Python backend.clip方法代码示例

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import clip [as 别名]
def focal_loss_binary(y_true, y_pred):
    """Binary cross-entropy focal loss
    """
    gamma = 2.0
    alpha = 0.25

    pt_1 = tf.where(tf.equal(y_true, 1),
                    y_pred,
                    tf.ones_like(y_pred))
    pt_0 = tf.where(tf.equal(y_true, 0),
                    y_pred,
                    tf.zeros_like(y_pred))

    epsilon = K.epsilon()
    # clip to prevent NaN and Inf
    pt_1 = K.clip(pt_1, epsilon, 1. - epsilon)
    pt_0 = K.clip(pt_0, epsilon, 1. - epsilon)

    weight = alpha * K.pow(1. - pt_1, gamma)
    fl1 = -K.sum(weight * K.log(pt_1))
    weight = (1 - alpha) * K.pow(pt_0, gamma)
    fl0 = -K.sum(weight * K.log(1. - pt_0))

    return fl1 + fl0 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import clip [as 别名]
def focal_loss_categorical(y_true, y_pred):
    """Categorical cross-entropy focal loss"""
    gamma = 2.0
    alpha = 0.25

    # scale to ensure sum of prob is 1.0
    y_pred /= K.sum(y_pred, axis=-1, keepdims=True)

    # clip the prediction value to prevent NaN and Inf
    epsilon = K.epsilon()
    y_pred = K.clip(y_pred, epsilon, 1. - epsilon)

    # calculate cross entropy
    cross_entropy = -y_true * K.log(y_pred)

    # calculate focal loss
    weight = alpha * K.pow(1 - y_pred, gamma)
    cross_entropy *= weight

    return K.sum(cross_entropy, axis=-1) 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import clip [as 别名]
def action(self, args):
        """Given mean and stddev, sample an action, clip 
            and return
            We assume Gaussian distribution of probability 
            of selecting an action given a state
        Argument:
            args (list) : mean, stddev list
        Return:
            action (tensor): policy action
        """
        mean, stddev = args
        dist = tfp.distributions.Normal(loc=mean, scale=stddev)
        action = dist.sample(1)
        action = K.clip(action,
                        self.env.action_space.low[0],
                        self.env.action_space.high[0])
        return action 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import clip [as 别名]
def mi_loss(self, y_true, y_pred):
        """ MINE loss function

        Arguments:
            y_true (tensor): Not used since this is
                unsupervised learning
            y_pred (tensor): stack of predictions for
                joint T(x,y) and marginal T(x,y)
        """
        size = self.args.batch_size
        # lower half is pred for joint dist
        pred_xy = y_pred[0: size, :]

        # upper half is pred for marginal dist
        pred_x_y = y_pred[size : y_pred.shape[0], :]
        loss = K.mean(K.exp(pred_x_y))
        loss = K.clip(loss, K.epsilon(), np.finfo(float).max)
        loss = K.mean(pred_xy) - K.log(loss)
        return -loss 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import clip [as 别名]
def mcor(y_true, y_pred):
    # matthews_correlation
    y_pred_pos = K.round(K.clip(y_pred, 0, 1))
    y_pred_neg = 1 - y_pred_pos

    y_pos = K.round(K.clip(y_true, 0, 1))
    y_neg = 1 - y_pos

    tp = K.sum(y_pos * y_pred_pos)
    tn = K.sum(y_neg * y_pred_neg)

    fp = K.sum(y_neg * y_pred_pos)
    fn = K.sum(y_pos * y_pred_neg)

    numerator = (tp * tn - fp * fn)
    denominator = K.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))

    return numerator / (denominator + K.epsilon()) 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import clip [as 别名]
def recall(y_true, y_pred):
    """Precision for foreground pixels.

    Calculates pixelwise recall TP/(TP + FN).

    """
    # count true positives
    truth = K.round(K.clip(y_true, K.epsilon(), 1))
    pred_pos = K.round(K.clip(y_pred, K.epsilon(), 1))
    true_pos = K.sum(K.cast(K.all(K.stack([truth, pred_pos], axis=2), axis=2),
                            dtype='float64'))
    truth_ct = K.sum(K.round(K.clip(y_true, K.epsilon(), 1)))
    if truth_ct == 0:
        return 0
    recall = true_pos/truth_ct

    return recall 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import clip [as 别名]
def call(self, inputs):
        F = K.int_shape(inputs)[-1]
        minkowski_prod_mat = np.eye(F)
        minkowski_prod_mat[-1, -1] = -1.
        minkowski_prod_mat = K.constant(minkowski_prod_mat)
        output = K.dot(inputs, minkowski_prod_mat)
        output = K.dot(output, K.transpose(inputs))
        output = K.clip(output, -10e9, -1.)

        if self.activation is not None:
            output = self.activation(output)

        return output 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import clip [as 别名]
def loss(self, y_true, y_pred):
        """ categorical crossentropy loss """

        if self.crop_indices is not None:
            y_true = utils.batch_gather(y_true, self.crop_indices)
            y_pred = utils.batch_gather(y_pred, self.crop_indices)

        if self.use_float16:
            y_true = K.cast(y_true, 'float16')
            y_pred = K.cast(y_pred, 'float16')

        # scale and clip probabilities
        # this should not be necessary for softmax output.
        y_pred /= K.sum(y_pred, axis=-1, keepdims=True)
        y_pred = K.clip(y_pred, K.epsilon(), 1)

        # compute log probability
        log_post = K.log(y_pred)  # likelihood

        # loss
        loss = - y_true * log_post

        # weighted loss
        if self.weights is not None:
            loss *= self.weights

        if self.vox_weights is not None:
            loss *= self.vox_weights

        # take the total loss
        # loss = K.batch_flatten(loss)
        mloss = K.mean(K.sum(K.cast(loss, 'float32'), -1))
        tf.verify_tensor_all_finite(mloss, 'Loss not finite')
        return mloss 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import clip [as 别名]
def mi_loss(self, y_true, y_pred):
        """Mutual information loss computed from the joint
           distribution matrix and the marginals

        Arguments:
            y_true (tensor): Not used since this is
                unsupervised learning
            y_pred (tensor): stack of softmax predictions for
                the Siamese latent vectors (Z and Zbar)
        """
        size = self.args.batch_size
        n_labels = y_pred.shape[-1]
        # lower half is Z
        Z = y_pred[0: size, :]
        Z = K.expand_dims(Z, axis=2)
        # upper half is Zbar
        Zbar = y_pred[size: y_pred.shape[0], :]
        Zbar = K.expand_dims(Zbar, axis=1)
        # compute joint distribution (Eq 10.3.2 & .3)
        P = K.batch_dot(Z, Zbar)
        P = K.sum(P, axis=0)
        # enforce symmetric joint distribution (Eq 10.3.4)
        P = (P + K.transpose(P)) / 2.0
        # normalization of total probability to 1.0
        P = P / K.sum(P)
        # marginal distributions (Eq 10.3.5 & .6)
        Pi = K.expand_dims(K.sum(P, axis=1), axis=1)
        Pj = K.expand_dims(K.sum(P, axis=0), axis=0)
        Pi = K.repeat_elements(Pi, rep=n_labels, axis=1)
        Pj = K.repeat_elements(Pj, rep=n_labels, axis=0)
        P = K.clip(P, K.epsilon(), np.finfo(float).max)
        Pi = K.clip(Pi, K.epsilon(), np.finfo(float).max)
        Pj = K.clip(Pj, K.epsilon(), np.finfo(float).max)
        # negative MI loss (Eq 10.3.7)
        neg_mi = K.sum((P * (K.log(Pi) + K.log(Pj) - K.log(P))))
        # each head contribute 1/n_heads to the total loss
        return neg_mi/self.args.heads 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import clip [as 别名]
def precision(y_true, y_pred):
    """Precision metric.

    Computes the precision, a metric for multi-label classification of
    how many selected items are relevant. Only computes a batch-wise average of
    precision.
    """

    import tensorflow.keras.backend as k
    true_positives = k.sum(k.round(k.clip(y_true * y_pred, 0, 1)))
    predicted_positives = k.sum(k.round(k.clip(y_pred, 0, 1)))
    return true_positives / (predicted_positives + k.epsilon()) 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import clip [as 别名]
def focal_loss(y_true, y_pred, gamma=2, alpha=0.95):
    pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
    pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))

    pt_1 = K.clip(pt_1, 1e-3, .999)
    pt_0 = K.clip(pt_0, 1e-3, .999)

    return -K.sum(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1)) - K.sum((1-alpha) * K.pow( pt_0, gamma) * K.log(1. - pt_0)) 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import clip [as 别名]
def f1(y_true, y_pred):
    K.set_epsilon(1e-05)
    def recall(y_true, y_pred):
        """Recall metric.

        Only computes a batch-wise average of recall.

        Computes the recall, a metric for multi-label classification of
        how many relevant items are selected.
        """
        K.set_epsilon(1e-05)
        #y_pred = tf.convert_to_tensor(y_pred, np.float32)
        #y_true = tf.convert_to_tensor(y_true, np.float32)
        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
        recall = true_positives / (possible_positives + K.epsilon())
        return recall

    def precision(y_true, y_pred):
        """Precision metric.

        Only computes a batch-wise average of precision.

        Computes the precision, a metric for multi-label classification of
        how many selected items are relevant.
        """
        K.set_epsilon(1e-05)
        #y_pred = tf.convert_to_tensor(y_pred, np.float32)
        #y_true = tf.convert_to_tensor(y_true, np.float32)
        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
        precision = true_positives / (predicted_positives + K.epsilon())
        return precision
    precision = precision(y_true, y_pred)
    recall = recall(y_true, y_pred)
    return 2*((precision*recall)/(precision+recall+K.epsilon())) 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import clip [as 别名]
def precision(y_true, y_pred):
    K.set_epsilon(1e-05)
    #y_pred = tf.convert_to_tensor(y_pred, np.float32)
    #y_true = tf.convert_to_tensor(y_true, np.float32)
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
    precision = true_positives / (predicted_positives + K.epsilon())
    return precision 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import clip [as 别名]
def recall(y_true, y_pred):
    K.set_epsilon(1e-05)
    #y_pred = tf.convert_to_tensor(y_pred, np.float32)
    #y_true = tf.convert_to_tensor(y_true, np.float32)
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
    recall = true_positives / (possible_positives + K.epsilon())
    return recall 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import clip [as 别名]
def _euclidian_dist(self, x_pair: List[Tensor]) -> Tensor:
        x1_norm = K.l2_normalize(x_pair[0], axis=1)
        x2_norm = K.l2_normalize(x_pair[1], axis=1)
        diff = x1_norm - x2_norm
        square = K.square(diff)
        _sum = K.sum(square, axis=1)
        _sum = K.clip(_sum, min_value=1e-12, max_value=None)
        dist = K.sqrt(_sum) / 2.
        return dist