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How to write masked MSE loss in Keras?
up vote
1
down vote
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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 yesterday
Milo Lu
1,48011326
asked yesterday
mrgloom
4,881954124
add a comment |
up vote
1
down vote
favorite
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 yesterday
Milo Lu
1,48011326
asked yesterday
mrgloom
4,881954124
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)
– Or Dinari
yesterday
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
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 yesterday
Milo Lu
1,48011326
asked yesterday
mrgloom
4,881954124
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 yesterday
Milo Lu
1,48011326
asked yesterday
mrgloom
4,881954124
edited yesterday
Milo Lu
1,48011326
asked yesterday
mrgloom
4,881954124
edited yesterday
Milo Lu
1,48011326
edited yesterday
Milo Lu
1,48011326
edited yesterday
Milo Lu
1,48011326
1,48011326
asked yesterday
mrgloom
4,881954124
asked yesterday
mrgloom
4,881954124
asked yesterday
mrgloom
4,881954124
4,881954124
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)
– Or Dinari
yesterday
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)
– Or Dinari
yesterday
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)
– Or Dinari
yesterday
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)
– Or Dinari
yesterday
add a comment |
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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)
– Or Dinari
yesterday