def correlation_coefficient_loss(y_true, y_pred):
x = y_true
y = y_pred
mx = K.mean(x)
my = K.mean(y)
xm, ym = x-mx, y-my
r_num = K.sum(tf.multiply(xm,ym))
r_den = K.sqrt(tf.multiply(K.sum(K.square(xm)), K.sum(K.square(ym))))
r = r_num / r_den
r = K.maximum(K.minimum(r, 1.0), -1.0)
return K.square(r)
def Kaggle_IoU_Precision(y_true, y_pred, threshold=0.5):
y_pred = K.squeeze(tf.to_int32(y_pred > threshold), -1)
y_true = K.cast(y_true[..., 0], K.floatx())
y_pred = K.cast(y_pred, K.floatx())
truth_areas = K.sum(y_true, axis=[1, 2])
pred_areas = K.sum(y_pred, axis=[1, 2])
intersection = K.sum(y_true * y_pred, axis=[1, 2])
union = K.clip(truth_areas + pred_areas - intersection, 1e-9, 128 * 128)
check = K.map_fn(lambda x: K.equal(x, 0),
truth_areas + pred_areas,
dtype=tf.bool)
p = intersection / union
iou = K.switch(check, p + 1., p)
prec = K.map_fn(lambda x: K.mean(K.greater(x, np.arange(0.5, 1.0, 0.05))),
iou,
dtype=tf.float32)
prec_iou = K.mean(prec)
return prec_iou
def FocalLoss(y_true, y_pred):
"""
:param y_true: A tensor of the same shape as `y_pred`
:param y_pred: A tensor resulting from a sigmoid
:return: Output tensor.
"""
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's and Inf's
pt_1 = K.clip(pt_1, epsilon, 1. - epsilon)
pt_0 = K.clip(pt_0, epsilon, 1. - epsilon)
return -K.mean(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1)) \
- K.mean((1 - alpha) * K.pow(pt_0, gamma) * K.log(1. - pt_0))
def weighted_mse(y_pred, y_true):
majority_weight = 0.9500
minority_weight = 0.0500
# Apply the weights
loss = K.mean(K.square((y_pred - y_true) * (y_true * majority_weight) +
(1. - y_true) * minority_weight),
axis=-1)
# Return the mean error
return loss
def mrcnn_mask_loss_graph(target_masks, target_class_ids, pred_masks):
"""Mask binary cross-entropy loss for the masks head.
target_masks: [batch, num_rois, height, width].
A float32 tensor of values 0 or 1. Uses zero padding to fill array.
target_class_ids: [batch, num_rois]. Integer class IDs. Zero padded.
pred_masks: [batch, proposals, height, width, num_classes] float32 tensor
with values from 0 to 1.
"""
# Reshape for simplicity. Merge first two dimensions into one.
target_class_ids = K.reshape(target_class_ids,
(-1, )) # (batch * num_rois, )
mask_shape = tf.shape(target_masks)
target_masks = K.reshape(
target_masks, (-1, mask_shape[2],
mask_shape[3])) # (batch * num_rois, height, width)
pred_shape = tf.shape(pred_masks)
pred_masks = K.reshape(
pred_masks,
(-1, pred_shape[2], pred_shape[3],
pred_shape[4])) # (batch * proposals, height, width, num_classes)
# Permute predicted masks to [N, num_classes, height, width]
pred_masks = tf.transpose(
pred_masks,
[0, 3, 1, 2]) # (batch * proposals, num_classes, height, width)
# Only positive ROIs contribute to the loss. And only
# the class specific mask of each ROI.
positive_ix = tf.where(target_class_ids > 0)[:, 0] # (?, )
positive_class_ids = tf.cast(tf.gather(target_class_ids, positive_ix),
tf.int64) # (?, )
indices = tf.stack([positive_ix, positive_class_ids], axis=1) # (?, 2)
# Gather the masks (predicted and true) that contribute to loss
y_true = tf.gather(
target_masks, # (batch * num_rois, height, width)
positive_ix) # (?, )
y_pred = tf.gather_nd(
pred_masks, # (batch * proposals, num_classes, height, width)
indices) # (?, 2)
# Compute binary cross entropy. If no positive ROIs, then return 0.
# shape: [batch, roi, num_classes]
loss = K.switch(
tf.size(y_true) > 0,
K.binary_crossentropy(
target=y_true, # (?, height, width)
output=y_pred), # (?, height, width)
tf.constant(0.0))
loss = K.mean(loss)
return loss
def attention_3d_block(inputs):
# inputs.shape = (batch_size, time_steps, input_dim)
input_dim = int(inputs.shape[3])
a = Permute((3, 1, 2))(inputs)
# a = Reshape((input_dim, TIME_STEPS , TIME_STEPS ))(a) # this line is not useful. It's just to know which dimension is what. softmax
a = Dense(TIME_STEPS, activation='softmax')(a)
print(a)
if SINGLE_ATTENTION_VECTOR:
a = Lambda(lambda x: K.mean(x, axis=1))(a)
a = RepeatVector(input_dim)(a)
a_probs = Permute((2, 3, 1))(a)
output_attention_mul = merge([inputs, a_probs],
name='attention_mul',
mode='mul')
return output_attention_mul
def dice_coef(y_true, y_pred, smooth=1):
intersection = keras.sum(y_true * y_pred, axis=[1,2,3])
union = keras.sum(y_true, axis=[1,2,3]) + keras.sum(y_pred, axis=[1,2,3])
return keras.mean( (2. * intersection + smooth) / (union + smooth), axis=0)
def act(state) #roll the dice for a move
if random.random() < epsilon
return random action
else
return optimal action # action = argmax Q(s, a')
def observe()
observe that reward
def remember()
def learn() #learning fun
def loss(target, prediction)
err = prediction - target
loss = keras.mean(keras.sqrt(1 + keras.square(error)) - 1)
return (loss)
def _build_model(self):
model = Sequential()
model.add(Dense(24, input_dim=self.state_size, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(self.action_size, activation='linear'))
model.compile(loss=self._huber_loss, optimizer=Adam(lr=self.learning_rate))
return model
def load(self, name):
self.model.load_weights(name)
def save(self, name):
self.model.save_weights(name)
def surface_loss(y_true, y_pred):
y_true_dist_map = tf.py_function(func=calc_dist_map_batch,
inp=[y_true],
Tout=tf.float32)
multipled = y_pred * y_true_dist_map
return K.mean(multipled)
def root_mean_squared_error(y_true, y_pred):
return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))
def rmse(y_true, y_pred):
return K.sqrt(K.mean(K.square(y_pred - y_true)))
def custom_loss(y_true, y_pred):
return keras.mean(keras.square(func_g(X[-1])-y_pred))
def loss(y_true, y_pred):
return k.mean(k.square(y_pred - y_true) - k.square(y_true), axis=-1)
decoder_h = Dense(dense1, activation='tanh')
decoder_mean = Dense(original_dim, activation='tanh')
f_decoded = decoder_f(z)
h_decoded = decoder_h(f_decoded)
x_decoded_mean = decoder_mean(h_decoded)
x_decoded_img = Reshape(original_shape)(x_decoded_mean)
# instantiate VAE model
vae = Model(in_layer, x_decoded_img)
# Compute VAE loss
xent_loss = original_dim * metrics.binary_crossentropy(x, x_decoded_mean)
kl_loss = -0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var),
axis=-1)
vae_loss = K.mean(0.5 * xent_loss + 0.5 * kl_loss)
vae.add_loss(vae_loss)
vae.compile(optimizer='adam')
vae.summary()
vae.fit(x_train,
shuffle=True,
epochs=train_epoch,
batch_size=batch_size,
validation_data=(anomaly_test, None))
vae.save('%s = %d %d vae_dense2_model.hdf5' % (var_str, var1, var2))
encoder = Model(in_layer, z_mean)
def wasserstein_loss(self, y_true, y_pred):
return K.mean(y_true * y_pred)
