How to write masked MSE loss in Keras?
2
I was trying to write masked MSE loss:
def mae_loss_masked(mask):
def loss_fn(y_true, y_pred):
abs_vec = tf.multiply(tf.abs(y_pred-y_true), mask)
loss = tf.reduce_mean(abs_vec)
return loss
return loss_fn
My model:
def MobileNet_v1():
# MobileNet with dense layer on top
# Keras 2.1.6
mobilenet = MobileNet(input_shape=(config.IMAGE_H, config.IMAGE_W, config.N_CHANNELS),
alpha=1.0,
depth_multiplier=1,
include_top=False,
weights='imagenet'
)
x = Flatten()(mobilenet.output)
x = Dropout(0.5)(x)
x = Dense(config.N_LANDMARKS * 2, activation='linear')(x)
# -------------------------------------------------------
model = Model(inputs=mobilenet.input, outputs=x)
optimizer = Adadelta()
model.compile(optimizer=optimizer, loss=mae_loss_masked)
model.summary()
import sys
sys.exit()
return model
But it give an error:
TypeError: mae_loss_masked() takes 1 positional argument but 2 were given
Also a question how batch generator output should look like in this case.
python tensorflow keras deep-learning mse
edited Nov 19 '18 at 9:57
Milo Lu
1,60811327
asked Nov 18 '18 at 23:56
mrgloommrgloom
5,1631062131
add a comment |
2
I was trying to write masked MSE loss:
def mae_loss_masked(mask):
def loss_fn(y_true, y_pred):
abs_vec = tf.multiply(tf.abs(y_pred-y_true), mask)
loss = tf.reduce_mean(abs_vec)
return loss
return loss_fn
My model:
def MobileNet_v1():
# MobileNet with dense layer on top
# Keras 2.1.6
mobilenet = MobileNet(input_shape=(config.IMAGE_H, config.IMAGE_W, config.N_CHANNELS),
alpha=1.0,
depth_multiplier=1,
include_top=False,
weights='imagenet'
)
x = Flatten()(mobilenet.output)
x = Dropout(0.5)(x)
x = Dense(config.N_LANDMARKS * 2, activation='linear')(x)
# -------------------------------------------------------
model = Model(inputs=mobilenet.input, outputs=x)
optimizer = Adadelta()
model.compile(optimizer=optimizer, loss=mae_loss_masked)
model.summary()
import sys
sys.exit()
return model
But it give an error:
TypeError: mae_loss_masked() takes 1 positional argument but 2 were given
Also a question how batch generator output should look like in this case.
python tensorflow keras deep-learning mse
edited Nov 19 '18 at 9:57
Milo Lu
1,60811327
asked Nov 18 '18 at 23:56
mrgloommrgloom
5,1631062131
Your loss function takes 1 argument, while you are actually giving it 2. using mae_loss_masked(some_mask) will get you the actual loss function you need: stackoverflow.com/questions/46858016/… , batch should still be (x,y) , or optionally (x,y, weights)
– Dinari
Nov 19 '18 at 7:31
add a comment |
2
2
2
I was trying to write masked MSE loss:
def mae_loss_masked(mask):
def loss_fn(y_true, y_pred):
abs_vec = tf.multiply(tf.abs(y_pred-y_true), mask)
loss = tf.reduce_mean(abs_vec)
return loss
return loss_fn
My model:
def MobileNet_v1():
# MobileNet with dense layer on top
# Keras 2.1.6
mobilenet = MobileNet(input_shape=(config.IMAGE_H, config.IMAGE_W, config.N_CHANNELS),
alpha=1.0,
depth_multiplier=1,
include_top=False,
weights='imagenet'
)
x = Flatten()(mobilenet.output)
x = Dropout(0.5)(x)
x = Dense(config.N_LANDMARKS * 2, activation='linear')(x)
# -------------------------------------------------------
model = Model(inputs=mobilenet.input, outputs=x)
optimizer = Adadelta()
model.compile(optimizer=optimizer, loss=mae_loss_masked)
model.summary()
import sys
sys.exit()
return model
But it give an error:
TypeError: mae_loss_masked() takes 1 positional argument but 2 were given
Also a question how batch generator output should look like in this case.
python tensorflow keras deep-learning mse
edited Nov 19 '18 at 9:57
Milo Lu
1,60811327
asked Nov 18 '18 at 23:56
mrgloommrgloom
5,1631062131
I was trying to write masked MSE loss:
def mae_loss_masked(mask):
def loss_fn(y_true, y_pred):
abs_vec = tf.multiply(tf.abs(y_pred-y_true), mask)
loss = tf.reduce_mean(abs_vec)
return loss
return loss_fn
My model:
def MobileNet_v1():
# MobileNet with dense layer on top
# Keras 2.1.6
mobilenet = MobileNet(input_shape=(config.IMAGE_H, config.IMAGE_W, config.N_CHANNELS),
alpha=1.0,
depth_multiplier=1,
include_top=False,
weights='imagenet'
)
x = Flatten()(mobilenet.output)
x = Dropout(0.5)(x)
x = Dense(config.N_LANDMARKS * 2, activation='linear')(x)
# -------------------------------------------------------
model = Model(inputs=mobilenet.input, outputs=x)
optimizer = Adadelta()
model.compile(optimizer=optimizer, loss=mae_loss_masked)
model.summary()
import sys
sys.exit()
return model
But it give an error:
TypeError: mae_loss_masked() takes 1 positional argument but 2 were given
Also a question how batch generator output should look like in this case.
python tensorflow keras deep-learning mse
python tensorflow keras deep-learning mse
edited Nov 19 '18 at 9:57
Milo Lu
1,60811327
asked Nov 18 '18 at 23:56
mrgloommrgloom
5,1631062131
edited Nov 19 '18 at 9:57
Milo Lu
1,60811327
asked Nov 18 '18 at 23:56
mrgloommrgloom
5,1631062131
edited Nov 19 '18 at 9:57
Milo Lu
1,60811327
edited Nov 19 '18 at 9:57
Milo Lu
1,60811327
edited Nov 19 '18 at 9:57
Milo Lu
1,60811327
1,60811327
asked Nov 18 '18 at 23:56
mrgloommrgloom
5,1631062131
asked Nov 18 '18 at 23:56
mrgloommrgloom
5,1631062131
asked Nov 18 '18 at 23:56
mrgloommrgloom
5,1631062131
5,1631062131
Your loss function takes 1 argument, while you are actually giving it 2. using mae_loss_masked(some_mask) will get you the actual loss function you need: stackoverflow.com/questions/46858016/… , batch should still be (x,y) , or optionally (x,y, weights)
– Dinari
Nov 19 '18 at 7:31
add a comment |
Your loss function takes 1 argument, while you are actually giving it 2. using mae_loss_masked(some_mask) will get you the actual loss function you need: stackoverflow.com/questions/46858016/… , batch should still be (x,y) , or optionally (x,y, weights)
– Dinari
Nov 19 '18 at 7:31
Your loss function takes 1 argument, while you are actually giving it 2. using mae_loss_masked(some_mask) will get you the actual loss function you need: stackoverflow.com/questions/46858016/… , batch should still be (x,y) , or optionally (x,y, weights)
– Dinari
Nov 19 '18 at 7:31
Your loss function takes 1 argument, while you are actually giving it 2. using mae_loss_masked(some_mask) will get you the actual loss function you need: stackoverflow.com/questions/46858016/… , batch should still be (x,y) , or optionally (x,y, weights)
– Dinari
Nov 19 '18 at 7:31
add a comment |
0
active
oldest
votes
Your Answer
StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
draft saved
draft discarded
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53366667%2fhow-to-write-masked-mse-loss-in-keras%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
0
active
oldest
votes
0
active
oldest
votes
active
oldest
votes
active
oldest
votes
draft saved
draft discarded
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
draft saved
draft discarded
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53366667%2fhow-to-write-masked-mse-loss-in-keras%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Your loss function takes 1 argument, while you are actually giving it 2. using mae_loss_masked(some_mask) will get you the actual loss function you need: stackoverflow.com/questions/46858016/… , batch should still be (x,y) , or optionally (x,y, weights)
– Dinari
Nov 19 '18 at 7:31