Python tensorflow.keras.backend.sigmoid() Examples

def __init__(self,
                 ratio,
                 return_mask=False,
                 sigmoid_gating=False,
                 kernel_initializer='glorot_uniform',
                 kernel_regularizer=None,
                 kernel_constraint=None,
                 **kwargs):
        super().__init__(**kwargs)
        self.ratio = ratio
        self.return_mask = return_mask
        self.sigmoid_gating = sigmoid_gating
        self.gating_op = K.sigmoid if self.sigmoid_gating else K.tanh
        self.kernel_initializer = initializers.get(kernel_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint) 

def get_swish(**kwargs):
    def swish(x):
        """Swish activation function: x * sigmoid(x).
        Reference: [Searching for Activation Functions](https://arxiv.org/abs/1710.05941)
        """

        if backend.backend() == 'tensorflow':
            try:
                # The native TF implementation has a more
                # memory-efficient gradient implementation
                return backend.tf.nn.swish(x)
            except AttributeError:
                pass

        return x * backend.sigmoid(x)
    return swish 

def swish(x):
    """Swish activation function.
    # Arguments
        x: Input tensor.
    # Returns
        The Swish activation: `x * sigmoid(x)`.
    # References
        [Searching for Activation Functions](https://arxiv.org/abs/1710.05941)
    """
    if K.backend() == 'tensorflow':
        try:
            # The native TF implementation has a more
            # memory-efficient gradient implementation
            return K.tf.nn.swish(x)
        except AttributeError:
            pass

    return x * K.sigmoid(x) 

def call(self, inputs, **kwargs):
        assert isinstance(inputs, list) and len(inputs) == 3
        first, second, features = inputs[0], inputs[1], inputs[2]
        if not self.from_logits:
            first = K.clip(first, 1e-10, 1.0)
            second = K.clip(second, 1e-10, 1.0)
            first_, second_ = K.log(first), K.log(second)
        else:
            first_, second_ = first, second
        # embedded_features.shape = (M, T, 1)
        if self.use_intermediate_layer:
            features = K.dot(features, self.first_kernel)
            features = K.bias_add(features, self.first_bias, data_format="channels_last")
            features = self.intermediate_activation(features)
        embedded_features = K.dot(features, self.features_kernel)
        embedded_features = K.bias_add(
            embedded_features, self.features_bias, data_format="channels_last")
        if self.use_dimension_bias:
            tiling_shape = [1] * (K.ndim(first) - 1) + [K.shape(first)[-1]]
            embedded_features = K.tile(embedded_features, tiling_shape)
            embedded_features = K.bias_add(
                embedded_features, self.dimensions_bias, data_format="channels_last")
        sigma = K.sigmoid(embedded_features)

        result = weighted_sum(first_, second_, sigma,
                              self.first_threshold, self.second_threshold)
        probs = K.softmax(result)
        if self.return_logits:
            return [probs, result]
        return probs 

def _ctdet_decode(hm, reg, wh, k=100, output_stride=4):
  hm = K.sigmoid(hm)
  hm = _nms(hm)
  hm_shape = K.shape(hm)
  reg_shape = K.shape(reg)
  wh_shape = K.shape(wh)
  batch, width, cat = hm_shape[0], hm_shape[2], hm_shape[3]

  hm_flat = K.reshape(hm, (batch, -1))
  reg_flat = K.reshape(reg, (reg_shape[0], -1, reg_shape[-1]))
  wh_flat = K.reshape(wh, (wh_shape[0], -1, wh_shape[-1]))

  def _process_sample(args):
    _hm, _reg, _wh = args
    _scores, _inds = tf.math.top_k(_hm, k=k, sorted=True)
    _classes = K.cast(_inds % cat, 'float32')
    _inds = K.cast(_inds / cat, 'int32')
    _xs = K.cast(_inds % width, 'float32')
    _ys = K.cast(K.cast(_inds / width, 'int32'), 'float32')
    _wh = K.gather(_wh, _inds)
    _reg = K.gather(_reg, _inds)

    _xs = _xs + _reg[..., 0]
    _ys = _ys + _reg[..., 1]

    _x1 = _xs - _wh[..., 0] / 2
    _y1 = _ys - _wh[..., 1] / 2
    _x2 = _xs + _wh[..., 0] / 2
    _y2 = _ys + _wh[..., 1] / 2

    # rescale to image coordinates
    _x1 = output_stride * _x1
    _y1 = output_stride * _y1
    _x2 = output_stride * _x2
    _y2 = output_stride * _y2

    _detection = K.stack([_x1, _y1, _x2, _y2, _scores, _classes], -1)
    return _detection

  detections = K.map_fn(_process_sample, [hm_flat, reg_flat, wh_flat], dtype=K.floatx())
  return detections 

def yolo3_head(feats, anchors, num_classes, input_shape, calc_loss=False):
    """Convert final layer features to bounding box parameters."""
    num_anchors = len(anchors)
    # Reshape to batch, height, width, num_anchors, box_params.
    anchors_tensor = K.reshape(K.constant(anchors), [1, 1, 1, num_anchors, 2])

    grid_shape = K.shape(feats)[1:3] # height, width
    grid_y = K.tile(K.reshape(K.arange(0, stop=grid_shape[0]), [-1, 1, 1, 1]),
        [1, grid_shape[1], 1, 1])
    grid_x = K.tile(K.reshape(K.arange(0, stop=grid_shape[1]), [1, -1, 1, 1]),
        [grid_shape[0], 1, 1, 1])
    grid = K.concatenate([grid_x, grid_y])
    grid = K.cast(grid, K.dtype(feats))

    feats = K.reshape(
        feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5])

    # Adjust preditions to each spatial grid point and anchor size.
    box_xy = (K.sigmoid(feats[..., :2]) + grid) / K.cast(grid_shape[..., ::-1], K.dtype(feats))
    box_wh = K.exp(feats[..., 2:4]) * anchors_tensor / K.cast(input_shape[..., ::-1], K.dtype(feats))
    box_confidence = K.sigmoid(feats[..., 4:5])
    box_class_probs = K.sigmoid(feats[..., 5:])

    if calc_loss == True:
        return grid, feats, box_xy, box_wh
    return box_xy, box_wh, box_confidence, box_class_probs 

def yolo2_head(feats, anchors, num_classes, input_shape, calc_loss=False):
    """Convert final layer features to bounding box parameters."""
    num_anchors = len(anchors)
    # Reshape to batch, height, width, num_anchors, box_params.
    anchors_tensor = K.reshape(K.constant(anchors), [1, 1, 1, num_anchors, 2])

    grid_shape = K.shape(feats)[1:3] # height, width
    grid_y = K.tile(K.reshape(K.arange(0, stop=grid_shape[0]), [-1, 1, 1, 1]),
        [1, grid_shape[1], 1, 1])
    grid_x = K.tile(K.reshape(K.arange(0, stop=grid_shape[1]), [1, -1, 1, 1]),
        [grid_shape[0], 1, 1, 1])
    grid = K.concatenate([grid_x, grid_y])
    grid = K.cast(grid, K.dtype(feats))

    feats = K.reshape(
        feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5])

    # Adjust preditions to each spatial grid point and anchor size.
    box_xy = (K.sigmoid(feats[..., :2]) + grid) / K.cast(grid_shape[..., ::-1], K.dtype(feats))
    #box_wh = K.exp(feats[..., 2:4]) * anchors_tensor / K.cast(grid_shape[..., ::-1], K.dtype(feats))
    box_wh = K.exp(feats[..., 2:4]) * anchors_tensor / K.cast(input_shape[..., ::-1], K.dtype(feats))
    box_confidence = K.sigmoid(feats[..., 4:5])
    box_class_probs = K.softmax(feats[..., 5:])

    if calc_loss == True:
        return grid, feats, box_xy, box_wh
    return box_xy, box_wh, box_confidence, box_class_probs