我们从Python开源项目中，提取了以下26个代码示例，用于说明如何使用tensorflow.as_dtype()。
def __new__(cls, dtype=None, shape=None, tag='', tensor=None): if tensor is not None: if dtype is not None: raise TypeError('Specify only one of tensor and dtype.') if shape is not None: raise TypeError('Specify only one of tensor and shape.') dtype = tensor.dtype shape = tensor.get_shape().as_list() elif not (isinstance(dtype, tf.DType) or isinstance(dtype, six.string_types)): raise TypeError('%r is not a tf.DType or string' % (dtype,)) dtype = tf.as_dtype(dtype).base_dtype.name if not all(isinstance(s, numbers.Integral) and s >= 0 for s in shape): raise TypeError('shape must be non-negative integers: %s' % shape) shape = tuple(int(s) for s in shape) if not isinstance(tag, six.string_types): raise TypeError('A TypeShape tag must be a string; type of %r is %s' % (tag, type(tag))) return _TypeShape.__new__(cls, dtype, shape, tag)
def __init__(self, images, labels, fake_data=False, one_hot=False, dtype=tf.float32): """Construct a DataSet. one_hot arg is used only if fake_data is true. `dtype` can be either `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into `[0, 1]`. """ dtype = tf.as_dtype(dtype).base_dtype if dtype not in (tf.uint8, tf.float32): raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype) if fake_data: self._num_examples = 10000 self.one_hot = one_hot else: assert images.shape[0] == labels.shape[0], ( 'images.shape: %s labels.shape: %s' % (images.shape, labels.shape)) self._num_examples = images.shape[0] # Convert shape from [num examples, rows, columns, depth] # to [num examples, rows*columns] (assuming depth == 1) assert images.shape[3] == 1 images = images.reshape(images.shape[0], images.shape[1] * images.shape[2]) if dtype == tf.float32: # Convert from [0, 255] -> [0.0, 1.0]. images = images.astype(numpy.float32) images = numpy.multiply(images, 1.0 / 255.0) self._images = images self._labels = labels self._epochs_completed = 0 self._index_in_epoch = 0
def _create_variables(self): if self.input_type.ndim != 0: raise TypeError('Embeddings take scalar inputs.') dtype = tf.as_dtype(self.input_type.dtype) if not dtype.is_integer: raise TypeError('Embeddings take integer inputs.') if dtype not in (tf.int32, tf.int64): # only dtypes supported by tf.gather if np.iinfo(dtype.as_numpy_dtype).max > 2147483647: # pedantic future-proofing to handle hypothetical tf.uint64 raise TypeError('cannot gather or upcast dtype %s' % dtype) self._cast = True else: self._cast = False self._weights = tf.get_variable( 'weights', self._weights_shape, initializer=self._initializer, trainable=self._trainable)
def __init__(self, shape, dtype='float32', name=None): super(Tensor, self).__init__(input_type=tdt.PyObjectType(), output_type=tdt.TensorType(shape, dtype), name=name) self._dtype = np.dtype(self.output_type.dtype) if not shape and tf.as_dtype(dtype).is_integer: # memoize scalar ints self._evaluate = self._evaluate_memoized
def dtype_enum(self): """The dtype of this TypeShape as an enum.""" return tf.as_dtype(self.dtype).as_datatype_enum
def _build(self, **kwargs): """Build the mask input layer.""" Mask = kwargs[self.name] assert tf.as_dtype(Mask.dtype).is_bool M = tf.convert_to_tensor(Mask) return M, 0.0
def _check_rank_type(self, X, M): """Check the rank of the input tensors.""" data_rank = len(X.shape) mask_rank = len(M.shape) assert data_rank == 3 assert mask_rank == 2 assert tf.as_dtype(M.dtype).is_bool
def __init__(self, dtype): self._dtype = tf.as_dtype(dtype)
def __setstate__(self, state): self._dtype = tf.as_dtype(state)
def __setstate__(self, state): self._dtype = tf.as_dtype(state['dtype']) self._is_categorical = state['is_categorical'] self._min_value = state['min_value'] self._max_value = state['max_value'] self._vocabulary_file = state['vocabulary_file']
def __init__(self, images, labels, dtype=tf.float32): dtype = tf.as_dtype(dtype).base_dtype if dtype is not tf.float32: raise TypeError('Invalid image dtype %r, expected float32' %dtype) assert images.shape[0] == labels.shape[0], ('images.shape: %s labels.shape: %s' % (images.shape, labels.shape)) self._num_examples = images.shape[0] self._images = images self._labels = labels self._epochs_completed = 0 self._index_in_epoch = 0
def clip_gradients_by_global_norm(gradients_variables, clip_norm=20.): """Clips gradients of a multitask loss by their global norm. Ignores all-zero tensors when computing the global norm. Args: gradients_variables: a list of pairs (gradient, variable). clip_norm: a float Tensor, the global norm to clip on. Default is 20.0. Returns: list: A list of pairs of the same type as gradients_variables,. fixed_global_norm: A 0-D (scalar) Tensor representing the global norm. """ gradients, variables = six.moves.zip(*gradients_variables) def _replace_nonexisting_grad(grad): if grad is None: return grad all_zeros = _is_all_zeros(grad) return tf.cond( all_zeros, lambda: tf.zeros([], dtype=tf.as_dtype(grad.dtype)), lambda: grad) nonzero_gradients = [_replace_nonexisting_grad(g) for g in gradients] fixed_global_norm = tf.global_norm(nonzero_gradients) gradients, _ = tf.clip_by_global_norm( gradients, clip_norm, use_norm=fixed_global_norm) return list(six.moves.zip(gradients, variables)), fixed_global_norm
def __init__(self, images, labels, dtype=tf.float32, flatten_images=True): """Construct a DataSet. `dtype` can be either `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into `[0, 1]`. """ dtype = tf.as_dtype(dtype).base_dtype if dtype not in (tf.uint8, tf.float32): raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype) assert images.shape[0] == labels.shape[0], ( 'images.shape: %s labels.shape: %s' % (images.shape, labels.shape)) self._num_examples = images.shape[0] # Convert shape from [num examples, rows, columns, depth] # to [num examples, rows*columns] (assuming depth == 1) assert images.shape[3] == 1 if (flatten_images): images = images.reshape(images.shape[0], images.shape[1] * images.shape[2]) if dtype == tf.float32: # Convert from [0, 255] -> [0.0, 1.0]. images = images.astype(numpy.float32) images = numpy.multiply(images, 1.0 / 255.0) self._images = images self._labels = labels self._epochs_completed = 0 self._index_in_epoch = 0
def cast_dtype(dtype, target): """Changes float dtypes to the target dtype, leaves others unchanged. Used to map all float values to a target precision. Also casts numpy dtypes to TensorFlow dtypes. Parameters ---------- dtype : ``tf.DType`` or :class:`~numpy:numpy.dtype` Input dtype to be converted target : ``tf.DType`` Floating point dtype to which all floating types should be converted Returns ------- ``tf.DType`` Input dtype, converted to ``target`` type if necessary """ if not isinstance(dtype, tf.DType): dtype = tf.as_dtype(dtype) if dtype.is_floating: dtype = target return dtype
def __init__(self, images, labels, fake_data=False, one_hot=False, dtype=tf.float32): """Construct a DataSet. one_hot arg is used only if fake_data is true. `dtype` can be either `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into `[0, 1]`. """ dtype = tf.as_dtype(dtype).base_dtype if dtype not in (tf.uint8, tf.float32): raise TypeError( 'Invalid image dtype %r, expected uint8 or float32' % dtype) if fake_data: self._num_examples = 10000 self.one_hot = one_hot else: assert images.shape[0] == labels.shape[0], ( 'images.shape: %s labels.shape: %s' % (images.shape, labels.shape)) self._num_examples = images.shape[0] self._width = images.shape[1] self._height = images.shape[2] self._depth = images.shape[3] # Convert shape from [num examples, rows, columns, depth] # to [num examples, rows*columns*depth] assert images.shape[3] == IMAGE_DEPTH images = images.reshape( images.shape[0], images.shape[1] * images.shape[2] * images.shape[3]) if dtype == tf.float32: # Convert from [0, 255] -> [0.0, 1.0]. images = images.astype(np.float32) images = np.multiply(images, 1.0 / 255.0) self._images = images self._labels = labels self._epochs_completed = 0 self._index_in_epoch = 0
def _process_health_pill_value(self, wall_time, step, device_name, output_slot, node_name, tensor_proto, node_name_set=None): """Creates a HealthPillEvent containing various properties of a health pill. Args: wall_time: The wall time in seconds. step: The session run step of the event. device_name: The name of the node's device. output_slot: The numeric output slot. node_name: The name of the node (without the output slot). tensor_proto: A tensor proto of data. node_name_set: An optional set of node names that are relevant. If not provided, no filtering by relevance occurs. Returns: An event_accumulator.HealthPillEvent. Or None if one could not be created. """ if node_name_set and node_name not in node_name_set: # This event is not relevant. return None # Since we seek health pills for a specific step, this function # returns 1 health pill per node per step. The wall time is the # seconds since the epoch. elements = list(tf.make_ndarray(tensor_proto)) return HealthPillEvent( wall_time=wall_time, step=step, device_name=device_name, output_slot=output_slot, node_name=node_name, dtype=repr(tf.as_dtype(elements[12])), shape=elements[14:], value=elements)
def variable(value, dtype=None, borrow=None, broadcastable=None, name=None, settable=True): if dtype is None: dtype = np.float32 x = tf.Variable(value, dtype=tf.as_dtype(dtype), name=name) x._statestream_settable = settable if settable: x._assign_placeholder = tf.placeholder(dtype, shape=value.shape) x._assign_op = x.assign(x._assign_placeholder) return x
def scalar(value, dtype=np.float32, borrow=None, name=None, settable=True): if dtype is None: dtype = np.float32 x = tf.Variable(value, dtype=tf.as_dtype(dtype), name=name) x._statestream_settable = settable if settable: x._assign_placeholder = tf.placeholder(dtype, shape=x.get_shape().as_list()) x._assign_op = x.assign(x._assign_placeholder) return x
def set_value(x, value): if x._statestream_settable: value = np.asarray(value, dtype=x.dtype.base_dtype.name) tf_dtype = tf.as_dtype(x.dtype.name.split('_')[0]) tf_get_session().run(x._assign_op, feed_dict={x._assign_placeholder: value}) else: raise TypeError("Tried to set / assign non-settable tensorflow variable: " + str(x.name))
def zeros(shape, dtype=None, name=None): if dtype is None: dtype = np.float32 return tf.zeros(shape=shape, dtype=tf.as_dtype(dtype), name=name)
def ones(shape, dtype=None, name=None): if dtype is None: dtype = np.float32 return tf.ones(shape=shape, dtype=tf.as_dtype(dtype), name=name)
def __init__(self, images, labels, fake_data=False, one_hot=False, dtype=tf.float32, trim_flag=False): """Construct a DataSet. one_hot arg is used only if fake_data is true. `dtype` can be either `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into `[0, 1]`. """ dtype = tf.as_dtype(dtype).base_dtype if dtype not in (tf.uint8, tf.float32): raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype) if fake_data: self._num_examples = 10000 self.one_hot = one_hot else: assert images.shape[0] == labels.shape[0], ( 'images.shape: %s labels.shape: %s' % (images.shape, labels.shape)) self._num_examples = images.shape[0] # Convert shape from [num examples, rows, columns, depth] # to [num examples, rows*columns] (assuming depth == 1) assert images.shape[3] == 1 # images = images.reshape(images.shape[0], # images.shape[1] * images.shape[2]) if dtype == tf.float32: # Convert from [0, 255] -> [0.0, 1.0]. images = images.astype(np.float32) images = np.multiply(images, 1.0 / 255.0) # log.info(str(images.max())) log.info(str(images.shape)) # (50000, 28, 28, 1) log.info(str(labels.shape)) # if trim_flag: # images = images[:500] # labels = labels[:500] # add generated data '''gen_data = np.load('mnist-gen') images = np.concatenate((images, gen_data['image'])) labels = np.concatenate((labels, gen_data['label']))''' self._num_examples = images.shape[0] log.info('using %d data for training' % self._num_examples ) self._images = images self._labels = labels self._epochs_completed = 0 self._index_in_epoch = 0
