'int' 对象在 vae.fit() 函数中不可下标

Question

我正在使用以下内容开发VAE：数据集

我使用了keras教程代码，并开发了自己的编码器和解码器，问题是当我运行vae.fit((时，我得到了'int' object is not subscriptable。我做错了什么？

df = pd.read_csv('local path')
xtrain, xtest = train_test_split(df, test_size=0.2)

编码器：

def encoder(input_shape):
inputs = keras.Input(shape=input_shape)
x = layers.Dense(128, activation='relu')(inputs)
x = layers.Dense(128, activation='relu')(x)
z_mean = layers.Dense(2, name='z_mean')(x)
z_log_var = layers.Dense(2, name='z_log_var')(x)
z = Sampling()([z_mean, z_log_var])
encoder = keras.Model(inputs, [z_mean, z_log_var, z], name='encoder')
encoder.summary()
return encoder

解码器：

def decoder(input_shape):
inputs = keras.Input(shape=input_shape)
x = layers.Dense(128, activation='relu')(inputs)
x = layers.Dense(128, activation='relu')(x)
outputs = layers.Dense(input_shape[0], activation='sigmoid')(x)
decoder = keras.Model(inputs, outputs, name='decoder')
decoder.summary()
return decoder

VAE等级：

class VAE(keras.Model):
def __init__(self, encoder, decoder, **kwargs):
super(VAE, self).__init__(**kwargs)
self.encoder = encoder
self.decoder = decoder
self.total_loss_tracker = keras.metrics.Mean(name="total_loss")
self.reconstruction_loss_tracker = keras.metrics.Mean(
name="reconstruction_loss"
)
self.kl_loss_tracker = keras.metrics.Mean(name="kl_loss")
@property
def metrics(self):
return [
self.total_loss_tracker,
self.reconstruction_loss_tracker,
self.kl_loss_tracker,
]
def train_step(self, data):
with tf.GradientTape() as tape:
z_mean, z_log_var, z = self.encoder(data)
reconstruction = self.decoder(z)
reconstruction_loss = tf.reduce_mean(
tf.reduce_sum(
keras.losses.binary_crossentropy(data, reconstruction), axis=(1, 2)
)
)
kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))
kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1))
total_loss = reconstruction_loss + kl_loss
grads = tape.gradient(total_loss, self.trainable_weights)
self.optimizer.apply_gradients(zip(grads, self.trainable_weights))
self.total_loss_tracker.update_state(total_loss)
self.reconstruction_loss_tracker.update_state(reconstruction_loss)
self.kl_loss_tracker.update_state(kl_loss)
return {
"loss": self.total_loss_tracker.result(),
"reconstruction_loss": self.reconstruction_loss_tracker.result(),
"kl_loss": self.kl_loss_tracker.result(),
}

这就是我得到错误的地方：

data = np.concatenate([xtrain.values, xtest.values])
vae = VAE(encoder(data.shape[1]), 
decoder(data.shape[1]))
vae.compile(optimizer="adam", 
loss="binary_crossentropy")
vae.fit(data, epochs=10, batch_size=32, 
validation_split=0.2)

完整回溯：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
c:UsersUserDocumentsGithubGenerative-ModelsTFGVAE.ipynb Cell 9' in <cell line: 3>()
1 data = np.concatenate([xtrain.values, xtest.values])
----> 3 vae = VAE(encoder(data.shape[1]), decoder(data.shape[1]))
4 vae.compile(optimizer="adam", loss="binary_crossentropy")
5 vae.fit(data, epochs=10, batch_size=32, validation_split=0.2)
c:UsersUserDocumentsGithubGenerative-ModelsTFGVAE.ipynb Cell 7' in 
decoder(input_shape)
3 x = layers.Dense(128, activation='relu')(inputs)
4 x = layers.Dense(128, activation='relu')(x)
----> 5 outputs = layers.Dense(input_shape[0], activation='sigmoid')(x)
6 decoder = keras.Model(inputs, outputs, name='decoder')
7 decoder.summary()
TypeError: 'int' object is not subscriptable

我应该更改什么？我们非常感谢您的帮助。

Answer 1

encoder和decoder函数需要一个input_shape序列。但是

vae = VAE(
encoder(data.shape[1]), 
decoder(data.shape[1])
)

您正在传递int值。

可以通过传递一系列int值来解决此问题。例如

vae = VAE(
encoder(data.shape[1:]), 
decoder(data.shape[1:])
)

这假设数据的形状是(samples, features)。那么您的input_shape将是(features,)。