'int'物件在vae.fit()函式中不可下標

我正在使用這個開發一個VAE：資料集

我使用過 keras教程代碼，并且開發了自己的編碼器和解碼器，問題是當我運行 vae.fit() 時，我得到了'int' object is not subscriptable. 我究竟做錯了什么？

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

編碼器：

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

解碼器：

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類：

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(),
    }

這是我得到錯誤的地方：

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)

完整追溯：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
c:\Users\User\Documents\Github\Generative-Models\TFG\VAE.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:\Users\User\Documents\Github\Generative-Models\TFG\VAE.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

我應該改變什么？非常感謝您的幫助。

uj5u.com熱心網友回復：

encoderanddecoder函式需要一個序列input_shape。但隨著

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

你正在傳遞int價值。

您可以通過傳遞一系列int值來解決此問題。例如與

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

這假設資料的形狀是(samples, features)。然后你input_shape會的(features,)。

標籤：Python 张量流 喀拉斯 自动编码器

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