Question

我是ML的新手，我正在尝试玩一些本教程： https://medium.com/@ferhat00/deep-learning-with-keras-classifying-cats-and-dogs-part-2-21b3b25bbe5c。无论如何，CNN似乎仅在训练后我没有将其保存在h5文件中的情况下才起作用。更精确地：

from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import Dropout
from keras import optimizers

classifier = Sequential()

classifier.add(Conv2D(32, (3, 3), input_shape = (128, 128, 3), activation = 'relu'))


classifier.add(MaxPooling2D(pool_size = (2, 2)))


classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Conv2D(64, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))


classifier.add(Flatten())


classifier.add(Dense(units = 64, activation = 'relu'))
classifier.add(Dropout(0.5))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])


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('PetImages/training_set',
                                             target_size = (128, 128),
                                             batch_size = 32,
                                             class_mode = 'binary')

test_set = test_datagen.flow_from_directory('PetImages/test_set',
                                        target_size = (128, 128),
                                        batch_size = 32,
                                        class_mode = 'binary')

history = classifier.fit_generator(training_set,
                     steps_per_epoch = 8000/32,
                     epochs = 30,
                     validation_data = test_set,
                     validation_steps = 2000/32, workers=12, max_q_size=100)

///////////////////////////////////////
classifier.save("cnnPetsModel.h5")
print("saved model to disk")
///////////////////////////////////////

from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
import numpy as np
img_path = 'PetImages/test_set/Dog/5000.jpg'
img = image.load_img(img_path, target_size=(128, 128))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)

x = preprocess_input(x)
preds = classifier.predict(x)

training_set.class_indices

print(preds)
if preds[0][0] == 1:
    prediction = 'dog'
else:
    prediction = 'cat'

print(prediction)


img_path = 'PetImages/test_set/Cat/5000.jpg'
img = image.load_img(img_path, target_size=(128, 128))
x = image.img_to_array(img)

x = np.expand_dims(x, axis=0)

x = preprocess_input(x)
preds = classifier.predict(x)

training_set.class_indices

print(preds)
if preds[0][0] == 1:
    prediction = 'dog'
else:
    prediction = 'cat'

print(prediction)

如果删除“ ////////”之间的代码，则可以正确预测这两个图像，但是在保存模型的情况下，它们始终变为“ cat”。为什么会这样？我只在文件中保存参数，结构等。预先感谢！

将CNN模型保存到h5文件中时的奇怪行为

0 个答案: