Keras：使用训练模型进行预测

Question

我是keras的一个完全初学者，我在keras中实现了以下代码，我在网上找到了此代码，并成功地以97％的精度对其进行了培训。 在预测过程中我一点点问题。

以下代码进行培训：

from __future__ import print_function
import keras
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('model3.h5')

该模型已成功保存，我按照以下预测代码实施了该模型。

预测代码：

import keras
import tensorflow as tf
import h5py
from keras.models import load_model
import cv2
import numpy as np

model = load_model('model3.h5')
print('Model Loaded')
dim = (32,32)
img = cv2.imread('download.jpg')
img = cv2.resize(img,dim)
Array = [np.array(img)]


Prediction = model.predict(Array)
print(Prediction)

产生错误：

Using TensorFlow backend.
Model Loaded
Traceback (most recent call last):
  File "E:\Prediction\Prediction.py", line 16, in <module>
    Prediction = model.predict(Array)
  File "C:\Users\Dilip\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\training.py", line 1149, in predict
    x, _, _ = self._standardize_user_data(x)
  File "C:\Users\Dilip\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\training.py", line 751, in _standardize_user_data
    exception_prefix='input')
  File "C:\Users\Dilip\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\training_utils.py", line 128, in standardize_input_data
    'with shape ' + str(data_shape))
ValueError: Error when checking input: expected conv2d_1_input to have 4 dimensions, but got array with shape (32, 32, 3)
>>> 

我在这里知道，由于输入图像的形状不正确而产生了一些问题，我试图将其转换为（1,32,32,3），但是我失败了！

请在这里帮助。

Answer 1

看来您缺少代码中用于预测的类。 尝试以下方法：

import cv2
import tensorflow as tf

#write the 10 classes here nb_classes
CATEGORIES = ['1','2','3','4','5','6','7','8','9','10']

def prepare(filepath):
    IMG_SIZE = 32
    img_array = cv2.imread(filepath, cv2.IMREAD_COLOR)
    new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
    return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 3) #img_channels = 3

model = tf.keras.models.load_model('model3.h5')

prediction = model.predict([prepare('download.jpg')])

print(CATEGORIES[int(prediction[0][0])])