预言说什么？ CNN 凯拉斯答案

【问题标题】：What does the prediction say? CNN Keras预言说什么？ CNN 凯拉斯
【发布时间】：2017-10-09 17:14:38
【问题描述】：

我创建了一个CNN 模型来尝试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我保存的模型：

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')

作为输出我得到这个：

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.]]

但是我怎么知道预测是狗还是猫？有了这个结果，我仍然不知道图像是狗还是猫。

【问题讨论】：

确实有人需要阅读一些基础教程。忽略这一点在未来不会有太大的乐趣。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？
我没有说有什么问题。我暗示了关于解释输出的 3 件最重要的事情。
fcl3 = Dense(2,activation='softmax')，表示您正在进行两类分类，并且 [0 1] 输出表明它预测了第二类 (1)，无论它是什么
好的，谢谢

标签： python computer-vision keras artificial-intelligence

【解决方案1】：

除非您指定标签，否则您的生成器会自动为您创建分类标签。您可以使用train_generator.class_indices 检查那些类标签的顺序是字母数字，所以cats=0 dogs=1

【讨论】：