警告:tensorflow:编译了加载的模型，但编译的指标尚未构建

Question

我的模型-

from tensorflow.keras.layers import ReLU
from keras.layers import Dropout
from tensorflow.keras.utils import plot_model
from matplotlib import pyplot

# define encoder
visible = Input(shape=(n_inputs,))



# encoder level 1
e = Dense(300)(visible)
e = ReLU()(e)
e = Dropout(0.05)(e)

# encoder level 1
e = Dense(200)(visible)
e = ReLU()(e)
e = Dropout(0.05)(e)

# encoder level 1
e = Dense(100)(visible)
e = ReLU()(e)
e = Dropout(0.05)(e)

# encoder level 1
e = Dense(50)(visible)
e = ReLU()(e)
e = Dropout(0.05)(e)



# bottleneck
n_bottleneck = round(float(n_inputs))
bottleneck = Dense(n_bottleneck)(e)


# define decoder, level 1
# encoder level 1
d = Dense(50)(bottleneck)
d = ReLU()(d)
d = Dropout(0.05)(d)

d = Dense(100)(bottleneck)
d = ReLU()(d)
d = Dropout(0.05)(d)

d = Dense(200)(bottleneck)
d = ReLU()(d)
d = Dropout(0.05)(d)

d = Dense(300)(bottleneck)
d = ReLU()(d)
d = Dropout(0.05)(d)

# output layer
output = Dense(n_inputs, activation='sigmoid')(d)
# define autoencoder model
model = Model(inputs=visible, outputs=output)

# compile autoencoder model
model.compile(optimizer='adam', loss='binary_crossentropy')

# plot the autoencoder
plot_model(model, 'autoencoder_compress.png', show_shapes=True)
# fit the autoencoder model to reconstruct input
history = model.fit(X_train_norm, X_train_norm, epochs=100, batch_size=32, verbose=2, validation_data=(X_test_norm,X_test_norm))
# plot loss
pyplot.plot(history.history['loss'], label='train')
pyplot.plot(history.history['val_loss'], label='test')
pyplot.legend()
pyplot.show()
# define an encoder model (without the decoder)
encoder = Model(inputs=visible, outputs=bottleneck)
plot_model(encoder, 'encoder_compress.png', show_shapes=True)
# save the encoder to file
encoder.save('drive/MyDrive/encoder_rf.h5')

我得到了这个错误-

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.

该模型已经过训练。

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Answer 1

只需在绘制之前编译新的encoder模型即可。

此代码不会给出警告：

from tensorflow.keras.layers import ReLU, Input, Dense
from keras.layers import Dropout
from tensorflow.keras.utils import plot_model
from matplotlib import pyplot
from tensorflow.keras.models import Model

n_inputs = 100

# define encoder
visible = Input(shape=(n_inputs,))



# encoder level 1
e = Dense(300)(visible)
e = ReLU()(e)
e = Dropout(0.05)(e)

# encoder level 1
e = Dense(200)(visible)
e = ReLU()(e)
e = Dropout(0.05)(e)

# encoder level 1
e = Dense(100)(visible)
e = ReLU()(e)
e = Dropout(0.05)(e)

# encoder level 1
e = Dense(50)(visible)
e = ReLU()(e)
e = Dropout(0.05)(e)



# bottleneck
n_bottleneck = round(float(n_inputs))
bottleneck = Dense(n_bottleneck)(e)


# define decoder, level 1
# encoder level 1
d = Dense(50)(bottleneck)
d = ReLU()(d)
d = Dropout(0.05)(d)

d = Dense(100)(bottleneck)
d = ReLU()(d)
d = Dropout(0.05)(d)

d = Dense(200)(bottleneck)
d = ReLU()(d)
d = Dropout(0.05)(d)

d = Dense(300)(bottleneck)
d = ReLU()(d)
d = Dropout(0.05)(d)

# output layer
output = Dense(n_inputs, activation='sigmoid')(d)

# define an encoder model (without the decoder)
encoder = Model(inputs=visible, outputs=bottleneck)



# Compile before plotting
encoder.compile(optimizer='adam', loss='binary_crossentropy')



plot_model(encoder, 'encoder_compress.png', show_shapes=True)
# save the encoder to file
encoder.save('drive/MyDrive/encoder_rf.h5')

Answer 2

在您的代码中，model.compiled_metrics为None，请检查以下内容

model.compiled_metrics == None

您已经定义了一个没有权重的新编码器实例，并从您的模型更新了权重。训练后保存编码器。

您的代码中没有encoder-decoder。