如何在Keras中使用训练有素的模型预测输入图像？
computer-vision
keras
machine-learning
python
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0

一般来说，我只是从keras和机器学习开始。

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()将其保存。这是我使用的代码： 

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')

它成功地以0.98的精度进行了训练，相当不错。为了在新图像上加载并测试该模型，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='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

它输出：

[[0]]

为什么不给出类的实际名称以及为什么[[0]] ？

提前致谢。

参考资料：
Stack Overflow
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如果有人仍在努力对图像进行预测，则以下是优化的代码，用于加载保存的模型并进行预测： 

# 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]
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keras Forecast_classes（ docs ）输出类别预测的numpy数组。在您的模型案例中，哪个是来自您的最后一个（softmax）层的最高激活神经元的索引。 [[0]]表示您的模型预测您的测试数据为0类。（通常，您将传递多个图像，结果看起来像[[0], [1], [1], [0]] ）

您必须将实际标签（例如'cancer', 'not cancer' ）转换为二进制编码（ 0表示'cancer'， 1表示'not Cancer'）进行二进制分类。然后，您将[[0]]序列输出解释为具有类标签'cancer'

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您可以使用model.predict()来预测单个图像的类别，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，图像被加载为具有形状(1, height, width, channels)的numpy数组。然后，将其加载到模型中并预测其类，以[0，1]范围内的实际值返回（在此示例中为二进制分类）。

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那是因为您正在获得与该类关联的数值。例如，如果您有两个类别的猫和狗，Keras会将它们关联为数字值0和1。要获取您的类及其关联数字值之间的映射，可以使用

>>> classes = train_generator.class_indices    
>>> print(classes)
    {'cats': 0, 'dogs': 1}

现在您知道了类和索引之间的映射。所以现在你可以做的是

if classes[0][0] == 1: prediction = 'dog' else: prediction = 'cat'

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