问我想将我的CNN模型的特定预测更改为概率。
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Stack Overflow用户

提问于 2020-11-07 17:24:39

回答 1查看 73关注 0票数 1

我训练了一个模型来将图片分为两种不同的类型。一切都运行得很好，但我的模型只能做一个特定的预测(在我的例子中是1或0)，但我感兴趣的是一个更像概率的预测(例如90%1和10%0)。我的代码中现在应该修改的部分在哪里？是不是sigmoid函数最终决定了它是1还是0？帮帮忙就好了。提前谢谢。

import numpy as np
from keras.callbacks import TensorBoard
from keras import regularizers
from keras.models import Sequential
from keras.layers import Activation, Dropout, Flatten, Dense, Conv2D, MaxPooling2D
from keras.optimizers import Adam
from keras.metrics import categorical_crossentropy
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from keras.layers.normalization import BatchNormalization

from utils import DataGenerator, PATH

train_path = 'Dataset/train'
valid_path = 'Dataset/valid'
test_path = 'Dataset/test'

model = Sequential()
model.add(Conv2D(16, (3, 3), input_shape=(640, 640, 1), padding='same', activation='relu',
                 kernel_regularizer=regularizers.l2(1e-4),
                 bias_regularizer=regularizers.l2(1e-4)))
model.add(MaxPooling2D(pool_size=(4, 4)))

model.add(Conv2D(32, (3, 3), activation='relu',
                 kernel_regularizer=regularizers.l2(1e-4),
                 bias_regularizer=regularizers.l2(1e-4)))
model.add(MaxPooling2D(pool_size=(5, 5)))

model.add(Conv2D(64, (3, 3), activation='relu',
                 kernel_regularizer=regularizers.l2(1e-4),
                 bias_regularizer=regularizers.l2(1e-4)))
model.add(MaxPooling2D(pool_size=(6, 6)))

model.add(Flatten())
model.add(Dense(64, activation='relu',
                kernel_regularizer=regularizers.l2(1e-4),
                bias_regularizer=regularizers.l2(1e-4)))
model.add(Dropout(0.3))
model.add(Dense(1, activation='sigmoid',
                kernel_regularizer=regularizers.l2(1e-4),
                bias_regularizer=regularizers.l2(1e-4)))
print(model.summary())

model.compile(loss='binary_crossentropy', optimizer=Adam(lr=1e-3), metrics=['accuracy'])

epochs = 50
batch_size = 16

datagen = DataGenerator()
datagen.load_data()

model.fit_generator(datagen.flow(batch_size=batch_size), epochs=epochs, validation_data=datagen.get_validation_data(),
                    callbacks=[TensorBoard(log_dir=PATH+'/tensorboard')])

#model.save_weights('first_try.h5')
model.save('second_try')

如果我尝试在我的模型中获得一张图片，如下所示：

path = 'train/clean/picturenumber2'  
def prepare(filepath):   
  IMG_SIZE = 640    
  img_array = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)    
  new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))   
  return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1) 

model = tf.keras.models.load_model('second_try') 
prediction = model.predict(prepare(path))
print(prediction)