtype=tf.as_dtype(x.dtype))
            w2 = tf.constant(
                [[-2.4, 1.2], [0.5, -2.3]], dtype=tf.as_dtype(x.dtype))
        h1 = tf.nn.sigmoid(tf.matmul(x, w1))
        res = tf.matmul(h1, w2)
        return {self.O_LOGITS: res, self.O_PROBS: tf.nn.softmax(res)} 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import as_dtype [as 別名]
def __init__(self, dtype=tf.float32):
        self.dtype = tf.as_dtype(dtype) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import as_dtype [as 別名]
def getTRTType(tensor):
    if tf.as_dtype(tensor.dtype) == tf.float32:
        return 0
    if tf.as_dtype(tensor.dtype) == tf.float16:
        return 1
    if tf.as_dtype(tensor.dtype) == tf.int8:
        return 2
    if tf.as_dtype(tensor.dtype) == tf.int32:
        return 3
    print("Tensor data type of %s is not supported in TensorRT"%(tensor.dtype))
    sys.exit(); 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import as_dtype [as 別名]
def __init__(self,
                 M: Tuple[int, int] = (1000, 2000),
                 dtype: type = np.float32) -> None:

        self.__M = M
        self.__dtype = tf.as_dtype(dtype) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import as_dtype [as 別名]
def random(cls,
               priorU: List[Distribution],
               likelihood: Likelihood,
               M: Tuple[int, ...],
               K: int,
               dtype: tf.DType,
               phase: Phase,
               stopCriterion,
               noiseUniformity: NoiseUniformity = HOMOGENEOUS,
               transform: bool = False) -> "TensorFactorisation":

        # initialize U
        dtype = tf.as_dtype(dtype)
        zero = tf.constant(0., dtype=dtype)
        one = tf.constant(1., dtype=dtype)
        normal = tf.distributions.Normal(loc=zero, scale=one)
        F = len(M)
        U = []
        for f in range(F):
            if priorU[f].nonNegative:
                UfInit = tf.abs(normal.sample(sample_shape=(K, M[f])))
            else:
                UfInit = normal.sample(sample_shape=(K, M[f]))
            U.append(UfInit)

        # instantiate
        tefa = TensorFactorisation(U=U,
                                   priorU=priorU,
                                   likelihood=likelihood,
                                   dtype=dtype,
                                   phase=phase,
                                   transform=transform,
                                   noiseUniformity=noiseUniformity,
                                   stopCriterion=stopCriterion)
        return(tefa)