I don't understand what my problem is. It should work, if only because its the standard autoenoder from the tensorflow documentation.this is the error
line 64, in calldecoded = self.decoder(encoded)ValueError: Exception encountered when calling Autoencoder.call().
Invalid dtype: <property object at 0x7fb471cc1c60>
Arguments received by Autoencoder.call():• x=tf.Tensor(shape=(32, 28, 28), dtype=float32)
and this is my code
(x_train, _), (x_test, _) = fashion_mnist.load_data()x_train = x_train.astype('float32') / 255.x_test = x_test.astype('float32') / 255.print (x_train.shape)print (x_test.shape)class Autoencoder(Model): def __init__(self, latent_dim, shape): super(Autoencoder, self).__init__() self.latent_dim = latent_dim self.shape = shape self.encoder = tf.keras.Sequential([ layers.Flatten(), layers.Dense(latent_dim, activation='relu'), ]) self.decoder = tf.keras.Sequential([ layers.Dense(tf.math.reduce_prod(shape), activation='sigmoid'), layers.Reshape(shape) ]) def call(self, x): encoded = self.encoder(x) print(encoded) decoded = self.decoder(encoded) print(decoded) return decodedshape = x_test.shape[1:]latent_dim = 64autoencoder = Autoencoder(latent_dim, shape)autoencoder.compile(optimizer='adam', loss=losses.MeanSquaredError())autoencoder.fit(x_train, x_train, epochs=10, shuffle=True, validation_data=(x_test, x_test))I tried to change the database and also tried different shapes