我在玩一些 Keras 樣本，通過子類化定義模型，但我無法讓它作業。
from keras import layers, Model
from keras.datasets import mnist
from keras.callbacks import TensorBoard
import numpy as np
import datetime
import os
class Encoder(Model):
def __init__(self, name: str = "encoder"):
super(Encoder, self).__init__(name=name)
self._conv16 = layers.Conv2D(16, (3, 3), activation='relu', padding='same', name="conv16")
self._conv8 = layers.Conv2D(8, (3, 3), activation='relu', padding='same', name="conv8")
self._max_pool = layers.MaxPooling2D((2, 2), padding='same', name="max_pool")
def call(self, inputs: np.ndarray):
x = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(inputs)
x = layers.MaxPooling2D((2, 2), padding='same')(x)
x = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = layers.MaxPooling2D((2, 2), padding='same')(x)
x = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded = layers.MaxPooling2D((2, 2), padding='same')(x)
# x = self._conv16(inputs)
# x = self._max_pool(x)
# x = self._conv8(x)
# x = self._max_pool(x)
# x = self._conv8(x)
# encoded = self._max_pool(x)
return encoded
class Decoder(Model):
def __init__(self, name: str = "decoder"):
super(Decoder, self).__init__(name=name)
self._conv16 = layers.Conv2D(16, (3, 3), activation='relu', padding='same', name="conv16")
self._conv8 = layers.Conv2D(8, (3, 3), activation='relu', padding='same', name="conv8")
self._conv1 = layers.Conv2D(1, (3, 3), activation='sigmoid', padding='same', name="conv1")
self._up_sampling = layers.UpSampling2D((2, 2), name="up_samp")
def call(self, inputs: np.ndarray):
x = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(inputs)
x = layers.UpSampling2D((2, 2))(x)
x = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = layers.UpSampling2D((2, 2))(x)
x = layers.Conv2D(16, (3, 3), activation='relu')(x)
x = layers.UpSampling2D((2, 2))(x)
decoded = layers.Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
# x = self._conv8(inputs)
# x = self._up_sampling(x)
# x = self._conv8(x)
# x = self._up_sampling(x)
# x = self._conv16(x)
# x = self._up_sampling(x)
# decoded = self._conv1(x)
return decoded
class Autoencoder(Model):
def __init__(self, name: str = "autoencoder"):
super(Autoencoder, self).__init__(name=name)
self.encoder = Encoder()
self.decoder = Decoder()
def call(self, inputs: np.ndarray):
encoded = self.encoder(inputs)
reconstructed = self.decoder(encoded)
return reconstructed
(x_train, _), (x_test, _) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))
print(x_train.shape)
print(x_test.shape)
autoencoder = Autoencoder()
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.fit(x_train, x_train,
epochs=50,
batch_size=128,
shuffle=True,
validation_data=(x_test, x_test)
首先，我無法讓注釋代碼作業。每當我嘗試使用它時，我都會收到諸如“ValueError：圖層“conv8”的輸入0與圖層不兼容的錯誤：輸入形狀的預期軸-1具有值1，但接收到形狀為（60000、56、56， 8)"。我無法發現模型定義的差異。
其次，運行上面的代碼給了我：
ValueError: tf.function 僅支持在第一次呼叫時創建的單例 tf.Variables。確保 tf.Variable 僅創建一次或在 tf.function 之外創建。有關更多資訊，請參閱
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