我正在尝试在Keras储存库中的example之后在Keras中构建变体自动编码器。这是我的设置：
a=Circle(2)
a.show()
#Circle
这有效，但是如果您查看from keras.layers import Dense, Input, merge, concatenate, Dense, LSTM, Lambda, Flatten, Reshape
from keras import backend as K
from keras.models import Model
from keras.losses import mse
import numpy as np
class VAE:
def __init__(self, n_verts=15, n_dims=3, n_layers=3, n_units=128, latent_dim=2):
self.n_verts = n_verts
self.n_dims = n_dims
self.n_layers = n_layers
self.n_units = n_units
self.latent_dim = latent_dim
self.encoder = self.build_encoder()
self.decoder = self.build_decoder()
inputs = Input((self.n_verts, self.n_dims))
outputs = self.decoder(self.encoder(inputs)[2])
self.model = Model(inputs, outputs, name='vae')
self.model.compile(optimizer='adam', loss=self.get_loss)
def build_encoder(self):
i = Input(shape=(self.n_verts, self.n_dims), name='encoder_input')
h = i
h = Flatten()(h)
h = Dense(self.n_units, activation='relu')(h)
for idx in range(1, self.n_layers, 1):
h = Dense(self.n_units // (2*idx), activation='relu')(h)
self.z_mean = Dense(self.latent_dim, name='z_mean')(h)
self.z_log_var = Dense(self.latent_dim, name='z_log_var')(h)
# use reparameterization trick to factor stochastic node out of gradient flow
self.z = Lambda(self.sample, output_shape=(self.latent_dim,), name='z')([self.z_mean, self.z_log_var])
return Model(i, [self.z_mean, self.z_log_var, self.z], name='encoder')
def sample(self, args):
'''
Reparameterization trick by sampling from an isotropic unit Gaussian.
@arg (tensor): mean and log of variance of Q(z|X)
@returns z (tensor): sampled latent vector
'''
z_mean, z_log_var = args
batch = K.shape(z_mean)[0]
dim = K.int_shape(z_mean)[1]
# by default, random_normal has mean = 0 and std = 1.0
epsilon = K.random_normal(shape=(batch, dim))
return z_mean + K.exp(0.5 * z_log_var) * epsilon
def build_decoder(self):
i = Input(shape=(self.latent_dim,), name='z_sampling')
h = i
for idx in range(1, self.n_layers, 1):
h = Dense(self.n_units//(2*(self.n_layers-idx)), activation='relu')(h)
h = Dense(self.n_units, activation='relu')(h)
h = Dense(self.n_verts * self.n_dims, activation='sigmoid')(h)
o = Reshape((self.n_verts, self.n_dims))(h)
return Model(i, o, name='decoder')
def get_loss(self, inputs, outputs):
reconstruction_loss = mse(inputs, outputs)
reconstruction_loss *= self.n_verts * self.n_dims
return reconstruction_loss # this works fine
kl_loss = 1 + self.z_log_var - K.square(self.z_mean) - K.exp(self.z_log_var)
kl_loss = K.sum(kl_loss, axis=-1)
kl_loss *= -0.5
vae_loss = K.mean(reconstruction_loss + kl_loss) # todo: make this balance parameterizable
return vae_loss # this doesn't
def train(self, X, predict='frame', n_epochs=10000):
for idx in range(n_epochs):
i = np.random.randint(0, X.shape[1]-1) # sample idx
frame = np.expand_dims( X[:,i:i+1,:].squeeze(), axis=0) # shape = 1 sample, v verts, d dims
next_frame = np.expand_dims( X[:,i+1:i+2,:].squeeze(), axis=0)
if predict == 'frame': loss = self.model.train_on_batch(frame, frame)
elif predict == 'next_frame': loss = self.model.train_on_batch(frame, next_frame)
if idx % 1000 == 0:
print(' * training idx', idx, 'loss', loss)
X_train = np.random.rand(15, 100, 3)
vae = VAE(n_verts=15, latent_dim=2, n_layers=3, n_units=128)
vae.encoder.summary()
vae.train(X_train, n_epochs=10000, predict='frame')
函数，您会发现它过早返回。如果我注释掉get_loss，以使损失函数返回return reconstruction_loss，则会出现错误：
vae_loss有人知道如何解决此错误吗？任何建议将不胜感激！
答案 0 :(得分：0)
啊，一旦我的变量的范围正确，我就解决了这个问题：
from keras.layers import Dense, Input, merge, concatenate, Dense, LSTM, Lambda, Flatten, Reshape
from keras import backend as K
from keras.models import Model
from keras.losses import mse
import numpy as np
class VAE:
def __init__(self, n_verts=15, n_dims=3, n_layers=3, n_units=128, latent_dim=2):
self.input_shape = (n_verts*n_dims,)
self.n_layers = n_layers
self.n_units = n_units
self.latent_dim = latent_dim
# build the encoder and decoder
inputs = Input(shape=self.input_shape, name='encoder_input')
self.encoder = self.get_encoder(inputs)
self.decoder = self.get_decoder()
# build the VAE
outputs = self.decoder(self.encoder(inputs)[2])
self.model = Model(inputs, outputs, name='vae_mlp')
# add loss and compile
self.model.add_loss(self.get_loss(inputs, outputs))
self.model.compile(optimizer='adam')
def get_encoder(self, inputs):
h = inputs
h = Dense(self.n_units, activation='relu')(h)
for idx in range(1, self.n_layers, 1):
h = Dense(self.n_units // (2*idx), activation='relu')(h)
self.z_mean = Dense(self.latent_dim, name='z_mean')(h)
self.z_log_var = Dense(self.latent_dim, name='z_log_var')(h)
z = Lambda(self.sampling, output_shape=(self.latent_dim,), name='z')([self.z_mean, self.z_log_var])
encoder = Model(inputs, [self.z_mean, self.z_log_var, z], name='encoder')
return encoder
def sampling(self, args):
self.z_mean, self.z_log_var = args
batch = K.shape(self.z_mean)[0]
dim = K.int_shape(self.z_mean)[1]
# by default, random_normal has mean = 0 and std = 1.0
epsilon = K.random_normal(shape=(batch, dim))
return self.z_mean + K.exp(0.5 * self.z_log_var) * epsilon
def get_decoder(self):
latent_inputs = Input(shape=(self.latent_dim,), name='z_sampling')
h = latent_inputs
for idx in range(1, self.n_layers, 1):
h = Dense(self.n_units//(2*(self.n_layers-idx)), activation='relu')(h)
h = Dense(self.n_units, activation='relu')(h)
outputs = Dense(self.input_shape[0], activation='sigmoid')(h)
decoder = Model(latent_inputs, outputs, name='decoder')
return decoder
def get_loss(self, inputs, outputs):
reconstruction_loss = mse(inputs, outputs)
reconstruction_loss *= self.input_shape[0]
kl_loss = 1 + self.z_log_var - K.square(self.z_mean) - K.exp(self.z_log_var)
kl_loss = K.sum(kl_loss, axis=-1)
kl_loss *= -0.5
vae_loss = K.mean(reconstruction_loss + kl_loss)
return vae_loss
# train
x_train = np.random.rand(10000, 45)
vae = VAE(n_verts=15, latent_dim=2, n_layers=3, n_units=128)
vae.model.fit(x_train[:-1000,:],
epochs=100,
batch_size=128,
validation_data=(x_train[-1000:,:], None))