如何在Keras中使用训练有素的模型预测输入图像？

Question

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类图像进行分类，并使用进行保存model.save()。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的准确度进行了训练，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，为什么[[0]]？

提前致谢。

Answer 1

keras
Forecast_classes（docs）输出类别预测的numpy数组。在您的模型情况下，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，结果将类似于[[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对于“癌症”，1对于“非癌症”）进行二进制分类。然后，您将解释您的序列输出[[0]]为具有类标签'cancer'