如何使用 Keras 中训练有素的模型预测输入图像？

我才刚开始接触 Keras 和机器学习。

我训练了一个模型来对 2 个类别的图像进行分类，并使用 model.save() 保存了它。以下是我使用的代码：

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K

# dimensions of our images.
img_width, img_height = 320, 240

train_data_dir = 'data/train'
validation_data_dir = 'data/validation'
nb_train_samples = 200  #total
nb_validation_samples = 10  # total
epochs = 6
batch_size = 10

if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)
else:
    input_shape = (img_width, img_height, 3)

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=5)

model.save('model.h5')

训练成功，准确率为 0.98，相当不错。为了在新图像上加载和测试该模型，我使用了下面的代码：

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

model = load_model('model.h5')

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              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

它的输出结果是

[[0]]

为什么它不给出类的实际名称，为什么是"[[0]]"？

在此先表示感谢。

Løiten
已编辑的问题 24日 五月 2018 в 10:51
编程
computer-vision python machine-learning keras
18日 四月 2017 в 10:09
尚无意见

如果有人还在为预测图像而苦恼，下面是加载已保存模型并进行预测的优化代码：

# Modify 'test1.jpg' and 'test2.jpg' to the images you want to predict on

from keras.models import load_model
from keras.preprocessing import image
import numpy as np

# dimensions of our images
img_width, img_height = 320, 240

# load the model we saved
model = load_model('model.h5')
model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

# predicting images
img = image.load_img('test1.jpg', target_size=(img_width, img_height))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)

images = np.vstack([x])
classes = model.predict_classes(images, batch_size=10)
print classes

# predicting multiple images at once
img = image.load_img('test2.jpg', target_size=(img_width, img_height))
y = image.img_to_array(img)
y = np.expand_dims(y, axis=0)

# pass the list of multiple images np.vstack()
images = np.vstack([x, y])
classes = model.predict_classes(images, batch_size=10)

# print the classes, the images belong to
print classes
print classes[0]
print classes[0][0]
评论(3)
解决办法

keras predict_classes (docs) 输出一个类预测的 numpy 数组。在你的模型中，它是最后（softmax）层中激活度最高的神经元的索引。[[0]]表示你的模型预测测试数据是 0 类（通常你会传入多个图像，结果会像[[0],1,1,[0]]`）。

您必须将实际标签（例如，"'癌症'，'不是癌症'）转换为二进制编码（"0 "表示'癌症'，"1 "表示'不是癌症'），以便进行二进制分类。然后，您将把输出为[[0]]的序列解释为具有类别标签'癌症'。

DNK
编辑本段答案18日 四月 2017 в 11:05
评论(10)

您可以使用 model.predict() 来预测单张图像的类别，如下所示 [doc]：

# load_model_sample.py
from keras.models import load_model
from keras.preprocessing import image
import matplotlib.pyplot as plt
import numpy as np
import os

def load_image(img_path, show=False):

    img = image.load_img(img_path, target_size=(150, 150))
    img_tensor = image.img_to_array(img)                    # (height, width, channels)
    img_tensor = np.expand_dims(img_tensor, axis=0)         # (1, height, width, channels), add a dimension because the model expects this shape: (batch_size, height, width, channels)
    img_tensor /= 255.                                      # imshow expects values in the range [0, 1]

    if show:
        plt.imshow(img_tensor[0])                           
        plt.axis('off')
        plt.show()

    return img_tensor

if __name__ == "__main__":

    # load model
    model = load_model("model_aug.h5")

    # image path
    img_path = '/media/data/dogscats/test1/3867.jpg'    # dog
    #img_path = '/media/data/dogscats/test1/19.jpg'      # cat

    # load a single image
    new_image = load_image(img_path)

    # check prediction
    pred = model.predict(new_image)

在此示例中，图像以形状为"(1, height, width, channels) "的 numpy 数组加载。然后，我们将其加载到模型中，并预测其类别，预测结果以 [0, 1] 范围内的实数值返回（本例中为二元分类）。

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