keras - 使用重新缩放时 model.predict 的意外输出

Question

首先：我知道这篇文章，但它没有提供答案。

我正在像这样构建我的模型：

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout
from keras.preprocessing.image import ImageDataGenerator  # for data augmentation
import pandas as pd  # to save .csv files
from time import perf_counter  # to track runtime
from keras.metrics import TrueNegatives, TruePositives, FalseNegatives, FalsePositives


def build_model(dimension):  
    model = Sequential()
    model.add(Conv2D(32, (11,11), activation='relu',
                     input_shape=(dimension, dimension, 3))) 
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(64, (5, 5), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(128, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(128, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())  # to prepare for dropout
    model.add(Dropout(0.2))  # to prevent overfitting
    model.add(Dense(256, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))  
    model.compile(optimizer='rmsprop',
                  loss='binary_crossentropy',
                  metrics=['accuracy',
                           TruePositives(),
                           TrueNegatives(),
                           FalsePositives(),
                           FalseNegatives()
                           ]
                  )
    return model

def train_model(epoch, batch_size, run, subrun):
    dimension = 200
    model = build_model(dimension)
    train_datagen = ImageDataGenerator(validation_split=0.2,
                                       # samplewise_std_normalization=True,
                                       rotation_range=40,
                                       rescale=1./255,
                                       shear_range=0.2,
                                       zoom_range=0.2,
                                       horizontal_flip=True,
                                       width_shift_range=0.2,
                                       height_shift_range=0.2,
                                       fill_mode='nearest'
                                       )
    training_set = train_datagen.flow_from_directory('train6', 
                                                     target_size=(dimension, dimension),
                                                     color_mode='rgb',  # default
                                                     class_mode='binary',
                                                     batch_size=batch_size,
                                                     save_to_dir=None,
                                                     interpolation='nearest', 
                                                     subset='training')
    validation_set = train_datagen.flow_from_directory('train6',  
                                                       target_size=(dimension, dimension),
                                                       color_mode='rgb',  # default
                                                       class_mode='binary',
                                                       batch_size=batch_size,
                                                       save_to_dir=None,
                                                       # if 'str', saves augmented images for visualisation
                                                       interpolation='nearest',  
                                                       subset='validation')

    start_time = perf_counter()  # start counting
    history = model.fit_generator(training_set,
                                  epochs=epoch,
                                  steps_per_epoch=training_set.samples // batch_size,
                                  validation_data=validation_set,
                                  validation_steps=validation_set.samples // batch_size,
                                  verbose=2)
    stop_time = perf_counter()  # stop counting

    # saving trained model & history file
    model.save_weights('models/cat_dog_classifier_{0}_{1}.h5'.format(run, subrun))  # save model weights
    hist_pd = pd.DataFrame(history.history)  # making panda file of history.history
    hist_csv_file = 'histories/history_{0}_{1}.csv'.format(run, subrun)  # defining name for csv file
    with open(hist_csv_file, mode='w') as f:  # saving the pd file as csv
        hist_pd.to_csv(f)

    return stop_time - start_time

我使用以下代码来获取概率：

from build_model import build_model
from keras.preprocessing import image
import numpy as np

run = 'A28'
subrun = 1
dimension = 200
# build model
model = build_model(dimension)
model.load_weights('models/cat_dog_classifier_{0}_{1}.h5'.format(run, subrun))
# Get test image ready
amount_of_images = 10
predictions = np.zeros((amount_of_images, 2))
labels = np.zeros(amount_of_images)
for i in range(amount_of_images):
    image_name = 1 + i  # choose what image to start from
    test_image = image.load_img('test1/{}.jpg'.format(image_name), target_size=(dimension, dimension))
    test_image = image.img_to_array(test_image)
    test_image = np.expand_dims(test_image, axis=0)
    label = model.predict_classes(test_image, batch_size=1)
    labels[i] = label
    prediction = model.predict(test_image, batch_size=1)
    print(prediction)
print(labels)

当我在不使用重新缩放或标准化的情况下训练我的模型时，预测是预期的概率。但是，当我使用其中任何一个时，它只返回 0 和 1（与 predict_classes 相同的标签）。我试图运行上面链接中提供的虚拟代码，它按预期工作；我想这是有道理的，因为当我没有使用重新缩放时脚本也可以正常运行。但是，我真的很想能够使用重新缩放。有谁知道出了什么问题？

标签： kerasclassificationconv-neural-networkimage-preprocessing