如何使用 Keras 中的训练模型预测输入图像，.h5 保存文件？

Question

我只是从一般的 Keras 和机器学习开始。

我训练了一个模型来对来自 9 个类别的图像进行分类，并使用 model.save() 保存它。这是我使用的代码：

from keras.layers import Input, Lambda, Dense, Flatten
from keras.models import Model
from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
import numpy as np
from glob import glob
import matplotlib.pyplot as plt

# re-size all the images to this
IMAGE_SIZE = [224, 224]

train_path = 'Datasets/Train'
valid_path = 'Datasets/Test'

# add preprocessing layer to the front of resnet
resnet = ResNet50(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)

# don't train existing weights
for layer in resnet.layers:
    layer.trainable = False

    # useful for getting number of classes
folders = glob('Datasets/Train/*')

# our layers - you can add more if you want
x = Flatten()(resnet.output)
# x = Dense(1000, activation='relu')(x)
prediction = Dense(len(folders), activation='softmax')(x)

# create a model object
model = Model(inputs=resnet.input, outputs=prediction)

# view the structure of the model
model.summary()

# tell the model what cost and optimization method to use
model.compile(
    loss='categorical_crossentropy',
    optimizer='adam',
    metrics=['accuracy']
)

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale=1. / 255,
                                   shear_range=0.1,
                                   zoom_range=0.1,
                                   horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1. / 255)

training_set = train_datagen.flow_from_directory('Datasets/Train',
                                                 target_size=(224, 224),
                                                 batch_size=32,
                                                 class_mode='categorical')

test_set = test_datagen.flow_from_directory('Datasets/Test',
                                            target_size=(224, 224),
                                            batch_size=32,
                                            class_mode='categorical')


# fit the model
r = model.fit_generator(
    training_set,
    validation_data=test_set,
    epochs=3,
    steps_per_epoch=len(training_set),
    validation_steps=len(test_set)
)
def plot_loss_accuracy(r):
    fig = plt.figure(figsize=(12, 6))
    ax = fig.add_subplot(1, 2, 1)
    ax.plot(r.history["loss"], 'r-x', label="Train Loss")
    ax.plot(r.history["val_loss"], 'b-x', label="Validation Loss")
    ax.legend()
    ax.set_title('cross_entropy loss')
    ax.grid(True)

    ax = fig.add_subplot(1, 2, 2)
    ax.plot(r.history["accuracy"], 'r-x', label="Train Accuracy")
    ax.plot(r.history["val_accuracy"], 'b-x', label="Validation Accuracy")
    ax.legend()
    ax.set_title('acuracy')
    ax.grid(True)

它训练成功。为了在新图像上加载和测试这个模型，我使用了以下代码：

from keras.models import load_model
import cv2
import numpy as np

model = load_model('model.h5')

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

img = cv2.imread('test.jpg')
img = cv2.resize(img,(320,240))
img = np.reshape(img,[1,320,240,3])

classes = model.predict_classes(img)

print(classes)

它输出：

AttributeError：“模型”对象没有属性“predict_classes”

为什么它甚至不能预测？

谢谢，

Answer 1

predict_classes 仅适用于顺序 api http://faroit.com/keras-docs/1.0.0/models/sequential/

所以，你首先需要得到概率，并以最大概率为类。

from keras.models import load_model
import cv2
import numpy as np


class_names = ['a', 'b', 'c', ...] # fill the rest

model = load_model('model.h5')

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

img = cv2.imread('test.jpg')
img = cv2.resize(img,(320,240))
img = np.reshape(img,[1,320,240,3])

classes = np.argmax(model.predict(img), axis = -1)

print(classes)

names = [class_names[i] for i in classes]

print(names)