Keras 返回二进制结果 [英] Keras returns binary results

问题描述

我想预测 2 种疾病的种类，但我得到的结果是二进制的(如 1.0 和 0.0).我怎样才能获得这些的准确性(如 0.7213)?

I want to predict the kind of 2 diseases but I get results as binary (like 1.0 and 0.0). How can I get accuracy of these (like 0.7213)?

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)

<小时>

单一预测代码:

---

Single prediction code:

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

<小时>

文件结构如下:

---

The file structures is like:

• 测试集

• 良性
  • benigne_images
  • melignant_images

  训练集

  训练集结构也与测试集相同.

  Training set structure is also the same as test set.

  推荐答案

  更新: 正如您在评论中阐明的那样，您正在寻找给定一个测试样本的每个类的概率.因此，您可以使用 predict 方法.但是，请注意，您必须首先按照与训练阶段相同的方式对图像进行预处理:

  Update: As you clarified in the comments, you are looking for the probabilities of each class given one single test sample. Therefore you can use predict method. However, note that you must first preprocess the image the same way you have done in the training phase:

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

  result 将是给定图像属于第一类(即正类)的概率.

  The result would be the probability of the given image belonging to class one (i.e. positive class).

  如果你有测试数据的生成器，那么你可以使用evaluate_generator() 以获取模型在测试数据上的损失以及准确性(或您设置的任何其他指标).

  If you have a generator for test data, then you can use evaluate_generator() to get the loss as well as the accuracy (or any other metric you have set) of the model on the test data.

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

  For example, right after fitting the model, i.e. using fit_generator, you can use evaluate_generator on your test data generator, i.e. test_set:

  loss, acc = evaluate_generator(test_set)
  

  这篇关于Keras 返回二进制结果的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！