Tensorflow 如果使用keras在较小尺寸的图像上训练模型，我如何预测较大尺寸的图像

我在tensorflow后端使用下面的keras代码来分类狗和猫之间的区别。它没有预测任何图像高于800x800图像。如何预测或调整图像大小以预测hd图像

训练代码：

# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.preprocessing.image import load_img, img_to_array
from keras.models import model_from_json
from scipy.misc import imresize


# Initialising the CNN
classifier = Sequential()

# Step 1 - Convolution
classifier.add(Convolution2D(32, 3, 3, input_shape = (64, 64, 3), activation = 'relu'))

# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Adding a second convolutional layer
classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Step 3 - Flattening
classifier.add(Flatten())

# Step 4 - Full connection
classifier.add(Dense(output_dim = 128, activation = 'relu'))
classifier.add(Dense(output_dim = 1, activation = 'sigmoid'))

# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

# Part 2 - Fitting the CNN to the images

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('dataset/training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')

test_set = test_datagen.flow_from_directory('dataset/test_set',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'binary')

classifier.fit_generator(
    training_set,
    samples_per_epoch=80,
    nb_epoch=100,
    validation_data=test_set,
    nb_val_samples=2000
)


print(training_set.class_indices)

from keras.models import model_from_json

json_file = open('model.json', 'r')
model_json = json_file.read()
json_file.close()
model = model_from_json(model_json)
# load weights into new model
model.load_weights("model.h5")

# evaluate loaded model on test data
model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])


import shutil
import matplotlib.pyplot as plt
import requests

url = raw_input("Please enter the image url/link")
response = requests.get(url, stream=True)
with open('test.jpg', 'wb') as out_file:
    shutil.copyfileobj(response.raw, out_file)


from keras.preprocessing import image
import numpy as np

test = image.load_img('test.jpg')
test = image.img_to_array(test)
test = np.expand_dims(test, axis=0)
result = model.predict(test)

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

要预测的代码：

# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.preprocessing.image import load_img, img_to_array
from keras.models import model_from_json
from scipy.misc import imresize


# Initialising the CNN
classifier = Sequential()

# Step 1 - Convolution
classifier.add(Convolution2D(32, 3, 3, input_shape = (64, 64, 3), activation = 'relu'))

# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Adding a second convolutional layer
classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Step 3 - Flattening
classifier.add(Flatten())

# Step 4 - Full connection
classifier.add(Dense(output_dim = 128, activation = 'relu'))
classifier.add(Dense(output_dim = 1, activation = 'sigmoid'))

# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

# Part 2 - Fitting the CNN to the images

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('dataset/training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')

test_set = test_datagen.flow_from_directory('dataset/test_set',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'binary')

classifier.fit_generator(
    training_set,
    samples_per_epoch=80,
    nb_epoch=100,
    validation_data=test_set,
    nb_val_samples=2000
)


print(training_set.class_indices)

from keras.models import model_from_json

json_file = open('model.json', 'r')
model_json = json_file.read()
json_file.close()
model = model_from_json(model_json)
# load weights into new model
model.load_weights("model.h5")

# evaluate loaded model on test data
model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])


import shutil
import matplotlib.pyplot as plt
import requests

url = raw_input("Please enter the image url/link")
response = requests.get(url, stream=True)
with open('test.jpg', 'wb') as out_file:
    shutil.copyfileobj(response.raw, out_file)


from keras.preprocessing import image
import numpy as np

test = image.load_img('test.jpg')
test = image.img_to_array(test)
test = np.expand_dims(test, axis=0)
result = model.predict(test)

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

---

根据Keras文档，您可以使用以下方法指定目标尺寸：

test = image.load_img('test.jpg', target_size=(224, 224))

---

例如，请参阅。

如果您使用的是TensorFlow后端，最好的选择可能是编写一个调用。否则，您可以在将数据输入到模型之前调整数据大小，例如使用SciPy。我不介意再次将输入设置为“无”来训练模型，但有没有方法不用重新训练就进行预测？没错，看看（因为函数似乎不在文档中的任何地方）它确实执行了调整大小，而且，更重要的是，该函数返回一个，因此原则上，在调用

img\u to\u array

之前，您可以对其执行PIL支持的任何操作。我得到以下错误：ValueError：检查时出错：预期conv2d\u 7\u输入具有形状（无，64，64，3），但获得具有形状（1，224，224，3）的数组只需将目标大小更改为64，64@convolutionBoy对它一直是64,64，但无法预测更大的图像抱歉，我的意思是你需要将更大的图像缩小到64,64。这是唯一的解决办法。