无法在 Keras 中使用 VGG19 预测单个图像的标签
Cannot predict the label for a single image with VGG19 in Keras
我根据[本教程]使用迁移学习方法在 Keras 中使用经过训练的 VGG19 模型(https://towardsdatascience.com/keras-transfer-learning-for-beginners-6c9b8b7143e
).它展示了如何训练模型,但没有展示如何为预测准备测试图像。
评论区说:
Get an image, preprocess the image using the same preprocess_image
function, and call model.predict(image)
. This will give you the prediction of the model on that image. Using argmax(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。我做了一些搜索,想到了使用 this tutorial.
提出的方法
但这给出了一个错误提示:
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。这是训练模型的完整代码以及我是如何得到这个错误的:
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:
ValueError: `decode_predictions` expects a batch of predictions (i.e. a 2D array of shape (samples, 1000)). Found array with shape: (1, 12)
我应该如何正确地输入我的测试图像并获得预测?
decode_predictions
用于根据ImageNet数据集中类的标签解码模型的预测,该数据集有1000类。但是,您的微调模型只有 12 类。因此,在这里使用 decode_predictions
是没有意义的。当然,您必须知道这 12 类 个标签是什么。因此,只需取预测中最大分数的索引并找到其标签:
# 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]])
我根据[本教程]使用迁移学习方法在 Keras 中使用经过训练的 VGG19 模型(https://towardsdatascience.com/keras-transfer-learning-for-beginners-6c9b8b7143e ).它展示了如何训练模型,但没有展示如何为预测准备测试图像。
评论区说:
Get an image, preprocess the image using the same
preprocess_image
function, 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。我做了一些搜索,想到了使用 this tutorial.
但这给出了一个错误提示:
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。这是训练模型的完整代码以及我是如何得到这个错误的:
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:
ValueError: `decode_predictions` expects a batch of predictions (i.e. a 2D array of shape (samples, 1000)). Found array with shape: (1, 12)
我应该如何正确地输入我的测试图像并获得预测?
decode_predictions
用于根据ImageNet数据集中类的标签解码模型的预测,该数据集有1000类。但是,您的微调模型只有 12 类。因此,在这里使用 decode_predictions
是没有意义的。当然,您必须知道这 12 类 个标签是什么。因此,只需取预测中最大分数的索引并找到其标签:
# 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]])