Python 基于Keras的预测

我从一篇博文中获得的代码中学习了如何实现一个图像分类器，这非常有用，但我不知道如何对图像进行预测。我尝试过，但给出了一个值错误。我还是一个初学者

Keras代码：-

from __future__ import print_function
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import SGD, Adam
from keras.utils import np_utils
import numpy as np

#seed = 7
#np.random.seed(seed)

batch_size = 50
nb_classes = 10
nb_epoch = 150
data_augmentation = False

# input image dimensions
img_rows, img_cols = 32, 32
# the CIFAR10 images are RGB
img_channels = 3

# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

model = Sequential()

model.add(Convolution2D(32, 3, 3, border_mode='same',
                        input_shape=X_train.shape[1:]))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))

# let's train the model using SGD + momentum (how original).

#sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
sgd= Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='categorical_crossentropy',
              optimizer=sgd,
              metrics=['accuracy'])

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255

if not data_augmentation:
    print('Not using data augmentation.')
    model.fit(X_train, Y_train,
              batch_size=batch_size,
              nb_epoch=nb_epoch,
              validation_data=(X_test, Y_test),
              shuffle=True)

else:
    print('Using real-time data augmentation.')

    # this will do preprocessing and realtime data augmentation
    datagen = ImageDataGenerator(
        featurewise_center=False,  # set input mean to 0 over the dataset
        samplewise_center=False,  # set each sample mean to 0
        featurewise_std_normalization=False,  # divide inputs by std of the dataset
        samplewise_std_normalization=False,  # divide each input by its std
        zca_whitening=False,  # apply ZCA whitening
        rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
        horizontal_flip=True,  # randomly flip images
        vertical_flip=False)  # randomly flip images

    # compute quantities required for featurewise normalization
    # (std, mean, and principal components if ZCA whitening is applied)
    datagen.fit(X_train)

    # fit the model on the batches generated by datagen.flow()
    model.fit_generator(datagen.flow(X_train, Y_train,
                        batch_size=batch_size),
                        samples_per_epoch=X_train.shape[0],
                        nb_epoch=nb_epoch,
validation_data=(X_test, Y_test))

model.save('CIFAR10.h5')

我的预测代码：-

from __future__ import print_function
from keras.models import load_model
from keras.utils import np_utils
import numpy as np
import cv2

img_rows, img_cols = 32, 32
model = load_model('CIFAR10.h5')
img = cv2.imread('D:/Study_Material/Python_3_Tutorial/PythonScripts/Machine_Learning/Project/Images/Deer.jpg')
img = cv2.resize(img,(img_rows,img_cols))
Image = np.array(img)
print(model.predict(Image))

错误：-

 Warning (from warnings module):
  File "C:\Users\Na462\AppData\Local\Programs\Python\Python35\lib\site-packages\h5py\__init__.py", line 36
    from ._conv import register_converters as _register_converters
FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
Using TensorFlow backend.
Traceback (most recent call last):
  File "D:\Study_Material\Python_3_Tutorial\PythonScripts\Machine_Learning\Project\Keras(Prediction).py", line 12, in <module>
    print(model.predict(Image))
  File "C:\Users\Na462\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\models.py", line 1025, in predict
    steps=steps)
  File "C:\Users\Na462\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\engine\training.py", line 1817, in predict
    check_batch_axis=False)
  File "C:\Users\Na462\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\engine\training.py", line 113, in _standardize_input_data
    'with shape ' + str(data_shape))
ValueError: Error when checking : expected conv2d_1_input to have 4 dimensions, but got array with shape (32, 32, 3)

警告（来自警告模块）：
文件“C:\Users\Na462\AppData\Local\Programs\Python\35\lib\site packages\h5py\\uuuuu init\uuuuu.py”，第36行
from.\u conv导入寄存器\u转换器作为\u寄存器\u转换器
FutureWarning:不推荐将issubdtype的第二个参数从'float'转换为'np.floating'。将来，它将被视为`np.float64==np.dtype（float.type`。
使用TensorFlow后端。
回溯（最近一次呼叫最后一次）：
文件“D:\Study\u Material\Python\u 3\u Tutorial\PythonScripts\Machine\u Learning\Project\Keras（Prediction）.py”，第12行，在
打印（模型预测（图像））
文件“C:\Users\Na462\AppData\Local\Programs\Python\35\lib\site packages\keras\models.py”，第1025行，在predict中
步骤=步骤）
文件“C:\Users\Na462\AppData\Local\Programs\Python\35\lib\site packages\keras\engine\training.py”，第1817行，在predict中
检查（批次轴=假）
文件“C:\Users\Na462\AppData\Local\Programs\Python\35\lib\site packages\keras\engine\training.py”，第113行，在输入数据中
“带形状”+str（数据形状））
ValueError:检查时出错：预期conv2d_1_输入有4个维度，但得到了具有形状（32,32,3）的数组

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请告诉我在Keras中预测的正确方法，以便我可以在不同的测试用例上实现它。

您得到的错误是因为所有框架都假设一个图像输入是一批图像，使其成为4d张量，而不是一个图像（3d张量）。要只进行单个图像批处理，请将输入扩展为大小（1、32、32、3），开始时的1是批处理大小1

我不太了解keras，但由于您传递的是一个numpy数组，因此可以这样修改“Image”对象（请参见倒数第二行）：

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这不是正确的预测方式吗？因为在很多文章中，我学到了用适当的尺寸转换numpy数组中的图像并将其传递给模型。

img_rows, img_cols = 32, 32
model = load_model('CIFAR10.h5')
img = cv2.imread('D:/Study_Material/Python_3_Tutorial/PythonScripts/Machine_Learning/Project/Images/Deer.jpg')
img = cv2.resize(img,(img_rows,img_cols))
Image = np.expand_dims(np.array(img), axis=0)
print(model.predict(Image))