latent_inputs = Input(shape=(latent_dim,), name='z_sampling')
x = Dense(hidden_dim, activation='relu')(latent_inputs)
outputs = Dense(image_size, activation='sigmoid')(x)
# instantiate decoder model
decoder = Model(latent_inputs, outputs, name='decoder')
plot_model(decoder, to_file='vae_mlp_decoder.png', show_shapes=True)
# instantiate VAE model
outputs = decoder(encoder(inputs)[2])
vae = Model(inputs, outputs, name='vae_mlp')
models = (encoder, decoder)
data = (x_test, y_test)
# calculate reconstruction loss
reconstruction_loss = binary_crossentropy(inputs, outputs) * image_size
# KL divergence loss
k_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)
k_loss = -0.5 * K.sum(k_loss, axis=-1)
# create custom loss layer
vae_loss = K.mean(reconstruction_loss + k_loss)
vae.add_loss(vae_loss)
# compile vae
vae.compile(optimizer='adam')
vae.summary()
plot_model(vae,
to_file='vae_mlp.png',
show_shapes=True)
# train the VAE
history = vae.fit(x_train,
epochs=epochs,
batch_size=batch_size,
validation_data=(x_valid, None))
def yolo_wh_loss(y_true, y_pred, t):
error = K.square(K.sqrt(y_true)-K.sqrt(y_pred))
object_true = COORD_SCALE*(error)
object_false = K.zeros_like(y_true, dtype='float32')
loss = tf.where(t, object_true, object_false)
return K.sum(loss)
def custom_loss(y_actual, y_predicted):
custom_loss_value = K.square(y_predicted - y_actual)
custom_loss_value = K.sum(custom_loss_value, axis=1)
return custom_loss_value
def kl_loss(z_mean, z_log_var):
kl_loss = -0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var),
axis=-1)
return kl_loss
def yolo_loss(args, anchors, num_classes, ignore_thresh=.5, print_loss=False):
'''Return yolo_loss tensor
Parameters
----------
yolo_outputs: list of tensor, the output of yolo_body or tiny_yolo_body
y_true: list of array, the output of preprocess_true_boxes
anchors: array, shape=(N, 2), wh
num_classes: integer
ignore_thresh: float, the iou threshold whether to ignore object confidence loss
Returns
-------
loss: tensor, shape=(1,)
'''
num_layers = len(anchors)//3 # default setting
yolo_outputs = args[:num_layers]
y_true = args[num_layers:]
anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]]
input_shape = K.cast(K.shape(yolo_outputs[0])[1:3] * 32, K.dtype(y_true[0]))
grid_shapes = [K.cast(K.shape(yolo_outputs[l])[1:3], K.dtype(y_true[0])) for l in range(num_layers)]
loss = 0
m = K.shape(yolo_outputs[0])[0] # batch size, tensor
mf = K.cast(m, K.dtype(yolo_outputs[0]))
for l in range(num_layers):
object_mask = y_true[l][..., 4:5]
true_class_probs = y_true[l][..., 5:]
grid, raw_pred, pred_xy, pred_wh = yolo_head(yolo_outputs[l],
anchors[anchor_mask[l]], num_classes, input_shape, calc_loss=True)
pred_box = K.concatenate([pred_xy, pred_wh])
# Darknet raw box to calculate loss.
raw_true_xy = y_true[l][..., :2]*grid_shapes[l][::-1] - grid
raw_true_wh = K.log(y_true[l][..., 2:4] / anchors[anchor_mask[l]] * input_shape[::-1])
raw_true_wh = K.switch(object_mask, raw_true_wh, K.zeros_like(raw_true_wh)) # avoid log(0)=-inf
box_loss_scale = 2 - y_true[l][...,2:3]*y_true[l][...,3:4]
# Find ignore mask, iterate over each of batch.
ignore_mask = tf.TensorArray(K.dtype(y_true[0]), size=1, dynamic_size=True)
object_mask_bool = K.cast(object_mask, 'bool')
def loop_body(b, ignore_mask):
true_box = tf.boolean_mask(y_true[l][b,...,0:4], object_mask_bool[b,...,0])
iou = box_iou(pred_box[b], true_box)
best_iou = K.max(iou, axis=-1)
ignore_mask = ignore_mask.write(b, K.cast(best_iou<ignore_thresh, K.dtype(true_box)))
return b+1, ignore_mask
_, ignore_mask = tf.while_loop(lambda b,*args: b<m, loop_body, [0, ignore_mask])
ignore_mask = ignore_mask.stack()
ignore_mask = K.expand_dims(ignore_mask, -1)
# K.binary_crossentropy is helpful to avoid exp overflow.
xy_loss = object_mask * box_loss_scale * K.binary_crossentropy(raw_true_xy, raw_pred[...,0:2], from_logits=True)
wh_loss = object_mask * box_loss_scale * 0.5 * K.square(raw_true_wh-raw_pred[...,2:4])
confidence_loss = object_mask * K.binary_crossentropy(object_mask, raw_pred[...,4:5], from_logits=True)+ \
(1-object_mask) * K.binary_crossentropy(object_mask, raw_pred[...,4:5], from_logits=True) * ignore_mask
class_loss = object_mask * K.binary_crossentropy(true_class_probs, raw_pred[...,5:], from_logits=True)
xy_loss = K.sum(xy_loss) / mf
wh_loss = K.sum(wh_loss) / mf
confidence_loss = K.sum(confidence_loss) / mf
class_loss = K.sum(class_loss) / mf
loss += xy_loss + wh_loss + confidence_loss + class_loss
if print_loss:
loss = tf.Print(loss, [loss, xy_loss, wh_loss, confidence_loss, class_loss, K.sum(ignore_mask)], message='loss: ')
return loss
def call(self, x, mask=None):
norm = K.sqrt(K.sum(K.square(x), axis=-1, keepdims=True)) + K.epsilon()
x = x / norm * self.gamma
return x
help_ = "Use binary cross entropy instead of mse (default)"
parser.add_argument("--bce", help=help_, action='store_true')
args = parser.parse_args()
models = (encoder, decoder)
data = (x_test, y_test)
# VAE loss = mse_loss or xent_loss + kl_loss
if args.bce:
reconstruction_loss = binary_crossentropy(K.flatten(inputs),
K.flatten(outputs))
else:
reconstruction_loss = mse(K.flatten(inputs), K.flatten(outputs))
reconstruction_loss *= image_size * image_size
kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)
kl_loss = K.sum(kl_loss, axis=-1)
kl_loss *= -0.5
vae_loss = K.mean(reconstruction_loss + kl_loss)
vae.add_loss(vae_loss)
vae.compile(optimizer='rmsprop')
vae.summary()
plot_model(vae, to_file='vae_cnn.png', show_shapes=True)
save_dir = "vae_cnn_weights"
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
if args.weights:
filepath = os.path.join(save_dir, args.weights)
vae = vae.load_weights(filepath)
else:
# train the autoencoder
def kl_loss(self, w, mu, sigma):
variational_dist = tfp.distributions.Normal(mu, sigma)
return self.kl_weight * K.sum(
variational_dist.log_prob(w) - self.log_prior_prob(w))
def dice_coef(y_true, y_pred):
y_true_f = keras.flatten(y_true)
y_pred_f = keras.flatten(y_pred)
intersection = keras.sum(y_true_f * y_pred_f)
return (2. * intersection + 1) / (keras.sum(y_true_f) + keras.sum(y_pred_f) + 1)
def yolo_loss(y_true, y_pred):
label_class = y_true[..., :1] # ? * 7 * 7 * 1
# 分类
label_box = y_true[..., 1:5] # ? * 7 * 7 * 4
# BB1的坐标
response_mask = y_true[..., 5] # ? * 7 * 7
# BB1的置信度
response_mask = K.expand_dims(response_mask) # ? * 7 * 7 * 1
predict_class = y_pred[..., :1] # ? * 7 * 7 * 1
# 分类
predict_trust = y_pred[..., 1:3] # ? * 7 * 7 * 2
# BB1和BB2的置信度
predict_box = y_pred[..., 3:] # ? * 7 * 7 * 8
# BB1和BB2的坐标
_label_box = K.reshape(label_box, [-1, 7, 7, 1, 4])
_predict_box = K.reshape(predict_box, [-1, 7, 7, 2, 4])
label_xy, label_wh = yolo_head(_label_box, img_size=224) # ? * 7 * 7 * 1 * 2, ? * 7 * 7 * 1 * 2
label_xy = K.expand_dims(label_xy, 3) # ? * 7 * 7 * 1 * 1 * 2
label_wh = K.expand_dims(label_wh, 3) # ? * 7 * 7 * 1 * 1 * 2
label_xy_min, label_xy_max = X_Y_W_H_To_Min_Max(label_xy,
label_wh) # ? * 7 * 7 * 1 * 1 * 2, ? * 7 * 7 * 1 * 1 * 2
predict_xy, predict_wh = yolo_head(_predict_box, img_size=224) # ? * 7 * 7 * 2 * 2, ? * 7 * 7 * 2 * 2
predict_xy = K.expand_dims(predict_xy, 4) # ? * 7 * 7 * 2 * 1 * 2
predict_wh = K.expand_dims(predict_wh, 4) # ? * 7 * 7 * 2 * 1 * 2
predict_xy_min, predict_xy_max = X_Y_W_H_To_Min_Max(predict_xy,
predict_wh) # ? * 7 * 7 * 2 * 1 * 2, ? * 7 * 7 * 2 * 1 * 2
iou_scores = iou(predict_xy_min, predict_xy_max, label_xy_min, label_xy_max) # ? * 7 * 7 * 2 * 1
best_ious = K.max(iou_scores, axis=4) # ? * 7 * 7 * 2
best_box = K.max(best_ious, axis=3, keepdims=True) # ? * 7 * 7 * 1
box_mask = K.cast(best_ious >= best_box, K.dtype(best_ious)) # ? * 7 * 7 * 2
no_object_loss = 0.5 * (1 - box_mask * response_mask) * K.square(0 - predict_trust)
object_loss = box_mask * response_mask * K.square(1 - predict_trust)
confidence_loss = no_object_loss + object_loss
confidence_loss = K.sum(confidence_loss)
class_loss = response_mask * K.square(label_class - predict_class)
class_loss = K.sum(class_loss)
_label_box = K.reshape(label_box, [-1, 7, 7, 1, 4])
_predict_box = K.reshape(predict_box, [-1, 7, 7, 2, 4])
label_xy, label_wh = yolo_head(_label_box, img_size=224) # ? * 7 * 7 * 1 * 2, ? * 7 * 7 * 1 * 2
predict_xy, predict_wh = yolo_head(_predict_box, img_size=224) # ? * 7 * 7 * 2 * 2, ? * 7 * 7 * 2 * 2
box_mask = K.expand_dims(box_mask)
response_mask = K.expand_dims(response_mask)
box_loss = 5 * box_mask * response_mask * K.square((label_xy - predict_xy) / 224)
box_loss += 5 * box_mask * response_mask * K.square((K.sqrt(label_wh) - K.sqrt(predict_wh)) / 224)
box_loss = K.sum(box_loss)
loss = confidence_loss + class_loss + box_loss
return loss
def neg_log_likelihood(y_obs, y_pred, sigma=noise):
dist = tfp.distributions.Normal(loc=y_pred, scale=sigma)
return K.sum(-dist.log_prob(y_obs))
alignment_probs = K.softmax(
dot([Dense(hidden_dim)(h_enc), h_dec], axes=-1, normalize=False), -2)
h_enc_rep = K.tile(K.expand_dims(h_enc, -2), [1, 1, T_y, 1])
h_dec_rep = K.tile(K.expand_dims(h_dec, -3), [1, T_x, 1, 1])
h_rep = K.concatenate([h_enc_rep, h_dec_rep], -1)
alignment_probs_ = []
for i in range(T_y):
if i == 0:
align_prev_curr = tf.gather(alignment_probs, i, axis=-1)
if i > 0:
align_prev_curr = tf.einsum('nx,ny->nxy',
tf.gather(alignment_probs, i, axis=-1),
alignment_probs_[i - 1])
align_prev_curr *= struc_zeros
align_prev_curr = K.sum(align_prev_curr, 1) + 1e-6
align_prev_curr /= K.sum(align_prev_curr, -1, keepdims=True)
alignment_probs_.append(align_prev_curr)
alignment_probs_ = K.stack(alignment_probs_, -1)
emission_probs = Dense(hidden_dim * 3, activation='tanh')(h_rep)
emission_probs = Dense(Y, activation='softmax')(emission_probs)
alignment_probs_ = Lambda(lambda x: x)(alignment_probs_)
alignment_model = Model([enc_in_, dec_in_], alignment_probs_)
alignment_output = alignment_model([enc_in_, dec_in_])
alignment = Model([enc_in_, dec_in_], alignment_output)
alphas = tf.expand_dims(alignment_probs_, -1) * emission_probs
dec_out_ = tf.reduce_sum(alphas, -3)
def _l2normalizer(v, epsilon=1e-4):
return v / (K.sum(v**2)**0.5 + epsilon)
def call(self, inputs, states, targets, training=None):
h_tm1 = states[
0] # previous set of memory states: shape (batch_size, number of particles, number of units)
c_tm1 = states[
1] # previous set of carry states: shape (batch_size, number of particles, number of units)
# adding the set of weights
w_tm1 = states[
2] # previous set of weights (for SMC): shape (batch_size, number of particles)
# RESAMPLING STEP FOR LSTM with particle filter
indices = tf.random.categorical(
logits=w_tm1, num_samples=self.particles
) # see if we need to use numpy functions instead
indices = list(K.eval(indices))
h_sampl = h_tm1[:, indices, :]
c_sampl = c_tm1[:, indices, :]
# TO DO: check these 2 functions to see if they need to be adjusted for PF LSTM Cell.
dp_mask = self.get_dropout_mask_for_cell(inputs, training, count=4)
rec_dp_mask = self.get_recurrent_dropout_mask_for_cell(h_tm1,
training,
count=4)
if self.implementation == 1:
if 0 < self.dropout < 1.:
inputs_i = inputs * dp_mask[0]
inputs_f = inputs * dp_mask[1]
inputs_c = inputs * dp_mask[2]
inputs_o = inputs * dp_mask[3]
else:
inputs_i = inputs
inputs_f = inputs
inputs_c = inputs
inputs_o = inputs
# split on last axis
k_i, k_f, k_c, k_o = array_ops.split(self.kernel,
num_or_size_splits=4,
axis=-1)
x_i = K.dot(inputs_i, k_i)
x_f = K.dot(inputs_f, k_f)
x_c = K.dot(inputs_c, k_c)
x_o = K.dot(inputs_o, k_o)
if self.use_bias:
b_i, b_f, b_c, b_o = array_ops.split(self.bias,
num_or_size_splits=4,
axis=0)
x_i = K.bias_add(x_i, b_i)
x_f = K.bias_add(x_f, b_f)
x_c = K.bias_add(x_c, b_c)
x_o = K.bias_add(x_o, b_o)
if 0 < self.recurrent_dropout < 1.:
h_sampl_i = h_sampl * rec_dp_mask[0]
h_sampl_f = h_sampl * rec_dp_mask[1]
h_sampl_c = h_sampl * rec_dp_mask[2]
h_sampl_o = h_sampl * rec_dp_mask[3]
else:
h_tm1_i = h_sampl
h_tm1_f = h_sampl
h_tm1_c = h_sampl
h_tm1_o = h_sampl
x = (x_i, x_f, x_c, x_o)
h_sampl = (h_sampl_i, h_sampl_f, h_sampl_c, h_sampl_o)
c, o = self._compute_carry_and_output(x, h_sampl, c_sampl)
else:
if 0. < self.dropout < 1.:
inputs *= dp_mask[0]
z = K.dot(inputs, self.kernel)
if 0. < self.recurrent_dropout < 1.:
h_tm1 *= rec_dp_mask[0]
z += K.dot(h_sampl, self.recurrent_kernel)
if self.use_bias:
z = K.bias_add(z, self.bias)
z = array_ops.split(z, num_or_size_splits=4, axis=1)
c, o = self._compute_carry_and_output_fused(z, c_sampl)
h = o * self.activation(c)
# COMPUTATION OF THE NEW SET OF WEIGHTS
w = K.softmax(K.dot(h, self.output_kernel) + self.bias_output
) # shape (batch_size, num of particles, num of words)
# w=keras.layers.Dense(self.num_words)(h)
# w=keras.layers.softmax(w)
w = w * targets # assuming that targets are one-hot encoded.
w = K.sum(w, axis=-1)
# add a linear combination with a uniform sampling (soft resampling: presumed trick from the 'Particle Filter Recurrent Networks' to make everything differentiable)
# link to the article:
w_final = self.alpha * w + (1 - self.alpha) * K.random_uniform(
shape=[self.batch_size, self.particles],
minval=0,
maxval=1 / self.particles)
w_final = w / w_final
w_final = w_final / K.sum(w_final, axis=-1)
def average_state(h_states, weights):
# TO SIMPLIFY???!!!
mean_weights = tf.expand_dims(weights, axis=-1)
h_mean = tf.squeeze(
tf.matmul(h_states, mean_weights, transpose_a=True))
sum_weights = tf.tile(
tf.expand_dims(tf.reduce_sum(weights, axis=-1), axis=-1),
[1, h_mean.shape[-1]])
h_mean = h_mean / sum_weights
return h_mean
# COMPUTATION OF HMEAN
h_mean = average_state(h, w_final)
return h_mean, [h, c, w_final]
def customLoss(y_true, y_pred):
log1 = 1.5 * y_true * K.log(y_pred + 1e-9) * K.pow(1 - y_pred, 2)
log0 = 0.5 * (1 - y_true) * K.log((1 - y_pred) + 1e-9) * K.pow(y_pred, 2)
return (-K.sum(K.mean(log0 + log1, axis=0)))
def vae_loss(x, y): x = K.reshape(x, shape=(batch_size, 28 * 28)) y = K.reshape(y, shape=(batch_size, 28 * 28)) loss = K.sum(K.square(x - y), axis=-1) kl_loss = -0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1) return (loss + kl_loss) / 2 / 28 / 28
def dice_coef(y_true, y_pred, smooth=1):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (2. * intersection + smooth) / (K.sum(y_true_f * y_true_f) +
K.sum(y_pred_f * y_pred_f) + smooth)
def jacard_coef(y_true, y_pred):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (intersection + 1.0) / (K.sum(y_true_f) + K.sum(y_pred_f) -
intersection + 1.0)
def get_MRI_VAE_3D(input_shape=(64, 64, 64, 1),
latent_dim=2,
batch_size=32,
disentangle=False,
gamma=1):
#TODO: add discriminator loss, see if there is improvement. Perhaps try on shapes dataset if it's easier...
image_size, _, _, channels = input_shape
kernel_size = 3
filters = 16
intermediate_dim = 128
epochs = 10
nlayers = 2
# VAE model = encoder + decoder
# build encoder model
inputs = Input(shape=input_shape, name='encoder_input')
x = inputs
for i in range(nlayers):
filters *= 2
x = Conv3D(filters=filters,
kernel_size=kernel_size,
activation='relu',
strides=2,
padding='same')(x)
# shape info needed to build decoder model
shape = K.int_shape(x)
# generate latent vector Q(z|X)
x = Flatten()(x)
x = Dense(intermediate_dim, activation='relu')(x)
z_mean = Dense(latent_dim, name='z_mean')(x)
z_log_var = Dense(latent_dim, name='z_log_var')(x)
# use reparameterization trick to push the sampling out as input
# note that "output_shape" isn't necessary with the TensorFlow backend
z = Lambda(sampling, output_shape=(latent_dim, ),
name='z')([z_mean, z_log_var])
# instantiate encoder model
encoder = Model(inputs, [z_mean, z_log_var, z], name='encoder')
# build decoder model
latent_inputs = Input(shape=(latent_dim, ), name='z_sampling')
x = Dense(intermediate_dim, activation='relu')(latent_inputs)
x = Dense(shape[1] * shape[2] * shape[3] * shape[4], activation='relu')(x)
x = Reshape((shape[1], shape[2], shape[3], shape[4]))(x)
for i in range(nlayers):
x = Conv3DTranspose(filters=filters,
kernel_size=kernel_size,
activation='relu',
strides=2,
padding='same')(x)
filters //= 2
outputs = Conv3DTranspose(filters=1,
kernel_size=kernel_size,
activation='sigmoid',
padding='same',
name='decoder_output')(x)
# instantiate decoder model
decoder = Model(latent_inputs, outputs, name='decoder')
# decoder.summary()
# instantiate VAE model
outputs = decoder(encoder(inputs)[2])
vae = Model(inputs, outputs, name='vae')
if disentangle:
discriminator = Dense(1, activation='sigmoid')
z1 = Lambda(lambda x: x[:int(batch_size / 2), :int(latent_dim / 2)])(z)
z2 = Lambda(lambda x: x[int(batch_size / 2):, :int(latent_dim / 2)])(z)
s1 = Lambda(lambda x: x[:int(batch_size / 2), int(latent_dim / 2):])(z)
s2 = Lambda(lambda x: x[int(batch_size / 2):, int(latent_dim / 2):])(z)
q_bar = tf.keras.layers.concatenate([
tf.keras.layers.concatenate([s1, z2], axis=1),
tf.keras.layers.concatenate([s2, z1], axis=1)
],
axis=0)
q = tf.keras.layers.concatenate([
tf.keras.layers.concatenate([s1, z1], axis=1),
tf.keras.layers.concatenate([s2, z2], axis=1)
],
axis=0)
# q_bar_score = discriminator(q_bar)
# q_score = discriminator(q)
# tc_loss = K.log(q_score / (1 - q_score))
q_bar_score = (discriminator(q_bar) +
.1) * .85 # +.1 * .85 so that it's 0<x<1
q_score = (discriminator(q) + .1) * .85
tc_loss = K.log(q_score / (1 - q_score))
discriminator_loss = -K.log(q_score) - K.log(1 - q_bar_score)
reconstruction_loss = mse(K.flatten(inputs), K.flatten(outputs))
reconstruction_loss *= image_size * image_size
kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)
kl_loss = K.sum(kl_loss, axis=-1)
kl_loss *= -0.5
if disentangle:
vae_loss = K.mean(reconstruction_loss) + K.mean(
kl_loss) + gamma * K.mean(tc_loss) + K.mean(discriminator_loss)
else:
vae_loss = K.mean(reconstruction_loss) + K.mean(kl_loss)
vae.add_loss(vae_loss)
opt = tf.keras.optimizers.Adam(learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-07,
amsgrad=False,
name='Adam')
#vae.compile(optimizer='rmsprop')
vae.compile(optimizer=opt)
if disentangle:
vae.metrics_tensors = [
reconstruction_loss, kl_loss, tc_loss, discriminator_loss
]
# vae.summary()
return encoder, decoder, vae
def sensitivity(y_true, y_pred):
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)))
return true_positives / (possible_positives + K.epsilon())
def call(self, inputs, mask=None, **kwargs):
if isinstance(inputs, list):
inputs, positions = inputs
positions = K.cast(positions, 'int32')
mask = mask[1]
else:
positions = None
input_len = K.shape(inputs)[1]
if self.attention_type == SeqSelfAttention.ATTENTION_TYPE_ADD:
e = self._call_additive_emission(inputs)
elif self.attention_type == SeqSelfAttention.ATTENTION_TYPE_MUL:
e = self._call_multiplicative_emission(inputs)
if self.attention_activation is not None:
e = self.attention_activation(e)
e = K.exp(e - K.max(e, axis=-1, keepdims=True))
if self.attention_width is not None:
ones = tf.ones((input_len, input_len))
if self.history_only:
local = tf.linalg.band_part(
ones,
K.minimum(input_len, self.attention_width - 1),
0,
)
else:
local = tf.linalg.band_part(
ones,
K.minimum(input_len, self.attention_width // 2),
K.minimum(input_len, (self.attention_width - 1) // 2),
)
e = e * K.expand_dims(local, 0)
if mask is not None:
mask = K.cast(mask, K.floatx())
mask = K.expand_dims(mask)
e = K.permute_dimensions(
K.permute_dimensions(e * mask, (0, 2, 1)) * mask, (0, 2, 1))
# a_{t} = \text{softmax}(e_t)
s = K.sum(e, axis=-1)
s = K.tile(K.expand_dims(s, axis=-1), K.stack([1, 1, input_len]))
a = e / (s + K.epsilon())
# l_t = \sum_{t'} a_{t, t'} x_{t'}
v = K.batch_dot(a, inputs)
if self.attention_regularizer_weight > 0.0:
self.add_loss(self._attention_regularizer(a))
if positions is not None:
pos_num = K.shape(positions)[1]
batch_indices = K.tile(
K.expand_dims(K.arange(K.shape(inputs)[0]), axis=-1),
K.stack([1, pos_num]))
pos_indices = K.stack([batch_indices, positions], axis=-1)
v = tf.gather_nd(v, pos_indices)
a = tf.gather_nd(a, pos_indices)
if self.return_attention:
return [v, a]
return v
def total_error(y_true, y_pred):
"""
I think this is simply the variance, but as a sum...
"""
return K.sum(K.square(y_true - K.mean(y_true)))
def calculate_content_loss(content_features, const_content_features):
test_l_content = 0.5 * K.sum(
K.square(content_features - const_content_features))
return test_l_content
def unexplained_error(y_true, y_pred):
"""
I think this is simply the squared error, but the sum not the mean as in mse
"""
return K.sum(K.square(y_true - y_pred))
def content_loss(base, combination):
return K.sum(K.square(combination - base))
def total_error_avgAx0(y_true, y_pred):
"""
"""
avgY = K.mean(y_true, axis=0, keepdims=True) # 0 is sample axis
return K.sum(K.square(y_true - avgY))
def yolo_class_loss(y_true, y_pred, t):
error = K.square(y_true - y_pred)
object_true = CLASS_SCALE*(error)
object_false = K.zeros_like(y_true)
loss = tf.where(t, object_true, object_false)
return K.sum(loss)
def euclidean_distance_loss(y_true, y_pred):
return K.sqrt(K.sum(K.square(y_pred - y_true), axis=-1))
def dice_coef(y_true, y_pred, smooth=1):
intersection = K.sum(y_true * y_pred, axis=0)
union = K.sum(y_true, axis=0) + K.sum(y_pred, axis=0)
return K.mean((2. * intersection + smooth) / (union + smooth), axis=0)
def action_augmentation_loss(y_true, y_pred):
return K.sum(K.square(y_true - y_pred))
