Keras model.predict总是0 [英] Keras model.predict always 0

问题描述

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

I am using keras applications for transfer learning with resnet 50 and inception v3 but when predicting always get [[ 0.]]

以下代码用于二进制分类问题.我也尝试过vgg19和vgg16，但是它们可以正常工作，它只是resnet和inception.数据集是50/50分割.而且我只更改每个模型的model = applications.resnet50.ResNet50代码行.

The below code is for a binary classification problem. I have also tried vgg19 and vgg16 but they work fine, its just resnet and inception. The dataset is a 50/50 split. And I am only changing the model = applications.resnet50.ResNet50 line of code for each model.

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)的预期准确度约为80％，只是它的预测始终为0.

Note That model_final.evaluate_generator(validation_generator, nb_validation_samples) provides an expected accuracy like 80% its just predict that is always 0.

发现vgg19和vgg16可以正常工作，但resnet50和inception却不能正常工作，这很奇怪.这些模型还需要其他功能吗?

Just find it strange that vgg19 and vgg16 work fine but not resnet50 and inception. Do these models require something else to work?

任何见识都会很棒.

谢谢.

推荐答案

我遇到了类似的问题.您正在训练期间将所有RGB值从0-255缩放到0-1.

I was running into similar problem. You are scaling all the RGB values from 0-255 to 0-1 during training.

在预测时也应这样做. 尝试 x = img_to_array(img) x = x/255

Thse same should be done at the time of prediction. Try x = img_to_array(img) x = x/255

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