def logloss(y_true, y_pred):
y_pred = ops.convert_to_tensor(y_pred)
y_true = math_ops.cast(y_true, y_pred.dtype)
losses = math_ops.multiply(y_true, math_ops.log(y_pred + K.epsilon()))
losses += math_ops.multiply((1 - y_true),
math_ops.log(1 - y_pred + K.epsilon()))
return K.mean(-losses, axis=-1)
def __call__(self, w):
norms = K.sqrt(
math_ops.reduce_sum(math_ops.square(w), axis=self.axis, keepdims=True))
desired = (
self.rate * K.clip(norms, self.min_value, self.max_value) +
(1 - self.rate) * norms)
return w * (desired / (K.epsilon() + norms))
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.
"""
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 __call__(self, w):
norms = backend.sqrt(
math_ops.reduce_sum(math_ops.square(w),
axis=self.axis,
keepdims=True))
desired = (
self.rate * backend.clip(norms, self.min_value, self.max_value) +
(1 - self.rate) * norms)
return w * (desired / (backend.epsilon() + norms))
def outer_distance_transform_2d(mask,
bins=None,
erosion_width=None,
normalize=True):
"""Transform a label mask with an outer distance transform.
Args:
mask (numpy.array): A label mask (y data).
bins (int): The number of transformed distance classes. If none,
returns the continuous outer transform.
erosion_width (int): Number of pixels to erode edges of each labels
normalize (boolean): Normalize the transform of each cell by that
cell's largest distance.
Returns:
numpy.array: A mask of same shape as input mask,
with each label being a distance class from 1 to bins.
"""
mask = np.squeeze(mask) # squeeze the channels
mask = erode_edges(mask, erosion_width)
distance = ndimage.distance_transform_edt(mask)
distance = distance.astype(K.floatx()) # normalized distances are floats
if normalize:
# uniquely label each cell and normalize the distance values
# by that cells maximum distance value
label_matrix = label(mask)
for prop in regionprops(label_matrix):
labeled_distance = distance[label_matrix == prop.label]
normalized_distance = labeled_distance / np.amax(labeled_distance)
distance[label_matrix == prop.label] = normalized_distance
if bins is None:
return distance
# bin each distance value into a class from 1 to bins
min_dist = np.amin(distance)
max_dist = np.amax(distance)
distance_bins = np.linspace(min_dist - K.epsilon(),
max_dist + K.epsilon(),
num=bins + 1)
distance = np.digitize(distance, distance_bins, right=True)
return distance - 1 # minimum distance should be 0, not 1
def __init__(self,
learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-7,
weight_decay=0.,
amsgrad=False,
total_steps=0,
warmup_proportion=0.1,
min_lr=0.,
name='RAdam',
**kwargs):
r"""Construct a new Adam optimizer.
Args:
learning_rate: A Tensor or a floating point value. The learning rate.
beta_1: A float value or a constant float tensor. The exponential decay
rate for the 1st moment estimates.
beta_2: A float value or a constant float tensor. The exponential decay
rate for the 2nd moment estimates.
epsilon: A small constant for numerical stability. This epsilon is
"epsilon hat" in the Kingma and Ba paper (in the formula just before
Section 2.1), not the epsilon in Algorithm 1 of the paper.
weight_decay: A floating point value. Weight decay for each param.
amsgrad: boolean. Whether to apply AMSGrad variant of this algorithm from
the paper "On the Convergence of Adam and beyond".
total_steps: An integer. Total number of training steps.
Enable warmup by setting a positive value.
warmup_proportion: A floating point value. The proportion of increasing steps.
min_lr: A floating point value. Minimum learning rate after warmup.
name: Optional name for the operations created when applying gradients.
Defaults to "Adam". @compatibility(eager) When eager execution is
enabled, `learning_rate`, `beta_1`, `beta_2`, and `epsilon` can each be
a callable that takes no arguments and returns the actual value to use.
This can be useful for changing these values across different
invocations of optimizer functions. @end_compatibility
**kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`,
`decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is clip
gradients by value, `decay` is included for backward compatibility to
allow time inverse decay of learning rate. `lr` is included for backward
compatibility, recommended to use `learning_rate` instead.
"""
super(RAdam, self).__init__(name, **kwargs)
self._set_hyper('learning_rate', kwargs.get('lr', learning_rate))
self._set_hyper('beta_1', beta_1)
self._set_hyper('beta_2', beta_2)
self._set_hyper('decay', self._initial_decay)
self._set_hyper('weight_decay', weight_decay)
self._set_hyper('total_steps', float(total_steps))
self._set_hyper('warmup_proportion', warmup_proportion)
self._set_hyper('min_lr', min_lr)
self.epsilon = epsilon or K.epsilon()
self.amsgrad = amsgrad
self._initial_weight_decay = weight_decay
self._initial_total_steps = total_steps
def call(self, x):
print(x)
features_dim = x.shape[-1].value
step_dim = x.shape[-2].value
# print(K.reshape(self.kernel, (-1, features_dim))) # n, d
# print(K.reshape(self.W, (features_dim, 1))) # w= dx1
# print(K.dot(K.reshape(self.kernel, (-1, features_dim)), K.reshape(self.W, (features_dim, 1)))) # nx1
eij = K.reshape(
K.dot(K.reshape(self.kernel, (-1, features_dim)),
K.reshape(self.W, (features_dim, 1))),
(-1, step_dim + self.windows))
print(eij)
eij += self.b
eij = K.tanh(eij)
a = K.exp(eij)
a = K.reshape(a, (step_dim + self.windows, 1))
print(a)
temp = a[0:self.windows, ]
print(temp)
temp /= K.cast(
K.sum(temp, axis=0, keepdims=True) + K.epsilon(), K.floatx())
weighted_input = self.kernel[0:self.windows, ] * temp
alltemp = K.sum(weighted_input, axis=0, keepdims=True)
for i in range(self.windows // 2 + 1, step_dim + self.windows // 2):
temp = a[i - self.windows // 2:i + self.windows // 2, ]
temp /= K.cast(
K.sum(temp, axis=0, keepdims=True) + K.epsilon(), K.floatx())
weighted_input = self.kernel[i - self.windows // 2:i +
self.windows // 2, ] * temp
temp = K.sum(weighted_input, axis=0, keepdims=True)
alltemp = keras.layers.concatenate([alltemp, temp], 0)
print(alltemp)
alltemp = keras.activations.tanh(alltemp)
return x + alltemp
def __init__(self, lr=0.01, epsilon=None, decay=0., **kwargs):
super(TruncatedAdagrad, self).__init__(**kwargs)
with K.name_scope(self.__class__.__name__):
self.lr = K.variable(lr, name='lr')
self.decay = K.variable(decay, name='decay')
self.iterations = K.variable(0, dtype='int64', name='iterations')
if epsilon is None:
epsilon = K.epsilon()
self.epsilon = epsilon
self.initial_decay = decay
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.
"""
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
def invert(grads):
"""Inverts the gradients.
Args:
grads: A numpy array of grads to use.
Returns:
The inverted gradients.
"""
return 1. / (grads + K.epsilon())
def _rbox_aabb_loss(ground_truth_aabb, predicted_aabb, EPS=K.epsilon()):
ground_truth_area = _aabb_box_area(ground_truth_aabb)
predicted_area = _aabb_box_area(predicted_aabb)
intersected_area = _aabb_intersected_area(ground_truth_aabb,
predicted_aabb)
union_area = ground_truth_area + predicted_area - intersected_area
# Equivalent to -log(intersected_area / union_area)
return K.log(union_area + EPS) - K.log(intersected_area + EPS)
def root_mean_squared_error(y_true, y_pred):
"""A simple Keras implementation of R^2 that can be used as a Keras
loss function.
Since `score` uses R^2, it is
advisable to use the same loss/metric when optimizing the model.
"""
ss_res = K.sum(K.square(y_true - y_pred), axis=0)
ss_tot = K.sum(K.square(y_true - K.mean(y_true, axis=0)), axis=0)
return K.mean(1 - ss_res / (ss_tot + K.epsilon()), axis=-1)
def _update_s_matrix_stats(self, num_of_complex_params_t, s):
if not self.add_s_matrix_stats:
return tf.no_op(), tf.no_op()
abs_eigvals = tf.math.abs(tf.linalg.eigvalsh(s))
tol = K.epsilon() * tf.cast(num_of_complex_params_t, abs_eigvals.dtype) * tf.math.reduce_max(
tf.math.abs(s)) # see https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.matrix_rank.html
filtered_eigvals = tf.boolean_mask(abs_eigvals, abs_eigvals > tol)
updated_s_matrix_rank = K.update(self.s_matrix_rank, tf.count_nonzero(filtered_eigvals))
updated_s_matrix_min_eigval = K.update(self.s_matrix_min_eigval, tf.math.reduce_min(filtered_eigvals))
return updated_s_matrix_min_eigval, updated_s_matrix_rank
def __init__(self, lr=0.01, epsilon=None, decay=0., **kwargs):
super(Adagrad, self).__init__(**kwargs)
with K.name_scope(self.__class__.__name__):
self.lr = K.variable(lr, name='lr')
self.decay = K.variable(decay, name='decay')
self.iterations = K.variable(0, dtype='int64', name='iterations')
if epsilon is None:
epsilon = K.epsilon()
self.epsilon = epsilon
self.initial_decay = decay
def f1_score(y_true, y_pred):
"""Computes the F1 Score
Only computes a batch-wise average of recall. Computes the recall, a metric
for multi-label classification of how many relevant items are selected.
"""
# print(y_true, y_pred)
# print(y_true.shape, y_pred.shape)
p = precision(y_true, y_pred)
r = recall(y_true, y_pred)
return (2 * p * r) / (p + r + K.epsilon())
def normalize(x):
"""utility function to normalize a tensor.
# Arguments
x: An input tensor.
# Returns
The normalized input tensor.
"""
return x / (K.sqrt(K.mean(K.square(x))) + K.epsilon())
def ssd_loss(y_true, y_pred):
num_classes = 11 # tf.shape(y_true)[2] - 4 # openCV에서 동적 shape를 지원안함.
y_true = tf.reshape(y_true, [-1, num_classes + 4])
y_pred = tf.reshape(y_pred, [-1, num_classes + 4])
eps = K.epsilon()
# Split Classification and Localization output
y_true_clf, y_true_loc = tf.split(y_true, [num_classes, 4], axis=-1)
y_pred_clf, y_pred_loc = tf.split(y_pred, [num_classes, 4], axis=-1)
# split foreground & background
mask = y_true_clf[:, -1]
if ignore_match:
# ignore match의 경우
# y_true_clf[:, -1]의 값이 {0,1}이 아닌 다른 값으로 채워져 있음
# y_true_clf의 경우, 이후 softmax 계산할 때 값이 {0,1}사이에 매칭되지 않은 경우,
# NaN을 반환할 수 있어, y_true_clf 내 ignore match된 값들을 다시 1로 바꾸어줌
neg_mask = tf.where(tf.equal(mask, 1.),
tf.ones_like(mask),
tf.zeros_like(mask))
pos_mask = tf.where(tf.equal(mask, 0.),
tf.ones_like(mask),
tf.zeros_like(mask))
y_true_clf = tf.where(tf.not_equal(y_true_clf, 0),
tf.ones_like(y_true_clf),
tf.zeros_like(y_true_clf))
else:
neg_mask = mask
pos_mask = 1 - mask
num_pos = tf.reduce_sum(pos_mask)
num_neg = tf.reduce_sum(neg_mask)
num_neg = tf.minimum(pos_neg_ratio * num_pos, num_neg)
# softmax loss
y_pred_clf = K.clip(y_pred_clf, eps, 1. - eps)
clf_loss = -tf.reduce_sum(y_true_clf * tf.log(y_pred_clf),
axis=-1)
pos_clf_loss = tf.reduce_sum(clf_loss * pos_mask) / (num_pos + eps)
neg_clf_loss = clf_loss * neg_mask
values, indices = tf.nn.top_k(neg_clf_loss,
k=tf.cast(num_neg, tf.int32))
neg_clf_loss = tf.reduce_sum(values) / (num_neg + eps)
clf_loss = pos_clf_loss + neg_clf_loss
# smooth l1 loss
l1_loss = tf.abs(y_true_loc - y_pred_loc)
l2_loss = 0.5 * (y_true_loc - y_pred_loc) ** 2
loc_loss = tf.where(tf.less(l1_loss, 1.0),
l2_loss,
l1_loss - 0.5)
loc_loss = tf.reduce_sum(loc_loss, axis=-1)
loc_loss = tf.reduce_sum(loc_loss * pos_mask) / (num_pos + eps)
# total loss
return clf_loss + alpha * loc_loss
def __init__(self, lr=1.0, rho=0.95, epsilon=None, decay=0., **kwargs):
super(Adadelta, self).__init__(**kwargs)
with K.name_scope(self.__class__.__name__):
self.lr = K.variable(lr, name='lr')
self.decay = K.variable(decay, name='decay')
self.iterations = K.variable(0, dtype='int64', name='iterations')
if epsilon is None:
epsilon = K.epsilon()
self.rho = rho
self.epsilon = epsilon
self.initial_decay = decay
def whale_siamese_image_mean_np(img: np.ndarray) -> np.ndarray:
"""
Test Pass, equal to `whale_siamese_image_mean_tf`
Args:
img:
Returns:
"""
img -= np.mean(img, keepdims=True)
img /= np.std(img, keepdims=True) + K.epsilon()
return img
def root_mean_squared_error(y_true, y_pred):
"""A simple Keras implementation of R^2 that can be used as a Keras
loss function.
Since ScikitLearn's `score` uses R^2 by default, it is
advisable to use the same loss/metric when optimizing the model.
"""
ss_res = k_backend.sum(k_backend.square(y_true - y_pred), axis=0)
ss_tot = k_backend.sum(
k_backend.square(y_true - k_backend.mean(y_true, axis=0)), axis=0)
return k_backend.mean(1 - ss_res / (ss_tot + k_backend.epsilon()),
axis=-1)
def call(inputs, mask=None):
steps_axis = 1
if mask is not None:
mask = math_ops.cast(mask, backend.floatx())
input_shape = inputs.shape.as_list()
broadcast_shape = [-1, input_shape[steps_axis], 1]
mask = array_ops.reshape(mask, broadcast_shape)
inputs *= mask
return backend.sum(inputs, axis=steps_axis) / (
math_ops.reduce_sum(mask, axis=steps_axis) + backend.epsilon())
else:
return backend.mean(inputs, axis=steps_axis)
def artifact_precision(y_true, y_pred):
weights = y_true[:, :, :, :, 2]
mask = tf.equal(weights, 1)
mask_true = tf.boolean_mask(y_true[:, :, :, :, 2], mask)
mask_pred = tf.boolean_mask(1 - y_pred[:, :, :, :, 0], mask)
true_positives = K.sum(K.round(K.clip(mask_true * mask_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(mask_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
def r2_score(y_true, y_pred):
"""
Adds coefficient of determination metric
:param y_true: The ground truth output tensorr, same dimensions as 'labels'
:param y_pred: The output from the neural network
:return: Weighted loss float Tensor
"""
'''
https://jmlb.github.io/ml/2017/03/20/CoeffDetermination_CustomMetric4Keras/
'''
SS_res = K.sum(K.square(y_true - y_pred))
SS_tot = K.sum(K.square(y_true - K.mean(y_true)))
return 1 - SS_res / (SS_tot + K.epsilon())
def __init__(self, lr=0.001, rho=0.9, epsilon=None, decay=0., **kwargs):
super(RMSprop, self).__init__(**kwargs)
with backend.name_scope(self.__class__.__name__):
self.lr = backend.variable(lr, name='lr')
self.rho = backend.variable(rho, name='rho')
self.decay = backend.variable(decay, name='decay')
self.iterations = backend.variable(0,
dtype='int64',
name='iterations')
if epsilon is None:
epsilon = backend.epsilon()
self.epsilon = epsilon
self.initial_decay = decay
def generalised_dice_loss(y_true,
y_pred,
type_weight='Uniform'):
"""
Function to calculate the Generalised Dice Loss defined in
Sudre, C. et. al. (2017) Generalised Dice overlap as a deep learning
loss function for highly unbalanced segmentations. DLMIA 2017
:param y_pred: the logits
:param y_true: the segmentation ground truth
:param type_weight: type of weighting allowed between labels (choice
between Square (square of inverse of volume),
Simple (inverse of volume) and Uniform (no weighting))
:return: the loss
"""
# n_el = tf.cast(K.prod(tf.shape(y_pred)), tf.float32)
# those ops need the y_true not to be 0! Although you find nan loss and accuracy
if type_weight == 'Square':
weights_op = lambda x: 1. / (tf.math.pow(K.sum(x, axis=(0, 1, 2)), y=3) + K.epsilon())
elif type_weight == 'Simple':
weights_op = lambda x: 1. / (tf.reduce_sum(x, axis=(0, 1, 2)) + K.epsilon())
elif type_weight == 'Uniform':
weights_op = lambda x: 1.
else:
raise ValueError("The variable type_weight \"{}\""
"is not defined.".format(type_weight))
# treat each class separately
w = weights_op(y_true)
numerator = y_true * y_pred
numerator = w * tf.reduce_sum(numerator, (0, 1, 2))
numerator = tf.reduce_sum(numerator)
denominator = tf.reduce_sum(y_true, (0, 1, 2)) + tf.reduce_sum(y_pred, (0, 1, 2))
denominator = w * denominator
denominator = tf.reduce_sum(denominator)
gen_dice_coef = numerator / denominator
# generalised_dice_score = 2. * num / denom
return 1. - 2. * gen_dice_coef
def call(self, x, mask=None):
features_dim = self.features_dim
step_dim = self.step_dim
t1 = x[:, 0, :]
t1 = K.expand_dims(t1, 1)
# t1 = K.tile(t1, [1, step_dim, 1])
print(t1)
eij = K.batch_dot(x, t1, (2, 2)) #(?,500,1)
# eij = K.tile(eij, [1, 1, features_dim])
print(eij)
a = K.exp(eij)
a /= K.cast(K.sum(a, axis=1, keepdims=True) + K.epsilon(), K.floatx())
print(a)
weighted_input = x * a
temp = K.sum(weighted_input, axis=1)
temp = K.expand_dims(temp, 1)
temp = K.tile(temp, [1, 1, features_dim])
print(temp)
alltemp = temp
for i in range(1, step_dim):
t1 = x[:, i, :]
t1 = K.expand_dims(t1, 1)
# t1 = K.tile(t1, [1, 2, 1])
eij = K.batch_dot(x, t1, (2, 2))
# eij = K.tile(eij, [1, 1, features_dim])
a = K.exp(eij)
a /= K.cast(
K.sum(a, axis=1, keepdims=True) + K.epsilon(), K.floatx())
weighted_input = x * a
temp = K.sum(weighted_input, axis=1)
temp = K.expand_dims(temp, 1)
temp = K.tile(temp, [1, 1, features_dim])
alltemp = keras.layers.concatenate([alltemp, temp], 1)
temp = keras.layers.concatenate([x, alltemp])
return temp
def __init__(self,
center=True,
scale=True,
epsilon=None,
gamma_initializer='ones',
beta_initializer='zeros',
gamma_regularizer=None,
beta_regularizer=None,
gamma_constraint=None,
beta_constraint=None,
**kwargs):
"""Layer normalization layer
See: [Layer Normalization](https://arxiv.org/pdf/1607.06450.pdf)
:param center: Add an offset parameter if it is True.
:param scale: Add a scale parameter if it is True.
:param epsilon: Epsilon for calculating variance.
:param gamma_initializer: Initializer for the gamma weight.
:param beta_initializer: Initializer for the beta weight.
:param gamma_regularizer: Optional regularizer for the gamma weight.
:param beta_regularizer: Optional regularizer for the beta weight.
:param gamma_constraint: Optional constraint for the gamma weight.
:param beta_constraint: Optional constraint for the beta weight.
:param kwargs:
"""
super(LayerNormalization, self).__init__(**kwargs)
self.supports_masking = True
self.center = center
self.scale = scale
if epsilon is None:
epsilon = K.epsilon() * K.epsilon()
self.epsilon = epsilon
self.gamma_initializer = initializers.get(gamma_initializer)
self.beta_initializer = initializers.get(beta_initializer)
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.gamma_constraint = constraints.get(gamma_constraint)
self.beta_constraint = constraints.get(beta_constraint)
self.gamma, self.beta = None, None
def expand_dims_f1(y_true, y_pred):
####
y_true = tf.expand_dims(y_true, -1)
y_true = tf.cast(y_true, tf.float32)
####
y_pred = tf.slice(y_pred, [0, 1], [-1, 1])
####
"""F1-score"""
precision = Precision(y_true, y_pred)
#print (" f1--Precision: %s" % (precision))
recall = Recall(y_true, y_pred)
#print (" f1--Recall: %s" % (recall))
f1 = 2 * ((precision * recall) / (precision + recall + K.epsilon()))
return f1
def fbeta_score(y_true, y_pred, beta=1):
# Calculates the F score, the weighted harmonic mean of precision and recall.
if beta < 0:
raise ValueError('The lowest choosable beta is zero (only precision).')
# If there are no true positives, fix the F score at 0 like sklearn.
if K.sum(K.round(K.clip(y_true, 0, 1))) == 0:
return 0
p = precision(y_true, y_pred)
r = recall(y_true, y_pred)
bb = beta**2
fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon())
return fbeta_score
def call(self, inputs):
inputs = ops.convert_to_tensor_v2_with_dispatch(inputs)
if inputs.shape.rank == 1:
inputs = array_ops.expand_dims(inputs, 1)
# If the inputs are not floats, cast them to floats. This avoids issues
# with int-float multiplication and division below.
if inputs.dtype != K.floatx():
inputs = math_ops.cast(inputs, K.floatx())
# We need to reshape the mean and variance data to ensure that Tensorflow
# broadcasts the data correctly.
mean = array_ops.reshape(self.mean, self._broadcast_shape)
variance = array_ops.reshape(self.variance, self._broadcast_shape)
return ((inputs - mean) /
math_ops.maximum(math_ops.sqrt(variance), K.epsilon()))
def axon_precision(y_true, y_pred):
weights = tf.reduce_sum(y_true, axis=-1)
mask = tf.equal(weights, 1)
mask_true = tf.boolean_mask(y_true[:, :, :, :, 0], mask)
mask_pred = tf.boolean_mask(y_pred[:, :, :, :, 0], mask)
true_positives = K.sum(K.round(K.clip(mask_true * mask_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(mask_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
def axon_recall(y_true, y_pred):
weights = tf.reduce_sum(y_true, axis=-1)
mask = tf.equal(weights, 1)
mask_true = tf.boolean_mask(y_true[:, :, :, :, 0], mask)
mask_pred = tf.boolean_mask(y_pred[:, :, :, :, 0], mask)
true_positives = K.sum(K.round(K.clip(mask_true * mask_pred, 0, 1)))
actual_positives = K.sum(K.round(K.clip(mask_true, 0, 1)))
recall = true_positives / (actual_positives + K.epsilon())
return recall
def discriminative_instance_loss_3D(y_true,
y_pred,
delta_v=0.5,
delta_d=1.5,
order=2,
gamma=1e-3):
def temp_norm(ten, axis=-1):
return tf.sqrt(
tf.constant(1e-4, dtype=K.floatx()) +
tf.reduce_sum(tf.square(ten), axis=axis))
# y_pred = tf.divide(y_pred, tf.expand_dims(tf.norm(y_pred, ord = 2, axis = -1), axis = -1))
# Compute variance loss
cells_summed = tf.tensordot(y_true,
y_pred,
axes=[[0, 1, 2, 3], [0, 1, 2, 3]])
n_pixels = tf.cast(tf.count_nonzero(y_true, axis=[0, 1, 2, 3]),
dtype=K.floatx()) + K.epsilon()
n_pixels_expand = tf.expand_dims(n_pixels, axis=1)
mu = tf.divide(cells_summed, n_pixels_expand)
mu_tensor = tf.tensordot(y_true, mu, axes=[[-1], [0]])
L_var_1 = y_pred - mu_tensor
L_var_2 = tf.square(
tf.nn.relu(
temp_norm(L_var_1, axis=-1) -
tf.constant(delta_v, dtype=K.floatx())))
L_var_3 = tf.tensordot(L_var_2, y_true, axes=[[0, 1, 2, 3], [0, 1, 2, 3]])
L_var_4 = tf.divide(L_var_3, n_pixels)
L_var = tf.reduce_mean(L_var_4)
# Compute distance loss
mu_a = tf.expand_dims(mu, axis=0)
mu_b = tf.expand_dims(mu, axis=1)
diff_matrix = tf.subtract(mu_a, mu_b)
L_dist_1 = temp_norm(diff_matrix, axis=-1)
L_dist_2 = tf.square(
tf.nn.relu(tf.constant(2 * delta_d, dtype=K.floatx()) - L_dist_1))
diag = tf.constant(0, dtype=K.floatx()) * tf.diag_part(L_dist_2)
L_dist_3 = tf.matrix_set_diag(L_dist_2, diag)
L_dist = tf.reduce_mean(L_dist_3)
# Compute regularization loss
L_reg = gamma * temp_norm(mu, axis=-1)
L = L_var + L_dist + L_reg
return L
def __init__(self,
lr=0.002,
beta_1=0.9,
beta_2=0.999,
epsilon=None,
decay=0.,
**kwargs):
super(Adamax, self).__init__(**kwargs)
with K.name_scope(self.__class__.__name__):
self.iterations = K.variable(0, dtype='int64', name='iterations')
self.lr = K.variable(lr, name='lr')
self.beta_1 = K.variable(beta_1, name='beta_1')
self.beta_2 = K.variable(beta_2, name='beta_2')
self.decay = K.variable(decay, name='decay')
if epsilon is None:
epsilon = K.epsilon()
self.epsilon = epsilon
self.initial_decay = decay
def __init__(self,
lr=0.002,
beta_1=0.9,
beta_2=0.999,
epsilon=None,
schedule_decay=0.004,
**kwargs):
super(Nadam, self).__init__(**kwargs)
with K.name_scope(self.__class__.__name__):
self.iterations = K.variable(0, dtype='int64', name='iterations')
self.m_schedule = K.variable(1., name='m_schedule')
self.lr = K.variable(lr, name='lr')
self.beta_1 = K.variable(beta_1, name='beta_1')
self.beta_2 = K.variable(beta_2, name='beta_2')
if epsilon is None:
epsilon = K.epsilon()
self.epsilon = epsilon
self.schedule_decay = schedule_decay
def mean_squared_logarithmic_error(y_true, y_pred): first_log = math_ops.log(K.clip(y_pred, K.epsilon(), None) + 1.) second_log = math_ops.log(K.clip(y_true, K.epsilon(), None) + 1.) return K.mean(math_ops.square(first_log - second_log), axis=-1)
def mean_squared_logarithmic_error(y_true, y_pred): # pylint: disable=missing-docstring y_pred = ops.convert_to_tensor(y_pred) y_true = math_ops.cast(y_true, y_pred.dtype) first_log = math_ops.log(K.clip(y_pred, K.epsilon(), None) + 1.) second_log = math_ops.log(K.clip(y_true, K.epsilon(), None) + 1.) return K.mean(math_ops.squared_difference(first_log, second_log), axis=-1)
def mean_absolute_percentage_error(y_true, y_pred): # pylint: disable=missing-docstring
y_pred = ops.convert_to_tensor(y_pred)
y_true = math_ops.cast(y_true, y_pred.dtype)
diff = math_ops.abs(
(y_true - y_pred) / K.clip(math_ops.abs(y_true), K.epsilon(), None))
return 100. * K.mean(diff, axis=-1)
def mean_absolute_percentage_error(y_true, y_pred):
diff = math_ops.abs(
(y_true - y_pred) / K.clip(math_ops.abs(y_true), K.epsilon(), None))
return 100. * K.mean(diff, axis=-1)
def __call__(self, w):
return w / (
K.epsilon() + K.sqrt(
math_ops.reduce_sum(
math_ops.square(w), axis=self.axis, keepdims=True)))
def kullback_leibler_divergence(y_true, y_pred): # pylint: disable=missing-docstring y_pred = ops.convert_to_tensor(y_pred) y_true = math_ops.cast(y_true, y_pred.dtype) y_true = K.clip(y_true, K.epsilon(), 1) y_pred = K.clip(y_pred, K.epsilon(), 1) return math_ops.reduce_sum(y_true * math_ops.log(y_true / y_pred), axis=-1)
def kullback_leibler_divergence(y_true, y_pred): y_true = K.clip(y_true, K.epsilon(), 1) y_pred = K.clip(y_pred, K.epsilon(), 1) return math_ops.reduce_sum(y_true * math_ops.log(y_true / y_pred), axis=-1)
def poisson(y_true, y_pred): return K.mean(y_pred - y_true * math_ops.log(y_pred + K.epsilon()), axis=-1)
def poisson(y_true, y_pred): y_pred = ops.convert_to_tensor(y_pred) y_true = math_ops.cast(y_true, y_pred.dtype) return K.mean(y_pred - y_true * math_ops.log(y_pred + K.epsilon()), axis=-1)
def logloss(y_true, y_pred):
losses = math_ops.multiply(y_true, math_ops.log(y_pred + K.epsilon()))
losses += math_ops.multiply((1 - y_true),
math_ops.log(1 - y_pred + K.epsilon()))
return K.mean(-losses, axis=-1)
