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<p>我想在googlecolab中训练一个分类器,使用Keras对图像代表的是狗还是猫进行分类。培训样本8000个,测试样本2000个。1个历元所用的时间是12小时。我是谷歌colab的新手,不知道怎么解决这个问题。我使用GPU作为硬件加速,我认为拥有1xtelak80将不到5分钟,但它花费了太多的时间。在</p>
<p>我尝试过将运行时改为GPU和TPU,但两个运行时都不工作。在</p>
<p>我的代码是:</p>
<pre><code>classifier = Sequential()
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 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(Flatten())
classifier.add(Dense(units = 128, activation = 'relu'))
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('/content/gdrive/My
Drive/Colab Notebooks/<a href="https://www.cnpython.com/pypi/dataset" class="inner-link">dataset</a>/training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('/content/gdrive/My
Drive/Colab Notebooks/dataset/test_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
classifier.fit_generator(training_set,
steps_per_epoch = 8000,
epochs = 1,
validation_data = test_set,
validation_steps = 2000)
</code></pre>
<p>执行此代码时有许多不推荐使用的代码。执行后分类器.fit_发生器(),显示1个历元剩余12小时</p>