如何解决Tensorflow 剪枝模型与原始基线模型大小相同
我有一个要修剪的基线 TF 功能模型。我已尝试按照文档中的代码进行操作,但压缩剪枝模型的大小与压缩基线模型的大小相同。
我认为我的代码没有任何问题,为什么会发生这种情况?
def get_gzipped_model_size(model):
# Returns size of gzipped model,in bytes.
import os
import zipfile
_,keras_file = tempfile.mkstemp('.h5')
model.save(keras_file,include_optimizer=False)
_,zipped_file = tempfile.mkstemp('.zip')
with zipfile.ZipFile(zipped_file,'w',compression=zipfile.ZIP_DEFLATED) as f:
f.write(keras_file)
return os.path.getsize(zipped_file)
def test():
model = keras.models.load_model('models/cifar10/baselines/convnet_small')
model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(model)
model_for_export = tfmot.sparsity.keras.strip_pruning(model_for_pruning)
print("Size of gzipped baseline model: %.2f bytes" % (get_gzipped_model_size(model)))
print("Size of gzipped pruned model without stripping: %.2f bytes" % (get_gzipped_model_size(model_for_pruning)))
print("Size of gzipped pruned model with stripping: %.2f bytes" % (get_gzipped_model_size(model_for_export)))
if __name__ == "__main__":
test()
输出:
Size of gzipped baseline model: 604286.00 bytes
Size of gzipped pruned model without stripping: 610750.00 bytes
Size of gzipped pruned model with stripping: 604287.00 bytes
编辑:
我也用与文档中相同的模型进行了尝试,修剪后的模型仍然与基线大小相同:
input_shape = [20]
x_train = np.random.randn(1,20).astype(np.float32)
y_train = tf.keras.utils.to_categorical(np.random.randn(1),num_classes=20)
def setup_model():
model = tf.keras.Sequential([
tf.keras.layers.Dense(20,input_shape=input_shape),tf.keras.layers.Flatten()
])
return model
def setup_pretrained_weights():
model = setup_model()
model.compile(
loss=tf.keras.losses.categorical_crossentropy,optimizer='adam',metrics=['accuracy']
)
model.fit(x_train,y_train)
_,pretrained_weights = tempfile.mkstemp('.tf')
model.save_weights(pretrained_weights)
return pretrained_weights
setup_model()
pretrained_weights = setup_pretrained_weights()
输出:
Size of gzipped baseline model: 2910.00 bytes
Size of gzipped pruned model without stripping: 3333.00 bytes
Size of gzipped pruned model with stripping: 2910.00 bytes
解决方法
在我看来,您似乎错过了实际进行修剪的步骤。如果我们看一下 test()
函数,您将模型设置为修剪,但实际上从未修剪过。看看下面的编辑。
import tensorflow_model_optimization as tfmot
def test():
model = keras.models.load_model('models/cifar10/baselines/convnet_small')
pruning_schedule = tfmot.sparsity.keras.ConstantSparsity(
target_sparsity=0.95,begin_step=0,end_step=-1,frequency=100
)
callbacks = [tfmot.sparsity.keras.UpdatePruningStep()]
model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(base_model,pruning_schedule=pruning_schedule)
model_for_pruning.compile(optimizer="adam",loss="some-loss")
model_for_pruning.fit(X,y,epochs=2,callbacks=callbacks)
model_for_export = tfmot.sparsity.keras.strip_pruning(model_for_pruning)
print("Size of gzipped baseline model: %.2f bytes" %(get_gzipped_model_size(model)))
print("Size of gzipped pruned model without stripping: %.2f bytes" % (get_gzipped_model_size(model_for_pruning)))
print("Size of gzipped pruned model with stripping: %.2f bytes" % (get_gzipped_model_size(model_for_export)))
你可以看看我刚才问的问题中的代码。我遇到了一个稍微不同的问题,但发布在那里的代码有效(至少在某些情况下)。
如果您有兴趣,您还可以查看 tensorflow.sparsity.keras
API 以查看其他一些选项
https://www.tensorflow.org/model_optimization/api_docs/python/tfmot/sparsity/keras
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