Question

我正在努力总结对sample variational autoencoder in the Keras repository的输入，但是似乎犯了一些基本的错误。特别是，以下模型仅适用于某些批次大小：

from keras.layers import Lambda, Input, Dense, Reshape
from keras.models import Model
from keras.losses import mse
from keras import backend as K
import numpy as np

# reparameterization trick
# instead of sampling from Q(z|X), sample epsilon = N(0,I)
# z = z_mean + sqrt(var) * epsilon
def sampling(args):
  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

# network parameters
original_dim = 45
input_shape = (original_dim, )
intermediate_dim = 512
latent_dim = 2

# VAE model = encoder + decoder
# build encoder model
inputs = Input(shape=input_shape, name='encoder_input')
x = Reshape((original_dim,))(inputs)
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)
z = Lambda(sampling, output_shape=(latent_dim,), name='z')([z_mean, z_log_var])
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(original_dim, activation='sigmoid')(x)
outputs = Reshape(input_shape)(x)
decoder = Model(latent_inputs, outputs, name='decoder')

# instantiate VAE model
outputs = decoder(encoder(inputs)[2])
vae = Model(inputs, outputs, name='vae_mlp')
vae.add_loss(mse(inputs, outputs))
vae.compile(optimizer='adam')

x_train = np.random.rand(1000, 45)
vae.fit(x_train, epochs=100, batch_size=10) # works, while 23 fails

任何人都可以帮助我理解为什么某些批量大小失败的原因（例如23）吗？对于其他人可以在这个问题上提供的任何见解，我将不胜感激。

Answer 1

如果DBContext，当前的批处理大小不相等。可以通过将代码更改为以下内容来解决问题：

DAL

这将导致所有批次具有相同的大小，here是data%batch_size != 0及其属性的文档。

Keras：许多批次大小都失败

1 个答案: