Question

我正在开始学习ML，遇到了一个巨大的坎,，花了好几个小时看着它。我想获得输出的predict（）概率，但是对于每个测试图像，预测仅输出[[1.]]。使用大量的训练数据和更多的时期，acc和validation acc分别高达约90％。这只是二进制分类，但是我不希望使用预报类。我不知道为什么要打印[[1。]]

这是我正在使用的代码：

A = 1
B = 2
C = 3

list = [A, B, C]

对于预测：

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K

img_width, img_height = 150, 150

train_data_dir = 'D:\Machine_Learning\\train'
validation_data_dir = 'D:\Machine_Learning\\test'
nb_train_samples = 20000
nb_validation_samples = 7000
epochs = 50
batch_size = 40

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

# Build model structure
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))



model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

# Image augmentation
train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1. / 255)

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')

model.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)

model.save('first_try.h5')
model.save_weights('my_weights.model')

如何使用Keras中训练有素的模型预测图像的输入概率？

0 个答案: