Python backend.dtype函数代码示例

  def get_updates(self, loss, params):
    grads = self.get_gradients(loss, params)
    accumulators = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
    self.weights = accumulators
    self.updates = [state_ops.assign_add(self.iterations, 1)]

    lr = self.lr
    if self.initial_decay > 0:
      lr = lr * (  # pylint: disable=g-no-augmented-assignment
          1. /
          (1. +
           self.decay * math_ops.cast(self.iterations, K.dtype(self.decay))))

    for p, g, a in zip(params, grads, accumulators):
      # update accumulator
      new_a = self.rho * a + (1. - self.rho) * math_ops.square(g)
      self.updates.append(state_ops.assign(a, new_a))
      new_p = p - lr * g / (K.sqrt(new_a) + self.epsilon)

      # Apply constraints.
      if getattr(p, 'constraint', None) is not None:
        new_p = p.constraint(new_p)

      self.updates.append(state_ops.assign(p, new_p))
    return self.updates

def _preprocess_symbolic_input(x, data_format, mode):
  """Preprocesses a tensor encoding a batch of images.

  Arguments:
      x: Input tensor, 3D or 4D.
      data_format: Data format of the image tensor.
      mode: One of "caffe", "tf" or "torch".
          - caffe: will convert the images from RGB to BGR,
              then will zero-center each color channel with
              respect to the ImageNet dataset,
              without scaling.
          - tf: will scale pixels between -1 and 1,
              sample-wise.
          - torch: will scale pixels between 0 and 1 and then
              will normalize each channel with respect to the
              ImageNet dataset.

  Returns:
      Preprocessed tensor.
  """
  global _IMAGENET_MEAN

  if mode == 'tf':
    x /= 127.5
    x -= 1.
    return x

  if mode == 'torch':
    x /= 255.
    mean = [0.485, 0.456, 0.406]
    std = [0.229, 0.224, 0.225]
  else:
    if data_format == 'channels_first':
      # 'RGB'->'BGR'
      if K.ndim(x) == 3:
        x = x[::-1, ...]
      else:
        x = x[:, ::-1, ...]
    else:
      # 'RGB'->'BGR'
      x = x[..., ::-1]
    mean = [103.939, 116.779, 123.68]
    std = None

  if _IMAGENET_MEAN is None:
    _IMAGENET_MEAN = constant_op.constant(-np.array(mean), dtype=K.floatx())

  # Zero-center by mean pixel
  if K.dtype(x) != K.dtype(_IMAGENET_MEAN):
    x = K.bias_add(x, math_ops.cast(_IMAGENET_MEAN, K.dtype(x)), data_format)
  else:
    x = K.bias_add(x, _IMAGENET_MEAN, data_format)
  if std is not None:
    x /= std
  return x

def sparse_categorical_accuracy(y_true, y_pred):
  # If the shape of y_true is (num_samples, 1), squeeze to (num_samples,)
  if (len(K.int_shape(y_true)) == len(K.int_shape(y_pred))):
    y_true = array_ops.squeeze(y_true, [-1])
  y_pred = math_ops.argmax(y_pred, axis=-1)

  # If the predicted output and actual output types don't match, force cast them
  # to match.
  if K.dtype(y_pred) != K.dtype(y_true):
    y_pred = math_ops.cast(y_pred, K.dtype(y_true))

  return math_ops.cast(math_ops.equal(y_true, y_pred), K.floatx())

  def get_updates(self, loss, params):
    grads = self.get_gradients(loss, params)
    shapes = [K.int_shape(p) for p in params]
    accumulators = [K.zeros(shape) for shape in shapes]
    delta_accumulators = [K.zeros(shape) for shape in shapes]
    self.weights = accumulators + delta_accumulators
    self.updates = [state_ops.assign_add(self.iterations, 1)]

    lr = self.lr
    if self.initial_decay > 0:
      lr = lr * (  # pylint: disable=g-no-augmented-assignment
          1. / (1. + self.decay * math_ops.cast(self.iterations,
                                                K.dtype(self.decay))))

    for p, g, a, d_a in zip(params, grads, accumulators, delta_accumulators):
      # update accumulator
      new_a = self.rho * a + (1. - self.rho) * math_ops.square(g)
      self.updates.append(state_ops.assign(a, new_a))

      # use the new accumulator and the *old* delta_accumulator
      update = g * K.sqrt(d_a + self.epsilon) / K.sqrt(new_a + self.epsilon)
      new_p = p - lr * update

      # Apply constraints.
      if getattr(p, 'constraint', None) is not None:
        new_p = p.constraint(new_p)

      self.updates.append(state_ops.assign(p, new_p))

      # update delta_accumulator
      new_d_a = self.rho * d_a + (1 - self.rho) * math_ops.square(update)
      self.updates.append(state_ops.assign(d_a, new_d_a))
    return self.updates

def sparse_categorical_accuracy(y_true, y_pred):
  y_true = math_ops.reduce_max(y_true, axis=-1)
  y_pred = math_ops.argmax(y_pred, axis=-1)

  # If the expected labels are float, we need to cast the int returned by
  # argmax to compare.
  if K.dtype(y_true) == K.floatx():
    y_pred = math_ops.cast(y_pred, K.floatx())

  return math_ops.cast(math_ops.equal(y_true, y_pred), K.floatx())

def sparse_categorical_accuracy(y_true, y_pred):
  # If the shape of y_true is (num_samples, 1), squeeze to (num_samples,)
  if (len(K.int_shape(y_true)) == len(K.int_shape(y_pred))):
    y_true = array_ops.squeeze(y_true, [-1])
  y_pred = math_ops.argmax(y_pred, axis=-1)

  # If the expected labels are float, we need to cast the int returned by
  # argmax to compare.
  if K.dtype(y_true) == K.floatx():
    y_pred = math_ops.cast(y_pred, K.floatx())

  return math_ops.cast(math_ops.equal(y_true, y_pred), K.floatx())

  def __init__(self,
               input_shape=None,
               batch_size=None,
               dtype=None,
               input_tensor=None,
               sparse=False,
               name=None,
               **kwargs):
    if 'batch_input_shape' in kwargs:
      batch_input_shape = kwargs.pop('batch_input_shape')
      if input_shape and batch_input_shape:
        raise ValueError('Only provide the input_shape OR '
                         'batch_input_shape argument to '
                         'InputLayer, not both at the same time.')
      batch_size = batch_input_shape[0]
      input_shape = batch_input_shape[1:]
    if kwargs:
      raise ValueError('Unrecognized keyword arguments:', kwargs.keys())

    if not name:
      prefix = 'input'
      name = prefix + '_' + str(K.get_uid(prefix))

    if not dtype:
      if input_tensor is None:
        dtype = K.floatx()
      else:
        dtype = K.dtype(input_tensor)
    super(InputLayer, self).__init__(dtype=dtype, name=name)
    self.built = True
    self.sparse = sparse
    self.batch_size = batch_size

    if isinstance(input_shape, tensor_shape.TensorShape):
      input_shape = tuple(input_shape.as_list())

    if input_tensor is None:
      if input_shape is not None:
        batch_input_shape = (batch_size,) + tuple(input_shape)
      else:
        batch_input_shape = None

      if context.executing_eagerly():
        # In eager mode, create a temporary placeholder to call the layer on.
        input_tensor = base_layer.DeferredTensor(  # pylint: disable=protected-access
            shape=batch_input_shape,
            dtype=dtype,
            name=self.name)
      else:
        # In graph mode, create a graph placeholder to call the layer on.
        if sparse:
          input_tensor = array_ops.sparse_placeholder(
              shape=batch_input_shape,
              dtype=dtype,
              name=self.name)
        else:
          input_tensor = array_ops.placeholder(
              shape=batch_input_shape,
              dtype=dtype,
              name=self.name)

      # For compatibility with Keras API.
      self.is_placeholder = True
      self._batch_input_shape = batch_input_shape
    else:
      # For compatibility with Keras API.
      self.is_placeholder = False
      self._batch_input_shape = tuple(input_tensor.get_shape().as_list())

      if context.executing_eagerly():
        raise ValueError('You should not pass an input tensor when executing '
                         'in eager mode. For example, instead of creating an '
                         'InputLayer, you should instantiate your model and '
                         'directly call it on your input.')

    # Create an input node to add to self.outbound_node
    # and set output_tensors' _keras_history.
    input_tensor._keras_history = (self, 0, 0)  # pylint: disable=protected-access
    base_layer.Node(
        self,
        inbound_layers=[],
        node_indices=[],
        tensor_indices=[],
        input_tensors=[input_tensor],
        output_tensors=[input_tensor])

 def call(self, inputs):
   dtype = K.dtype(inputs)
   if dtype != 'int32' and dtype != 'int64':
     inputs = math_ops.cast(inputs, 'int32')
   out = embedding_ops.embedding_lookup(self.embeddings, inputs)
   return out

  def __init__(self,
               input_shape=None,
               batch_size=None,
               dtype=None,
               input_tensor=None,
               sparse=False,
               name=None,
               **kwargs):
    if 'batch_input_shape' in kwargs:
      batch_input_shape = kwargs.pop('batch_input_shape')
      if input_shape and batch_input_shape:
        raise ValueError('Only provide the input_shape OR '
                         'batch_input_shape argument to '
                         'InputLayer, not both at the same time.')
      batch_size = batch_input_shape[0]
      input_shape = batch_input_shape[1:]
    if kwargs:
      raise ValueError('Unrecognized keyword arguments:', kwargs.keys())

    if not name:
      prefix = 'input'
      name = prefix + '_' + str(backend.get_uid(prefix))

    if not dtype:
      if input_tensor is None:
        dtype = backend.floatx()
      else:
        dtype = backend.dtype(input_tensor)
    super(InputLayer, self).__init__(dtype=dtype, name=name)
    self.built = True
    self.sparse = sparse
    self.batch_size = batch_size
    self.supports_masking = True

    if isinstance(input_shape, tensor_shape.TensorShape):
      input_shape = tuple(input_shape.as_list())

    if input_tensor is None:
      if input_shape is not None:
        batch_input_shape = (batch_size,) + tuple(input_shape)
      else:
        batch_input_shape = None
      graph = backend.get_graph()
      with graph.as_default():
        # In graph mode, create a graph placeholder to call the layer on.
        if sparse:
          input_tensor = backend.placeholder(
              shape=batch_input_shape,
              dtype=dtype,
              name=self.name,
              sparse=True)
        else:
          input_tensor = backend.placeholder(
              shape=batch_input_shape,
              dtype=dtype,
              name=self.name)

      self.is_placeholder = True
      self._batch_input_shape = batch_input_shape
    else:
      if not tf_utils.is_symbolic_tensor(input_tensor):
        raise ValueError('You should not pass an EagerTensor to `Input`. '
                         'For example, instead of creating an '
                         'InputLayer, you should instantiate your model and '
                         'directly call it on your input.')
      self.is_placeholder = False
      self._batch_input_shape = tuple(input_tensor.get_shape().as_list())

    # Create an input node to add to self.outbound_node
    # and set output_tensors' _keras_history.
    input_tensor._keras_history = (self, 0, 0)  # pylint: disable=protected-access
    base_layer.Node(
        self,
        inbound_layers=[],
        node_indices=[],
        tensor_indices=[],
        input_tensors=[input_tensor],
        output_tensors=[input_tensor])

  def call(self, inputs):
    if K.dtype(inputs) != 'int32':
      inputs = math_ops.cast(inputs, 'int32')

    inputs = array_ops.one_hot(inputs, self.input_dim)
    return math_ops.tensordot(inputs, self.embeddings, 1)

  def __init__(self,
               input_shape=None,
               batch_size=None,
               dtype=None,
               input_tensor=None,
               sparse=False,
               name=None,
               **kwargs):
    strategy = distribution_strategy_context.get_strategy()
    if strategy and batch_size is not None and \
        distributed_training_utils.global_batch_size_supported(strategy):
      if batch_size % strategy.num_replicas_in_sync != 0:
        raise ValueError('The `batch_size` argument value {} cannot be '
                         'divisible by number of replicas {}'.format(
                             batch_size, strategy.num_replicas_in_sync))
      batch_size = batch_size // strategy.num_replicas_in_sync

    if 'batch_input_shape' in kwargs:
      batch_input_shape = kwargs.pop('batch_input_shape')
      if input_shape and batch_input_shape:
        raise ValueError('Only provide the input_shape OR '
                         'batch_input_shape argument to '
                         'InputLayer, not both at the same time.')
      batch_size = batch_input_shape[0]
      input_shape = batch_input_shape[1:]
    if kwargs:
      raise ValueError('Unrecognized keyword arguments:', kwargs.keys())

    if not name:
      prefix = 'input'
      name = prefix + '_' + str(backend.get_uid(prefix))

    if not dtype:
      if input_tensor is None:
        dtype = backend.floatx()
      else:
        dtype = backend.dtype(input_tensor)
    elif input_tensor is not None and input_tensor.dtype != dtype:
      raise ValueError('`input_tensor.dtype` differs from `dtype`: %s vs. %s' %
                       (input_tensor.dtype, dtype))
    super(InputLayer, self).__init__(dtype=dtype, name=name)
    self.built = True
    self.sparse = sparse
    self.batch_size = batch_size
    self.supports_masking = True

    if isinstance(input_shape, tensor_shape.TensorShape):
      input_shape = tuple(input_shape.as_list())
    elif isinstance(input_shape, int):
      input_shape = (input_shape,)

    if input_tensor is None:
      if input_shape is not None:
        batch_input_shape = (batch_size,) + tuple(input_shape)
      else:
        batch_input_shape = None
      graph = backend.get_graph()
      with graph.as_default():
        # In graph mode, create a graph placeholder to call the layer on.
        if sparse:
          input_tensor = backend.placeholder(
              shape=batch_input_shape,
              dtype=dtype,
              name=self.name,
              sparse=True)
        else:
          input_tensor = backend.placeholder(
              shape=batch_input_shape,
              dtype=dtype,
              name=self.name)

      self.is_placeholder = True
      self._batch_input_shape = batch_input_shape
    else:
      if not tf_utils.is_symbolic_tensor(input_tensor):
        raise ValueError('You should not pass an EagerTensor to `Input`. '
                         'For example, instead of creating an '
                         'InputLayer, you should instantiate your model and '
                         'directly call it on your input.')
      self.is_placeholder = False
      self._batch_input_shape = tuple(input_tensor.shape.as_list())

    # Create an input node to add to self.outbound_node
    # and set output_tensors' _keras_history.
    input_tensor._keras_history = (self, 0, 0)  # pylint: disable=protected-access
    input_tensor._keras_mask = None
    base_layer.Node(
        self,
        inbound_layers=[],
        node_indices=[],
        tensor_indices=[],
        input_tensors=[input_tensor],
        output_tensors=[input_tensor])

def layer_test(layer_cls, kwargs={}, input_shape=None, input_dtype=None,

               input_data=None, expected_output=None,

               expected_output_dtype=None, fixed_batch_size=False):
    # generate input data

    if input_data is None:

        if not input_shape:
            raise AssertionError()

        if not input_dtype:

            input_dtype = K.floatx()

        input_data_shape = list(input_shape)

        for i, e in enumerate(input_data_shape):

            if e is None:

                input_data_shape[i] = np.random.randint(1, 4)

        if all(isinstance(e, tuple) for e in input_data_shape):
            input_data = []
            for e in input_data_shape:
                input_data.append(
                    (10 * np.random.random(e)).astype(input_dtype))

        else:

            input_data = (10 * np.random.random(input_data_shape))

            input_data = input_data.astype(input_dtype)

    else:

        if input_shape is None:

            input_shape = input_data.shape

        if input_dtype is None:

            input_dtype = input_data.dtype

    if expected_output_dtype is None:

        expected_output_dtype = input_dtype

    # instantiation

    layer = layer_cls(**kwargs)

    # test get_weights , set_weights at layer level

    weights = layer.get_weights()

    layer.set_weights(weights)

    try:
        expected_output_shape = layer.compute_output_shape(input_shape)
    except Exception:
        expected_output_shape = layer._compute_output_shape(input_shape)

    # test in functional API
    if isinstance(input_shape, list):
        if fixed_batch_size:

            x = [Input(batch_shape=e, dtype=input_dtype) for e in input_shape]

        else:

            x = [Input(shape=e[1:], dtype=input_dtype) for e in input_shape]
    else:
        if fixed_batch_size:

            x = Input(batch_shape=input_shape, dtype=input_dtype)

        else:

            x = Input(shape=input_shape[1:], dtype=input_dtype)

    y = layer(x)

    if not (K.dtype(y) == expected_output_dtype):
        raise AssertionError()

    # check with the functional API

    model = Model(x, y)

    actual_output = model.predict(input_data)

    actual_output_shape = actual_output.shape

    for expected_dim, actual_dim in zip(expected_output_shape,

                                        actual_output_shape):

#.........这里部分代码省略.........