我根据这里讨论的内容为mnist数据集设置了一个去噪自动编码器: https://blog.keras.io/building-autoencoders-in-keras.html
我试图看看输入图像的重建是如何随时间变化的;我注意到有时DAE峰值的损失(用于训练和验证),例如从~0.12到~3.0的损失。为了避免在训练过程中使用这些“失误”,我试图使用Keras的回调,保存最佳权重(val_loss明智)并在训练的每个“段”之后加载它们(在我的情况下= 10个时期)。
但是,我收到一条错误消息:
File "noise_e_mini.py", line 71, in <module>
callbacks=([checkpointer]))
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1650, in fit
validation_steps=validation_steps)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1145, in _fit_loop
callbacks.set_model(callback_model)
File "/usr/local/lib/python2.7/dist-packages/keras/callbacks.py", line 48, in set_model
callback.set_model(model)
AttributeError: 'tuple' object has no attribute 'set_model'
我的代码是:
from keras.layers import Input, Dense
from keras.models import Model
from keras import regularizers
from keras.callbacks import ModelCheckpoint
input_img = Input(shape=(784,))
filepath_for_w='denoise_by_AE_weights_1.h5'
def autoencoder_block(input,l1_score_encode,l1_score_decode):
# encoder:
encoded = Dense(256, activation='relu',activity_regularizer=regularizers.l1(l1_score_encode))(input_img)
encoded = Dense(128, activation='relu',activity_regularizer=regularizers.l1(l1_score_encode))(encoded)
encoded = Dense(64, activation='relu',activity_regularizer=regularizers.l1(l1_score_encode))(encoded)
encoded = Dense(32, activation='relu',activity_regularizer=regularizers.l1(l1_score_encode))(encoded)
encoder = Model (input=input_img, output=encoded)
# decoder:
connection_layer= Input(shape=(32,))
decoded = Dense(64, activation='relu',activity_regularizer=regularizers.l1(l1_score_decode))(connection_layer)
decoded = Dense(128, activation='relu',activity_regularizer=regularizers.l1(l1_score_decode))(decoded)
decoded = Dense(256, activation='relu',activity_regularizer=regularizers.l1(l1_score_decode))(decoded)
decoded = Dense(784, activation='sigmoid',activity_regularizer=regularizers.l1(l1_score_decode))(decoded)
decoder = Model (input=connection_layer , output=decoded)
crunched = encoder(input_img)
final = decoder(crunched)
autoencoder = Model(input=input_img, output=final)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
return (autoencoder)
from keras.datasets import mnist
import numpy as np
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
print x_train.shape
print x_test.shape
noise_factor = 0.5
x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)
autoencoder=autoencoder_block(input_img,0,0)
for i in range (10):
x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)
x_train_noisy = np.clip(x_train_noisy, 0., 1.)
checkpointer=ModelCheckpoint(filepath_for_w, monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=True, mode='auto', period=1),
autoencoder.fit(x_train_noisy, x_train,
epochs=10,
batch_size=256,
shuffle=True,
validation_data=(x_test_noisy, x_test),
callbacks=([checkpointer]))
autoencoder.load_weights(filepath_for_w) # load weights from the best in the run
decoded_imgs = autoencoder.predict(x_test_noisy) # save results for this stage for presentation
np.save('decoded'+str(i)+'.npy',decoded_imgs) ####
np.save('tested.npy',x_test_noisy)
np.save ('true_catagories.npy',y_test)
np.save('original.npy',x_test)
autoencoder.save('denoise_by_AE_model_1.h5')
我做错了什么? 非常感谢:))
答案 0 :(得分:4)
你的问题可能就在这一行内
callbacks=([checkpointer]))
你需要删除括号作为回调需求列表,而不是元组,尝试:
callbacks=[checkpointer]
我还注意到你的checkpointer以逗号结尾,你也应该删除它。
checkpointer=ModelCheckpoint(filepath_for_w, monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=True, mode='auto', period=1),