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<p>我创建了一个简单的CNN来区分5种不同种类的花。我想扩展CNN来识别更多的物体。例如,我想让CNN识别一杯啤酒、窗户、树等的图像。
下面是我做的花分类代码,效果不错。但是如何扩展它,让它识别越来越多的物体。我不想使用任何预先训练过的模型。我想让它学会分类更多的物体。请帮忙。你知道吗</p>
<pre><code>from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D, Flatten, Dense
classifier=Sequential()
#1st Convolution Layer
classifier.add(Convolution2D(32, 3, 3, input_shape=(64,64,3),activation="relu"))
#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)))
# Flattening
classifier.add(Flatten())
classifier.add(Dense(output_dim = 128, activation = 'relu'))
classifier.add(Dense(output_dim = 64, activation = 'relu'))
classifier.add(Dense(output_dim = 5, activation = 'softmax'))
classifier.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])
print(classifier.summary())
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('flowers/train_set',
target_size=(64,64),
batch_size=32,
class_mode='categorical')
test_set= test_datagen.flow_from_directory('flowers/test_set',
target_size=(64,64),
batch_size=32,
class_mode='categorical')
classifier.fit_generator(training_set,
samples_per_epoch = 3000,
nb_epoch = 25,
validation_data = test_set,
nb_val_samples=1000)
</code></pre>