Question

我创建了一个CNN模型，试图预测图像是狗还是猫，但在输出中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')

我在另一个档案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')

作为输出我得到了这个：

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.]]

但是如何知道预测是否是狗或猫。手中有这个结果我仍然不知道图像是狗还是猫。

Answer 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检查这些内容类标签的顺序是字母数字，因此cats = 0 dogs = 1

预测说的是什么？ CNN Keras

1 个答案: