Python tensorflow.python.ops.math_ops 模块，mod() 实例源码

def adjust_hue(image, delta, name=None):
    with ops.op_scope([image], name, 'adjust_hue') as name:
        # Remember original dtype to so we can convert back if needed
        orig_dtype = image.dtype
        flt_image = tf.image.convert_image_dtype(image, tf.float32)

        hsv = gen_image_ops.rgb_to_hsv(flt_image)

        hue = tf.slice(hsv, [0, 0, 0, 0], [-1, -1, -1, 1])
        saturation = tf.slice(hsv, [0, 0, 0, 1], [-1, -1, -1, 1])
        value = tf.slice(hsv, [0, 0, 0, 2], [-1, -1, -1, 1])

        # Note that we add 2*pi to guarantee that the resulting hue is a positive
        # floating point number since delta is [-0.5, 0.5].
        hue = math_ops.mod(hue + (delta + 1.), 1.)

        hsv_altered = tf.concat(3, [hue, saturation, value])
        rgb_altered = gen_image_ops.hsv_to_rgb(hsv_altered)

        return tf.image.convert_image_dtype(rgb_altered, orig_dtype)

def _shard_indices(self, keys):
    if self._key_dtype == dtypes.string:
      indices = string_ops.string_to_hash_bucket_fast(keys, self._num_shards)
    else:
      indices = math_ops.mod(keys, self._num_shards)
    return math_ops.cast(indices, dtypes.int32)

def insert_transformed_feature(self, columns_to_tensors):
    """Handles sparse column to id conversion."""
    sparse_id_values = math_ops.mod(columns_to_tensors[self.name].values,
                                    self.bucket_size,
                                    name="mod")
    columns_to_tensors[self] = ops.SparseTensor(
        columns_to_tensors[self.name].indices, sparse_id_values,
        columns_to_tensors[self.name].shape)

def _shard_indices(self, keys):
    key_shape = keys.get_shape()
    if key_shape.ndims > 1:
      # If keys are a matrix (i.e. a single key is a vector), we use the first
      # element of each key vector to determine the shard.
      keys = array_ops.slice(keys, [0, 0], [key_shape[0].value, 1])
      keys = array_ops.reshape(keys, [-1])
    indices = math_ops.mod(math_ops.abs(keys), self._num_shards)
    return math_ops.cast(indices, dtypes.int32)

def insert_transformed_feature(self, columns_to_tensors):
    """Handles sparse column to id conversion."""
    input_tensor = self._get_input_sparse_tensor(columns_to_tensors)

    sparse_id_values = math_ops.mod(input_tensor.values, self.bucket_size,
                                    name="mod")
    columns_to_tensors[self] = sparse_tensor_py.SparseTensor(
        input_tensor.indices, sparse_id_values, input_tensor.shape)

def __mod__(self, other):
    return mod(self, other)

def testFloat(self):
    x = [0.5, 0.7, 0.3]
    for dtype in [np.float32, np.double]:
      # Test scalar and vector versions.
      for denom in [x[0], [x[0]] * 3]:
        x_np = np.array(x, dtype=dtype)
        with self.test_session():
          x_tf = constant_op.constant(x_np, shape=x_np.shape)
          y_tf = math_ops.mod(x_tf, denom)
          y_tf_np = y_tf.eval()
          y_np = np.fmod(x_np, denom)
        self.assertAllClose(y_tf_np, y_np, atol=1e-2)

def testFixed(self):
    x = [5, 10, 23]
    for dtype in [np.int32, np.int64]:
      # Test scalar and vector versions.
      for denom in [x[0], x]:
        x_np = np.array(x, dtype=dtype)
        with self.test_session():
          x_tf = constant_op.constant(x_np, shape=x_np.shape)
          y_tf = math_ops.mod(x_tf, denom)
          y_tf_np = y_tf.eval()
          y_np = np.mod(x_np, denom)
        self.assertAllClose(y_tf_np, y_np)

def _shard_indices(self, keys):
    key_shape = keys.get_shape()
    if key_shape.ndims > 1:
      # If keys are a matrix (i.e. a single key is a vector), we use the first
      # element of each key vector to determine the shard.
      keys = array_ops.slice(keys, [0, 0], [key_shape[0].value, 1])
      keys = array_ops.reshape(keys, [-1])
    indices = math_ops.mod(math_ops.abs(keys), self._num_shards)
    return math_ops.cast(indices, dtypes.int32)

def insert_transformed_feature(self, columns_to_tensors):
    """Handles sparse column to id conversion."""
    input_tensor = self._get_input_sparse_tensor(columns_to_tensors)

    sparse_id_values = math_ops.mod(input_tensor.values, self.bucket_size,
                                    name="mod")
    columns_to_tensors[self] = sparse_tensor_py.SparseTensor(
        input_tensor.indices, sparse_id_values, input_tensor.dense_shape)

def setUp(self):
    super(CoreBinaryOpsTest, self).setUp()

    self.x_probs_broadcast_tensor = array_ops.reshape(
        self.x_probs_lt.tensor, [self.x_size, 1, self.probs_size])

    self.channel_probs_broadcast_tensor = array_ops.reshape(
        self.channel_probs_lt.tensor, [1, self.channel_size, self.probs_size])

    # == and != are not element-wise for tf.Tensor, so they shouldn't be
    # elementwise for LabeledTensor, either.
    self.ops = [
        ('add', operator.add, math_ops.add, core.add),
        ('sub', operator.sub, math_ops.subtract, core.sub),
        ('mul', operator.mul, math_ops.multiply, core.mul),
        ('div', operator.truediv, math_ops.div, core.div),
        ('mod', operator.mod, math_ops.mod, core.mod),
        ('pow', operator.pow, math_ops.pow, core.pow_function),
        ('equal', None, math_ops.equal, core.equal),
        ('less', operator.lt, math_ops.less, core.less),
        ('less_equal', operator.le, math_ops.less_equal, core.less_equal),
        ('not_equal', None, math_ops.not_equal, core.not_equal),
        ('greater', operator.gt, math_ops.greater, core.greater),
        ('greater_equal', operator.ge, math_ops.greater_equal,
         core.greater_equal),
    ]
    self.test_lt_1 = self.x_probs_lt
    self.test_lt_2 = self.channel_probs_lt
    self.test_lt_1_broadcast = self.x_probs_broadcast_tensor
    self.test_lt_2_broadcast = self.channel_probs_broadcast_tensor
    self.broadcast_axes = [self.a0, self.a1, self.a3]

def __mod__(self, other):
    return mod(self, other)

def __rmod__(self, other):
    return mod(other, self)