予測は何を表していますか？ CNN Keras

Question

CNNモデルを作成して、画像が犬か猫のどちらかであるかどうかを予測しようとしましたが、出力で予測されたことがわかりません。以下を参照してください：

import pandas as pd 
from keras.models import Sequential 
from keras.preprocessing.image import ImageDataGenerator 
from keras.layers import Dense, Flatten, Conv2D, Dropout, MaxPooling2D 
from scipy import misc 
import numpy as np 

def build_classifier(): 
    #Model based on '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' 
    #It's smarter to add layer without creating variables because of the processing, but as a small dataset it doesn't matter a lot. 
    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 classifier 

model = 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') 

私は別のファイルで私の保存されたモデル開か：予測は、それがあることを述べている場合

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.]] 

しかし、どのように知っている：私はこれを取得、出力として

from keras.models import load_model 
from scipy import misc 
import numpy as np 

def 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') 

を犬や猫。この結果を手に入れても、イメージが犬か猫かは分かりません。

Answer 1

ラベルを指定しない限り、ジェネレータは自動的にカテゴリラベルを作成します。 train_generator.class_indices クラスラベルの順序は英数字ですので、cats = 0 dogs = 1