Keras model.predict 总是 0

Question

我正在使用 keras 应用程序通过 resnet 50 和 inception v3 进行迁移学习，但是在预测时总是得到 [[ 0.]]

以下代码用于二进制分类问题。我也尝试过 vgg19 和 vgg16 但它们工作正常，它只是 resnet 和 inception。数据集是 50/50 拆分。而且我只是更改每个模型的model = applications.resnet50.ResNet50 代码行。

下面是代码：

from keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_loss', patience=2)

img_width, img_height = 256, 256
train_data_dir = xxx
validation_data_dir = xxx
nb_train_samples = 14000
nb_validation_samples = 6000 
batch_size = 16
epochs = 50

if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)
else:
    input_shape = (img_width, img_height, 3)

model = applications.resnet50.ResNet50(weights = "imagenet", include_top=False, input_shape = (img_width, img_height, 3))


    from keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_loss', patience=2)

img_width, img_height = 256, 256
train_data_dir = xxx
validation_data_dir = xxx
nb_train_samples = 14000
nb_validation_samples = 6000 
batch_size = 16
epochs = 50

if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)
else:
    input_shape = (img_width, img_height, 3)

model = applications.resnet50.ResNet50(weights = "imagenet", include_top=False, input_shape = (img_width, img_height, 3))


#Freeze the layers which you don't want to train. Here I am freezing the first 5 layers.
for layer in model.layers[:5]:
    layer.trainable = False

#Adding custom Layers 
x = model.output
x = Flatten()(x)
x = Dense(1024, activation="relu")(x)
x = Dropout(0.5)(x)
#x = Dense(1024, activation="relu")(x)
predictions = Dense(1, activation="sigmoid")(x)

# creating the final model 
model_final = Model(input = model.input, output = predictions)

# compile the model 
model_final.compile(loss = "binary_crossentropy", optimizer = optimizers.SGD(lr=0.0001, momentum=0.9), metrics=["accuracy"])


# Initiate the train and test generators with data Augumentation 
train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

test_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

# Save the model according to the conditions  
#checkpoint = ModelCheckpoint("vgg16_1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
#early = EarlyStopping(monitor='val_acc', min_delta=0, patience=10, verbose=1, mode='auto')



model_final.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size,
    callbacks=[early_stopping])



from keras.models import load_model
import numpy as np
from keras.preprocessing.image import img_to_array, load_img

#test_model = load_model('vgg16_1.h5')
img = load_img('testn7.jpg',False,target_size=(img_width,img_height))
x = img_to_array(img)
x = np.expand_dims(x, axis=0)
#preds = model_final.predict_classes(x)
prob = model_final.predict(x, verbose=0)
#print(preds)
print(prob)

请注意，model_final.evaluate_generator(validation_generator, nb_validation_samples) 提供了 80% 的预期准确度，它只是预测始终为 0。

只是觉得奇怪的是 vgg19 和 vgg16 工作正常，但 resnet50 和 inception 却不行。这些模型是否需要其他东西才能工作？

任何见解都会很棒。

提前致谢。

Answer 1

我遇到了类似的问题。您在训练期间将所有 RGB 值从 0-255 缩放到 0-1。

在预测时也应该这样做。 尝试 x = img_to_array(img) x = x/255