Keras调谐器贝叶斯优化图误差

Question

我正在尝试使用keras调谐器库中提供的贝叶斯优化算法优化卷积神经网络。

当我执行tuner_cnn.search(datagen.flow(X_trainRusReshaped,Y_trainRusHot), epochs=50, batch_size=256)我遇到这个错误:InvalidArgumentError: Graph execution error

One-Hot-Encode y_train和y_test如下:

y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
X_trainShape = X_train.shape[1]*X_train.shape[2]*X_train.shape[3]
X_testShape = X_test.shape[1]*X_test.shape[2]*X_test.shape[3]
X_trainFlat = X_train.reshape(X_train.shape[0], X_trainShape)
X_testFlat = X_test.reshape(X_test.shape[0], X_testShape)
# One-hot-encoding
Y_trainRusHot = to_categorical(Y_trainRus, num_classes = 2)
Y_testRusHot = to_categorical(Y_testRus, num_classes = 2)

我这样定义我的模型生成器:

datagen = ImageDataGenerator(
featurewise_center=True,
featurewise_std_normalization=True,
rotation_range=180,
horizontal_flip=True,vertical_flip = True)
def model_builder(hp):
model = Sequential()
#model.add(Input(shape=(50,50,3)))
for i in range(hp.Int('num_blocks', 1, 2)):
hp_padding=hp.Choice('padding_'+ str(i), values=['valid', 'same'])
hp_filters=hp.Choice('filters_'+ str(i), values=[32, 64])
model.add(Conv2D(hp_filters, (3, 3), padding=hp_padding, activation='relu', kernel_initializer='he_uniform', input_shape=(50, 50, 3)))
model.add(MaxPooling2D((2, 2)))
model.add(Dropout(hp.Choice('dropout_'+ str(i), values=[0.0, 0.1, 0.2])))
model.add(Flatten())
hp_units = hp.Int('units', min_value=25, max_value=150, step=25)
model.add(Dense(hp_units, activation='relu', kernel_initializer='he_uniform'))
model.add(Dense(10,activation="softmax"))
hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3])
hp_optimizer=hp.Choice('Optimizer', values=['Adam', 'SGD'])
if hp_optimizer == 'Adam':
hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3])
elif hp_optimizer == 'SGD':
hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3])
nesterov=True
momentum=0.9
model.compile(loss=keras.losses.binary_crossentropy, optimizer=tf.keras.optimizers.Adam(learning_rate=hp_learning_rate), metrics=['accuracy'])
return model

执行调谐器搜索:

tuner_cnn = kt.tuners.BayesianOptimization(
model_builder,
objective='val_loss',
max_trials=100,
directory='.',
project_name='tuning-cnn')
tuner_cnn.search(datagen.flow(X_trainRusReshaped,Y_trainRusHot), epochs=50, batch_size=256)

我还试着做:

tuner_cnn.search(X_trainRusReshaped, Y_trainRusHot, epochs=80, validation_data=(X_testRusReshaped, Y_testRusHot), callbacks=[stop_early])

但它也不起作用。任何想法?

Answer 1

从完整的错误信息中，我能够缩小问题的来源。问题是您的最后一个Dense层有10 units，这意味着您期望10 classes(您甚至根据units的数量选择了正确的激活函数)。然而，Binary CrossEntropy为loss.

所以你要么有10 classes，要么使用categorical或sparse categorical CrossEntropy，要么你有2 classes，所以损失确实是Binary CrossEntropy。