Question

我正在使用python Keras运行VAE模型。在安装自动编码器模型时，我会遇到错误（在这里称为“ vae”）。

错误：

tensorflow.python.framework.errors_impl.InvalidArgumentError：不兼容的形状：[128,784]与[96,784]

但是我认为这两个形状在下面是相同的。如果有人可以给我一些建议，这将非常有帮助。谢谢。

我的代码：

from keras.layers import Lambda, Input, Dense
from keras.models import Model
from keras.datasets import mnist
from keras.losses import mse, binary_crossentropy
from keras.utils import plot_model
from keras import backend as K


original_dim = 784 #28 * 28
input_shape = (original_dim, )
intermediate_dim = 512
batch_size = 128
latent_dim = 2
epochs = 50
epsilon_std = 1.0


#encoding
x = Input(batch_shape=(batch_size, original_dim))
h = Dense(intermediate_dim, activation = 'relu')(x) #h = hidden layer

#creating latent variable = z
z_mean = Dense(latent_dim)(h) #mean of latent variable (2 dimension)
z_log_sigma = Dense(latent_dim)(h) #log_sigma of latent variable (2 dimension)

def sampling(args):
    z_mean, z_log_sigma = args
    epsilon = K.random_normal(shape=(batch_size, latent_dim),
                              mean=0., 
                              #std=epsilon_std
                              )
    return(z_mean + K.exp(z_log_sigma) * epsilon)

z = Lambda(sampling, output_shape = (latent_dim,))([z_mean, z_log_sigma])

#encoder
encoder = Model(x, z_mean)

#decoding with z
decoder_h = Dense(intermediate_dim, activation='relu')
decoder_mean = Dense(original_dim, activation='sigmoid')

h_decoded = decoder_h(z)#input encoded z
x_decoded_mean = decoder_mean(h_decoded)

#autoencoder
vae = Model(x, x_decoded_mean)

#decoding to generate images
decoder_input = Input(shape=(latent_dim,))
_h_decoded = decoder_h(decoder_input)
_x_decoded_mean = decoder_mean(_h_decoded)

#generator
generator = Model(decoder_input, _x_decoded_mean)


#training
def vae_loss(x,x_decoded_mean):
    xent_loss = binary_crossentropy(x,x_decoded_mean)
    kl_loss = -0.5 * K.mean(1+z_log_sigma-K.square(z_mean)-K.exp(z_log_sigma), axis=-1)
    return(xent_loss+kl_loss)

vae.compile(optimizer = 'rmsprop', loss=vae_loss)

#creating dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
print('x_train', x_train.shape)
print('x_test', x_test.shape)

vae.fit(x_train, x_train,
        shuffle=True,
        epochs=epochs,
        batch_size=batch_size,
        validation_data=(x_test, x_test))

Answer 1

给出您的代码，似乎您正在对模型中的批处理大小进行硬编码，这通常是个坏主意。

由于您将输入定义为x = Input(batch_shape=(batch_size, original_dim))，因此网络期望数据的形状完全为(128, 784)。如果您输入的数据不完全是128的倍数，则这是有问题的，因为vae.fit()会分批拆分数据，而对于最后一批，如果没有足够的数据来填充整批数据将发送部分。看来，您的最后一批样品只有96个样品，而不是128个，因此该模型会引发形状不匹配错误。

执行此操作的正确方法是：不要在模型的任何位置指定批处理大小，而仅将参数传递给fit。实际上，这归结为从不使用Keras层的batch_shape参数，而是使用shape并传递一个样本的形状：

x = Input(shape=(original_dim,))

如果您有任何操作需要指定完整形状（即包括批次大小），请使用None：

epsilon = K.random_normal(shape=(None, latent_dim),
                          mean=0., 
                          #std=epsilon_std
                          )

tensorflow.python.framework.errors_impl.InvalidArgumentError：不兼容的形状：[128,784]与[96,784]

1 个答案: