我创建了一个CNN模型，试图预测图像是狗还是猫，但在输出结果中我不知道它预测了什么。请看下面的代码：
import pandas as pdfrom keras.models import Sequentialfrom keras.preprocessing.image import ImageDataGeneratorfrom keras.layers import Dense, Flatten, Conv2D, Dropout, MaxPooling2Dfrom scipy import miscimport numpy as npdef build_classifier(): # 模型基于 'https://www.researchgate.net/profile/Le_Lu/publication/277335071/figure/fig8/AS:294249976352779@1447166069905/Figure-8-The-proposed-CNN-model-architecture-is-composed-of-five-convolutional-layers.png' # 由于处理原因，最好在不创建变量的情况下添加层，但对于小数据集来说影响不大。 classifier = Sequential() conv1 = Conv2D(filters=64, kernel_size=(2,2), activation='relu', input_shape=(64,64,3)) conv2 = Conv2D(filters=192, kernel_size=(2,2), activation='relu') conv3 = Conv2D(filters=384, kernel_size=(2,2), activation='relu') conv4 = Conv2D(filters=256, kernel_size=(2,2), activation='relu') conv5 = Conv2D(filters=256, kernel_size=(2,2), activation='relu') pooling1 = MaxPooling2D(pool_size=(2,2)) pooling2 = MaxPooling2D(pool_size=(2,2)) pooling3 = MaxPooling2D(pool_size=(2,2)) fcl1 = Dense(1024, activation='relu') fcl2 = Dense(1024, activation='relu') fcl3 = Dense(2, activation='softmax') dropout1= Dropout(0.5) dropout2 = Dropout(0.5) flatten = Flatten() layers = [conv1, pooling1, conv2, pooling2, conv3, conv4, conv5, pooling3, flatten, fcl1, dropout1, fcl2, dropout2, fcl3] for l in layers: classifier.add(l) return classifiermodel = build_classifier()model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])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( 'dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='categorical')validation_generator = test_datagen.flow_from_directory( 'dataset/test_set', target_size=(64, 64), batch_size=32, class_mode='categorical')model.fit_generator( train_generator, steps_per_epoch=200, epochs=32, validation_data=validation_generator, validation_steps=100)model.save('model.h5')model.save_weights('model_weights.h5')我在另一个文件中打开了保存的模型：
from keras.models import load_modelfrom scipy import miscimport numpy as npdef single_pred(filepath, model): classifier = load_model(model) img = misc.imread(filepath) img = misc.imresize(img, (64,64,3)) img = np.expand_dims(img, 0) print(classifier.predict(img))if __name__ == '__main__': single_pred('/home/leonardo/Desktop/Help/dataset/single_prediction/cat_or_dog_2.jpg', 'model.h5')输出结果如下：
Using TensorFlow backend.2017-10-09 14:06:25.520018: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.2017-10-09 14:06:25.520054: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.[[ 0. 1.]]但是，我如何知道预测结果是狗还是猫呢？有了这个结果，我仍然不知道图像是狗还是猫。
回答：
除非你指定标签，否则你的生成器会自动为你创建分类标签。你可以使用 train_generator.class_indices 来检查这些标签。类标签的顺序是按字母数字排序的，所以猫=0，狗=1
