Question

我写了下面的代码来识别图像。

# Importing the Keras libraries and packages
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
# Initialising the CNN
classifier = Sequential()
# Step 1 - Convolution
classifier.add(Conv2D(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(Conv2D(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(units = 128, activation = 'relu'))
classifier.add(Dense(units = 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('E:\\ML_R&D\\training_set\\cats1',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('E:\\ML_R&D\\test_set\\cats1',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
classifier.fit_generator(training_set,
steps_per_epoch = 8000,
epochs = 25,
validation_data = test_set,
validation_steps = 2000)
# Part 3 - Making new predictions
import numpy as np
from keras.preprocessing import image
test_image = image.load_img('E:\\ML_R&D\\cat.jpg', target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
training_set.class_indices
if result[0][0] == 1:
    prediction = 'dog'
else:
    prediction = 'cat'

当我运行此代码时，它已成功运行，没有任何错误，但是在等待小时（2）之后没有显示任何结果。它只显示在下面。

找到0个属于0类的图像。找到属于0类的0个图像。时代1/25

Answer 1

问题在于您提供的目录结构。

training_set = train_datagen.flow_from_directory('E:\\ML_R&D\\training_set\\cats1',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')

此处，路径E:\\ML_R&D\\training_set\\cats1必须包含子文件夹（代表每个类），其中每个子文件夹中的图像都属于该特定类。

例如

/home/tlokeshkumar/Documents/image_data是我的数据集所在的位置。

image_data
    class_1
        class_1_1.jpg
        class_1_2.jpg
        ...
    class_2
        class_2_1.jpg
        class_2_2.jpg
        class_2_3.jpg
        ...
    class_3
    class_4
    ...

如果遵循此结构，则必须输入主文件夹（image_data）的路径。

training_set = train_datagen.flow_from_directory('home/tlokeshkumar/Documents/image_data',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')

对于Fast image classification，您可以查看我的存储库，在该存储库中，我使用keras编写了图像分类器，该图像分类器使用瓶颈比常规训练过程快得多地训练它。

图像分类算法可以花费多少时间？

1 个答案: