Python 预测是怎么说的？CNN凯拉斯

我创建了一个CNN模型，试图预测图像是狗还是猫，但在输出上我不知道它预测了什么。见下文：

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')

我在另一个文件中打开了保存的模型：

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')

作为输出，我得到如下结果：

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.]]

---

但是如何知道预言说它是狗还是猫呢。有了这个结果，我仍然不知道图像是狗还是猫。

除非您指定标签，否则生成器将自动为您创建分类标签。您可以使用

train\u generator.class\u index

---

类标签的顺序是字母数字，因此cats=0 dogs=1

有些人确实需要阅读一些基础教程。忽略这一点在未来将不会有多大乐趣。提示：看看你的最后一层

fcl3=Dense（2，activation='softmax'）

，你的输入形状和你的损失！输入形状有什么问题？我没说有什么问题。关于解释输出，我暗示了三个最重要的事情。fcl3=Dense（2，activation='softmax'），表示您正在进行两类分类，[01]输出表示它预测了第二类（1），不管它是什么，谢谢