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How to improve accuracy of variational autoencoder for coordinate data?
The Next CEO of Stack Overflow2019 Community Moderator ElectionLatent loss in variational autoencoder drowns generative lossHow to understand log-likelihood for generative image model?What is an intuitive explanation for the Importance Weighted Autoencoder?
$begingroup$
I have data like that:
x_data = (300000,42), for each row, there are 42 features(x,y coordinates):
[297.425 341.30002 280.1 295.625 275.375 240.5 287.975 213.725 294.275 186.95 332.07498 254.675 355.69998 215.3 380.9 201.125 402.94998 188.52501 357.275 268.85 391.925 234.20001 412.4 215.3 432.875 202.7 380.9 287.75 410.82498 259.4 432.875 238.925 450.2 224.75 391.925 306.65 428.15 290.9 448.625 272 469.1 254.675]
I want to generate new data from variational autoencoder. I add some noise to my input data, and feed to the network with (1,42) dimension input.
After training, when I predict noisy data, the accuracy is so bad and cannot reconstruct correct x,y coordinates.
What I should do now to improve my accuracy? Thank for your supports.
Edit:
I added some code for VAE.
x = Input(shape=(original_dim,))
h = Dense(int(original_dim/2), activation= 'relu')(x)
hh = Dense(int(original_dim/2), activation= 'relu')(h)
z_mean = Dense(20)(hh)
z_log_var = Dense(20)(hh)
z = Lambda(sampling, output_shape=(20,))([z_mean, z_log_var])
print('z',z)
#decoder
decoder_h = Dense(int(original_dim/2), activation='relu')
decoder_hh = Dense(int(original_dim/2), activation='relu')
decoder_mean = Dense(original_dim, activation=None)
h_decoded = decoder_h(z)
hh_decoded = decoder_hh(h_decoded)
x_decoded_mean = decoder_mean(hh_decoded)
autoencoder = Model(x, x_decoded_mean)
def sampling(args):
z_mean, z_log_var = args
epsilon = K.random_normal(shape=(K.shape(z_mean)[0], 20), mean=0., stddev = 1)
return z_mean + K.exp(z_log_var / 2) * epsilon
def vae_loss(x, x_decoded_mean):
xent_loss = original_dim * metrics.mse(x, x_decoded_mean)
#xent_loss = objectives.mse(x, x_decoded_mean)
print(xent_loss)
kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis = -1)
kl_loss = 0.005*kl_loss
print(kl_loss)
loss = K.mean(xent_loss + kl_loss)
return loss
generative-models
asked Mar 22 at 12:55
Dennis ThorDennis Thor
11
$endgroup$
add a comment |
$begingroup$
I have data like that:
x_data = (300000,42), for each row, there are 42 features(x,y coordinates):
[297.425 341.30002 280.1 295.625 275.375 240.5 287.975 213.725 294.275 186.95 332.07498 254.675 355.69998 215.3 380.9 201.125 402.94998 188.52501 357.275 268.85 391.925 234.20001 412.4 215.3 432.875 202.7 380.9 287.75 410.82498 259.4 432.875 238.925 450.2 224.75 391.925 306.65 428.15 290.9 448.625 272 469.1 254.675]
I want to generate new data from variational autoencoder. I add some noise to my input data, and feed to the network with (1,42) dimension input.
After training, when I predict noisy data, the accuracy is so bad and cannot reconstruct correct x,y coordinates.
What I should do now to improve my accuracy? Thank for your supports.
Edit:
I added some code for VAE.
x = Input(shape=(original_dim,))
h = Dense(int(original_dim/2), activation= 'relu')(x)
hh = Dense(int(original_dim/2), activation= 'relu')(h)
z_mean = Dense(20)(hh)
z_log_var = Dense(20)(hh)
z = Lambda(sampling, output_shape=(20,))([z_mean, z_log_var])
print('z',z)
#decoder
decoder_h = Dense(int(original_dim/2), activation='relu')
decoder_hh = Dense(int(original_dim/2), activation='relu')
decoder_mean = Dense(original_dim, activation=None)
h_decoded = decoder_h(z)
hh_decoded = decoder_hh(h_decoded)
x_decoded_mean = decoder_mean(hh_decoded)
autoencoder = Model(x, x_decoded_mean)
def sampling(args):
z_mean, z_log_var = args
epsilon = K.random_normal(shape=(K.shape(z_mean)[0], 20), mean=0., stddev = 1)
return z_mean + K.exp(z_log_var / 2) * epsilon
def vae_loss(x, x_decoded_mean):
xent_loss = original_dim * metrics.mse(x, x_decoded_mean)
#xent_loss = objectives.mse(x, x_decoded_mean)
print(xent_loss)
kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis = -1)
kl_loss = 0.005*kl_loss
print(kl_loss)
loss = K.mean(xent_loss + kl_loss)
return loss
generative-models
asked Mar 22 at 12:55
Dennis ThorDennis Thor
11
$endgroup$
1
$begingroup$
It would be helpful if you could provide the code which you used to build the AE.
$endgroup$
– Shubham Panchal
Mar 22 at 13:21
$begingroup$
I added my piece of code
$endgroup$
– Dennis Thor
Mar 25 at 5:49
add a comment |
$begingroup$
I have data like that:
x_data = (300000,42), for each row, there are 42 features(x,y coordinates):
[297.425 341.30002 280.1 295.625 275.375 240.5 287.975 213.725 294.275 186.95 332.07498 254.675 355.69998 215.3 380.9 201.125 402.94998 188.52501 357.275 268.85 391.925 234.20001 412.4 215.3 432.875 202.7 380.9 287.75 410.82498 259.4 432.875 238.925 450.2 224.75 391.925 306.65 428.15 290.9 448.625 272 469.1 254.675]
I want to generate new data from variational autoencoder. I add some noise to my input data, and feed to the network with (1,42) dimension input.
After training, when I predict noisy data, the accuracy is so bad and cannot reconstruct correct x,y coordinates.
What I should do now to improve my accuracy? Thank for your supports.
Edit:
I added some code for VAE.
x = Input(shape=(original_dim,))
h = Dense(int(original_dim/2), activation= 'relu')(x)
hh = Dense(int(original_dim/2), activation= 'relu')(h)
z_mean = Dense(20)(hh)
z_log_var = Dense(20)(hh)
z = Lambda(sampling, output_shape=(20,))([z_mean, z_log_var])
print('z',z)
#decoder
decoder_h = Dense(int(original_dim/2), activation='relu')
decoder_hh = Dense(int(original_dim/2), activation='relu')
decoder_mean = Dense(original_dim, activation=None)
h_decoded = decoder_h(z)
hh_decoded = decoder_hh(h_decoded)
x_decoded_mean = decoder_mean(hh_decoded)
autoencoder = Model(x, x_decoded_mean)
def sampling(args):
z_mean, z_log_var = args
epsilon = K.random_normal(shape=(K.shape(z_mean)[0], 20), mean=0., stddev = 1)
return z_mean + K.exp(z_log_var / 2) * epsilon
def vae_loss(x, x_decoded_mean):
xent_loss = original_dim * metrics.mse(x, x_decoded_mean)
#xent_loss = objectives.mse(x, x_decoded_mean)
print(xent_loss)
kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis = -1)
kl_loss = 0.005*kl_loss
print(kl_loss)
loss = K.mean(xent_loss + kl_loss)
return loss
generative-models
asked Mar 22 at 12:55
Dennis ThorDennis Thor
11
$endgroup$
I have data like that:
x_data = (300000,42), for each row, there are 42 features(x,y coordinates):
[297.425 341.30002 280.1 295.625 275.375 240.5 287.975 213.725 294.275 186.95 332.07498 254.675 355.69998 215.3 380.9 201.125 402.94998 188.52501 357.275 268.85 391.925 234.20001 412.4 215.3 432.875 202.7 380.9 287.75 410.82498 259.4 432.875 238.925 450.2 224.75 391.925 306.65 428.15 290.9 448.625 272 469.1 254.675]
I want to generate new data from variational autoencoder. I add some noise to my input data, and feed to the network with (1,42) dimension input.
After training, when I predict noisy data, the accuracy is so bad and cannot reconstruct correct x,y coordinates.
What I should do now to improve my accuracy? Thank for your supports.
Edit:
I added some code for VAE.
x = Input(shape=(original_dim,))
h = Dense(int(original_dim/2), activation= 'relu')(x)
hh = Dense(int(original_dim/2), activation= 'relu')(h)
z_mean = Dense(20)(hh)
z_log_var = Dense(20)(hh)
z = Lambda(sampling, output_shape=(20,))([z_mean, z_log_var])
print('z',z)
#decoder
decoder_h = Dense(int(original_dim/2), activation='relu')
decoder_hh = Dense(int(original_dim/2), activation='relu')
decoder_mean = Dense(original_dim, activation=None)
h_decoded = decoder_h(z)
hh_decoded = decoder_hh(h_decoded)
x_decoded_mean = decoder_mean(hh_decoded)
autoencoder = Model(x, x_decoded_mean)
def sampling(args):
z_mean, z_log_var = args
epsilon = K.random_normal(shape=(K.shape(z_mean)[0], 20), mean=0., stddev = 1)
return z_mean + K.exp(z_log_var / 2) * epsilon
def vae_loss(x, x_decoded_mean):
xent_loss = original_dim * metrics.mse(x, x_decoded_mean)
#xent_loss = objectives.mse(x, x_decoded_mean)
print(xent_loss)
kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis = -1)
kl_loss = 0.005*kl_loss
print(kl_loss)
loss = K.mean(xent_loss + kl_loss)
return loss
generative-models
generative-models
asked Mar 22 at 12:55
Dennis ThorDennis Thor
11
asked Mar 22 at 12:55
Dennis ThorDennis Thor
11
edited Mar 25 at 5:49
Dennis Thor
asked Mar 22 at 12:55
Dennis ThorDennis Thor
11
asked Mar 22 at 12:55
Dennis ThorDennis Thor
11
asked Mar 22 at 12:55
Dennis ThorDennis Thor
11
11
1
$begingroup$
It would be helpful if you could provide the code which you used to build the AE.
$endgroup$
– Shubham Panchal
Mar 22 at 13:21
$begingroup$
I added my piece of code
$endgroup$
– Dennis Thor
Mar 25 at 5:49
add a comment |
1
$begingroup$
It would be helpful if you could provide the code which you used to build the AE.
$endgroup$
– Shubham Panchal
Mar 22 at 13:21
$begingroup$
I added my piece of code
$endgroup$
– Dennis Thor
Mar 25 at 5:49
1
1
$begingroup$
It would be helpful if you could provide the code which you used to build the AE.
$endgroup$
– Shubham Panchal
Mar 22 at 13:21
$begingroup$
It would be helpful if you could provide the code which you used to build the AE.
$endgroup$
– Shubham Panchal
Mar 22 at 13:21
$begingroup$
I added my piece of code
$endgroup$
– Dennis Thor
Mar 25 at 5:49
$begingroup$
I added my piece of code
$endgroup$
– Dennis Thor
Mar 25 at 5:49
add a comment |
0
active
oldest
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1
$begingroup$
It would be helpful if you could provide the code which you used to build the AE.
$endgroup$
– Shubham Panchal
Mar 22 at 13:21
$begingroup$
I added my piece of code
$endgroup$
– Dennis Thor
Mar 25 at 5:49