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，但是我得到的结果是二进制的(例如1.0和0.0).如何获得这些精度(例如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)?

培训代码:

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:

---

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

---

文件结构如下:

---

The file structures is like:

• 测试集

• test_set

• 贝尼涅
  • benigne_images
  • benigne
    • benigne_images
    • melignant_images

    训练集

    训练集的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:正如您在注释中所阐明的，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，您必须首先按照训练阶段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是给定图像属于第一类(即肯定类)的概率.

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

    如果您具有测试数据生成器，则可以使用 evaluate_generator() 以获得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！