Keras返回二进制结果

Question

我想预测两种疾病的类型，但我得到的结果是二进制的(比如1.0和0.0(。我如何才能得到这些疾病的准确性(比如0.7213(？

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训练代码：

from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
# Intialising the CNN
classifier = Sequential()
# Step 1 - Convolution
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Step 3 - Flattening
classifier.add(Flatten())
# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Part 2 - Fitting the CNN to the images
import h5py
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('test_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')

classifier.fit_generator(training_set,
steps_per_epoch = 100,
epochs = 1,
validation_data = test_set,
validation_steps = 100)

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单个预测代码：

import numpy as np
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img,image
test_image = image.load_img('path_to_image', target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
print(result[0][0]) # Prints 1.0 or 0.0
# I want accuracy rate for this prediction like 0.7213

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文件结构如下：

• 测试设置

  • 贝宁
    • 贝宁图片
  • 香料
    • melignant_images

• 训练集

训练集结构也与测试集相同。

Answer 1

更新：正如您在评论中所澄清的，您正在寻找给定一个测试样本的每个类的概率。因此可以使用predict方法。但是，请注意，您必须首先以与训练阶段相同的方式对图像进行预处理：

test_image /= 255.0
result = classifier.predict(test_image)

CCD_ 2将是给定图像属于第一类(即正类(的概率。

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如果您有测试数据生成器，那么您可以使用evaluate_generator()来获得测试数据上模型的损失以及准确性(或您设置的任何其他度量(。

例如，在拟合模型之后，即使用fit_generator，您可以在测试数据生成器上使用evaluate_generator，即test_set:

loss, acc = evaluate_generator(test_set)