我从帖子中获得的代码中学会了实现图像分类器，它非常有帮助，但我不知道如何预测图像。我尝试过，但它给出了一个值错误。我仍然是初学者
凯拉斯代码：-
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)
请告诉我在 Keras 中进行预测的正确方法，以便我可以在不同的测试用例中实现它。