Что я делаю:
- Я тренирую предварительно обученный CNN с Keras
fit_generator().Это создает метрики оценки (loss, acc, val_loss, val_acc) после каждой эпохи.После обучения модели я создаю оценочные метрики (loss, acc) с evaluate_generator().
Что я ожидаю:
- Если я тренируюсьмодель для одной эпохи, я ожидаю, что метрики, полученные с
fit_generator() и evaluate_generator(), одинаковы.Они оба должны получать метрики на основе всего набора данных.
То, что я наблюдаю:
- Оба
loss и acc различныот fit_generator() и evaluate_generator(): 
Что я не понимаю:
- Почемуточность от
fit_generator() отличается от точности от evaluate_generator()
Мой код:
def generate_data(path, imagesize, nBatches):
datagen = ImageDataGenerator(rescale=1./255)
generator = datagen.flow_from_directory\
(directory=path, # path to the target directory
target_size=(imagesize,imagesize), # dimensions to which all images found will be resize
color_mode='rgb', # whether the images will be converted to have 1, 3, or 4 channels
classes=None, # optional list of class subdirectories
class_mode='categorical', # type of label arrays that are returned
batch_size=nBatches, # size of the batches of data
shuffle=True) # whether to shuffle the data
return generator
[...]
def train_model(model, nBatches, nEpochs, trainGenerator, valGenerator, resultPath):
history = model.fit_generator(generator=trainGenerator,
steps_per_epoch=trainGenerator.samples//nBatches, # total number of steps (batches of samples)
epochs=nEpochs, # number of epochs to train the model
verbose=2, # verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch
callbacks=None, # keras.callbacks.Callback instances to apply during training
validation_data=valGenerator, # generator or tuple on which to evaluate the loss and any model metrics at the end of each epoch
validation_steps=
valGenerator.samples//nBatches, # number of steps (batches of samples) to yield from validation_data generator before stopping at the end of every epoch
class_weight=None, # optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function
max_queue_size=10, # maximum size for the generator queue
workers=32, # maximum number of processes to spin up when using process-based threading
use_multiprocessing=True, # whether to use process-based threading
shuffle=False, # whether to shuffle the order of the batches at the beginning of each epoch
initial_epoch=0) # epoch at which to start training
print("%s: Model trained." % datetime.now().strftime('%Y-%m-%d_%H-%M-%S'))
# Save model
modelPath = os.path.join(resultPath, datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + '_modelArchitecture.h5')
weightsPath = os.path.join(resultPath, datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + '_modelWeights.h5')
model.save(modelPath)
model.save_weights(weightsPath)
print("%s: Model saved." % datetime.now().strftime('%Y-%m-%d_%H-%M-%S'))
return history, model
[...]
def evaluate_model(model, generator):
score = model.evaluate_generator(generator=generator, # Generator yielding tuples
steps=
generator.samples//nBatches) # number of steps (batches of samples) to yield from generator before stopping
print("%s: Model evaluated:"
"\n\t\t\t\t\t\t Loss: %.3f"
"\n\t\t\t\t\t\t Accuracy: %.3f" %
(datetime.now().strftime('%Y-%m-%d_%H-%M-%S'),
score[0], score[1]))
[...]
def main():
# Create model
modelUntrained = create_model(imagesize, nBands, nClasses)
# Prepare training and validation data
trainGenerator = generate_data(imagePathTraining, imagesize, nBatches)
valGenerator = generate_data(imagePathValidation, imagesize, nBatches)
# Train and save model
history, modelTrained = train_model(modelUntrained, nBatches, nEpochs, trainGenerator, valGenerator, resultPath)
# Evaluate on validation data
print("%s: Model evaluation (valX, valY):" % datetime.now().strftime('%Y-%m-%d_%H-%M-%S'))
evaluate_model(modelTrained, valGenerator)
# Evaluate on training data
print("%s: Model evaluation (trainX, trainY):" % datetime.now().strftime('%Y-%m-%d_%H-%M-%S'))
evaluate_model(modelTrained, trainGenerator)
Обновление
Я нашел несколько сайтов, которые сообщают оэта проблема:
До сих пор я безуспешно пытался следовать некоторым из предложенных ими решений.acc и loss по-прежнему отличаются от fit_generator() и evaluate_generator(), даже если для обучения и проверки используются одни и те же данные, созданные с помощью одного и того же генератора.Вот что я попробовал:
- статически устанавливая обучающую фазу для всего сценария или перед добавлением новых слоев к предварительно обученным
K.set_learning_phase(0) # testing
K.set_learning_phase(1) # training
- размораживаниевсе слои нормализации партии из предварительно обученной модели
for i in range(len(model.layers)):
if str.startswith(model.layers[i].name, 'bn'):
model.layers[i].trainable=True
- без добавления отсева или нормализации партии в качестве неподготовленных слоев
# Create pre-trained base model
basemodel = ResNet50(include_top=False, # exclude final pooling and fully connected layer in the original model
weights='imagenet', # pre-training on ImageNet
input_tensor=None, # optional tensor to use as image input for the model
input_shape=(imagesize, # shape tuple
imagesize,
nBands),
pooling=None, # output of the model will be the 4D tensor output of the last convolutional layer
classes=nClasses) # number of classes to classify images into
# Create new untrained layers
x = basemodel.output
x = GlobalAveragePooling2D()(x) # global spatial average pooling layer
x = Dense(1024, activation='relu')(x) # fully-connected layer
y = Dense(nClasses, activation='softmax')(x) # logistic layer making sure that probabilities sum up to 1
# Create model combining pre-trained base model and new untrained layers
model = Model(inputs=basemodel.input,
outputs=y)
# Freeze weights on pre-trained layers
for layer in basemodel.layers:
layer.trainable = False
# Define learning optimizer
learningRate = 0.01
optimizerSGD = optimizers.SGD(lr=learningRate, # learning rate.
momentum=0.9, # parameter that accelerates SGD in the relevant direction and dampens oscillations
decay=learningRate/nEpochs, # learning rate decay over each update
nesterov=True) # whether to apply Nesterov momentum
# Compile model
model.compile(optimizer=optimizerSGD, # stochastic gradient descent optimizer
loss='categorical_crossentropy', # objective function
metrics=['accuracy'], # metrics to be evaluated by the model during training and testing
loss_weights=None, # scalar coefficients to weight the loss contributions of different model outputs
sample_weight_mode=None, # sample-wise weights
weighted_metrics=None, # metrics to be evaluated and weighted by sample_weight or class_weight during training and testing
target_tensors=None) # tensor model's target, which will be fed with the target data during training
- с использованиемразличные предварительно обученные CNN в качестве базовой модели ( VGG19, InceptionV3, InceptionResNetV2, Xception )
from keras.applications.vgg19 import VGG19
basemodel = VGG19(include_top=False, # exclude final pooling and fully connected layer in the original model
weights='imagenet', # pre-training on ImageNet
input_tensor=None, # optional tensor to use as image input for the model
input_shape=(imagesize, # shape tuple
imagesize,
nBands),
pooling=None, # output of the model will be the 4D tensor output of the last convolutional layer
classes=nClasses) # number of classes to classify images into
Пожалуйста, дайте мне знать, если есть другие решения, которые мне не хватает.