无法在Keras中使用VGG19预测单个图像的标签 [英] Cannot predict the label for a single image with VGG19 in Keras
问题描述
根据[本教程]，我正在使用转移学习方法在Keras中使用按训练的VGG19模型(
I'm using transfer learning method to use per-trained VGG19 model in Keras according to [this tutorial](https://towardsdatascience.com/keras-transfer-learning-for-beginners-6c9b8b7143e ). It shows how to train the model but NOT how to prepare test images for the predictions.
在评论部分中说:
获取图像，使用相同的
preprocess_image函数预处理图像，然后调用model.predict(image).这将为您提供该图像上模型的预测.使用argmax(prediction)，您可以找到图像所属的类.
Get an image, preprocess the image using the same
preprocess_imagefunction, and callmodel.predict(image). This will give you the prediction of the model on that image. Usingargmax(prediction), you can find the class to which the image belongs.
我找不到代码中使用的名为preprocess_image的函数.我进行了一些搜索，并考虑使用此方法提出的方法教程.
I can not find a function named preprocess_image used in the code. I did some searches and thought of using the method proposed by this tutorial.
但这会给出错误信息:
decode_predictions expects a batch of predictions (i.e. a 2D array of shape (samples, 1000)). Found array with shape: (1, 12)
我的数据集有12个类别.这是训练模型以及如何得到此错误的完整代码:
My dataset has 12 categories. Here is the full code for training the model and how I got this error:
import pandas as pd
import numpy as np
import os
import keras
import matplotlib.pyplot as plt
from keras.layers import Dense, GlobalAveragePooling2D
from keras.applications.vgg19 import VGG19
from keras.preprocessing import image
from keras.applications.vgg19 import preprocess_input
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model
from keras.optimizers import Adam
base_model = VGG19(weights='imagenet', include_top=False)
x=base_model.output
x=GlobalAveragePooling2D()(x)
x=Dense(1024,activation='relu')(x)
x=Dense(1024,activation='relu')(x)
x=Dense(512,activation='relu')(x)
preds=Dense(12,activation='softmax')(x)
model=Model(inputs=base_model.input,outputs=preds)
# view the layer architecture
# for i,layer in enumerate(model.layers):
# print(i,layer.name)
for layer in model.layers:
layer.trainable=False
for layer in model.layers[:20]:
layer.trainable=False
for layer in model.layers[20:]:
layer.trainable=True
train_datagen=ImageDataGenerator(preprocessing_function=preprocess_input)
train_generator=train_datagen.flow_from_directory('dataset',
target_size=(96,96), # 224, 224
color_mode='rgb',
batch_size=64,
class_mode='categorical',
shuffle=True)
model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])
step_size_train=train_generator.n//train_generator.batch_size
model.fit_generator(generator=train_generator,
steps_per_epoch=step_size_train,
epochs=5)
# model.predict(new_image)
IPython:
In [3]: import classify_tl
Found 4750 images belonging to 12 classes.
Epoch 1/5
74/74 [==============================] - 583s 8s/step - loss: 2.0113 - acc: 0.4557
Epoch 2/5
74/74 [==============================] - 576s 8s/step - loss: 0.8222 - acc: 0.7170
Epoch 3/5
74/74 [==============================] - 563s 8s/step - loss: 0.5875 - acc: 0.7929
Epoch 4/5
74/74 [==============================] - 585s 8s/step - loss: 0.3897 - acc: 0.8627
Epoch 5/5
74/74 [==============================] - 610s 8s/step - loss: 0.2689 - acc: 0.9071
In [6]: model = classify_tl.model
In [7]: print(model)
<keras.engine.training.Model object at 0x7fb3ad988518>
In [8]: from keras.preprocessing.image import load_img
In [9]: image = load_img('examples/0021e90e4.png', target_size=(96,96))
In [10]: from keras.preprocessing.image import img_to_array
In [11]: image = img_to_array(image)
In [12]: image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
In [13]: from keras.applications.vgg19 import preprocess_input
In [14]: image = preprocess_input(image)
In [15]: yhat = model.predict(image)
In [16]: print(yhat)
[[1.3975363e-06 3.1069856e-05 9.9680350e-05 1.7175063e-03 6.2767825e-08
2.6133494e-03 7.2859187e-08 6.0187017e-07 2.0794137e-06 1.3714411e-03
9.9416250e-01 2.6067207e-07]]
In [17]: from keras.applications.vgg19 import decode_predictions
In [18]: label = decode_predictions(yhat)
IPython提示中的最后一行导致以下错误:
Last line in the IPython prompt lead to the following error:
ValueError: `decode_predictions` expects a batch of predictions (i.e. a 2D array of shape (samples, 1000)). Found array with shape: (1, 12)
我应该如何正确输入测试图像并获得预测?
How should I properly feed my test image and get the predictions?
推荐答案
decode_predictions用于根据ImageNet数据集中具有1000个类的类的标签对模型的预测进行解码.但是，您经过微调的模型只有12个类.因此，在此处使用decode_predictions没有任何意义.当然，您必须知道这12个类别的标签是什么.因此，只需将预测中最高分的索引作为索引并找到其标签即可:
decode_predictions is used for decoding predictions of a model according to the labels of classes in ImageNet dataset which has 1000 classes. However, your fine-tuned model has only 12 classes. Therefore, it does not make sense to use decode_predictions here. Surely, you must know what the labels for those 12 classes are. Therefore, just take the index of maximum score in the prediction and find its label:
# create a list containing the class labels
class_labels = ['class1', 'class2', 'class3', ...., 'class12']
# find the index of the class with maximum score
pred = np.argmax(class_labels, axis=-1)
# print the label of the class with maximum score
print(class_labels[pred[0]])
这篇关于无法在Keras中使用VGG19预测单个图像的标签的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
