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歡迎來到 Keras 權重剪枝完整指南。
本頁記錄了各種使用情境，並說明如何針對每種情境使用 API。一旦您知道您需要的 API，請在 API 文件中找到參數和底層細節。
涵蓋以下使用情境
- 定義並訓練剪枝模型。
- Sequential 和 Functional。
- Keras model.fit 和自訂訓練迴圈
- 檢查點並還原序列化剪枝模型。
- 部署剪枝模型並查看壓縮優勢。
如需剪枝演算法的設定，請參閱 tfmot.sparsity.keras.prune_low_magnitude API 文件。
設定
為了找到您需要的 API 並理解目的，您可以執行但不閱讀本節。
! pip install -q tensorflow-model-optimization
import tensorflow as tf
import numpy as np
import tensorflow_model_optimization as tfmot
import tf_keras as keras
%load_ext tensorboard
import tempfile
input_shape = [20]
x_train = np.random.randn(1, 20).astype(np.float32)
y_train = keras.utils.to_categorical(np.random.randn(1), num_classes=20)
def setup_model():
model = keras.Sequential([
keras.layers.Dense(20, input_shape=input_shape),
keras.layers.Flatten()
])
return model
def setup_pretrained_weights():
model = setup_model()
model.compile(
loss=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
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)
setup_model()
pretrained_weights = setup_pretrained_weights()
2024-03-09 12:22:11.550860: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
定義模型
剪枝整個模型 (Sequential 和 Functional)
提升模型準確度的訣竅
- 嘗試「剪枝某些層」以跳過剪枝最降低準確度的層。
- 一般來說，使用剪枝進行微調比從頭開始訓練更好。
若要讓整個模型在訓練時進行剪枝，請將 tfmot.sparsity.keras.prune_low_magnitude 套用至模型。
base_model = setup_model()
base_model.load_weights(pretrained_weights) # optional but recommended.
model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(base_model)
model_for_pruning.summary()
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
prune_low_magnitude_dense_ (None, 20) 822
2 (PruneLowMagnitude)
prune_low_magnitude_flatte (None, 20) 1
n_2 (PruneLowMagnitude)
=================================================================
Total params: 823 (3.22 KB)
Trainable params: 420 (1.64 KB)
Non-trainable params: 403 (1.58 KB)
_________________________________________________________________
剪枝某些層 (Sequential 和 Functional)
剪枝模型可能會對準確度產生負面影響。您可以選擇性地剪枝模型的層，以探索準確度、速度和模型大小之間的權衡。
提升模型準確度的訣竅
- 一般來說，使用剪枝進行微調比從頭開始訓練更好。
- 嘗試剪枝後面的層而不是前面的層。
- 避免剪枝關鍵層（例如注意力機制）。
更多:
tfmot.sparsity.keras.prune_low_magnitudeAPI 文件提供了如何針對每層變更剪枝設定的詳細資訊。
在以下範例中，僅剪枝 Dense 層。
# Create a base model
base_model = setup_model()
base_model.load_weights(pretrained_weights) # optional but recommended for model accuracy
# Helper function uses `prune_low_magnitude` to make only the
# Dense layers train with pruning.
def apply_pruning_to_dense(layer):
if isinstance(layer, keras.layers.Dense):
return tfmot.sparsity.keras.prune_low_magnitude(layer)
return layer
# Use `keras.models.clone_model` to apply `apply_pruning_to_dense`
# to the layers of the model.
model_for_pruning = keras.models.clone_model(
base_model,
clone_function=apply_pruning_to_dense,
)
model_for_pruning.summary()
WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://tensorflow.dev.org.tw/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.
WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://tensorflow.dev.org.tw/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._iterations
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._iterations
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._learning_rate
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._learning_rate
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.1
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.1
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.2
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.2
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.3
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.3
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.4
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.4
Model: "sequential_3"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
prune_low_magnitude_dense_ (None, 20) 822
3 (PruneLowMagnitude)
flatten_3 (Flatten) (None, 20) 0
=================================================================
Total params: 822 (3.21 KB)
Trainable params: 420 (1.64 KB)
Non-trainable params: 402 (1.57 KB)
_________________________________________________________________
雖然此範例使用層的類型來決定要剪枝的內容，但剪枝特定層最簡單的方法是設定其 name 屬性，並在 clone_function 中尋找該名稱。
print(base_model.layers[0].name)
dense_3
更易讀但模型準確度可能較低
這與使用剪枝進行微調不相容，這就是為什麼它可能不如上述支援微調的範例準確。
雖然可以在定義初始模型時套用 prune_low_magnitude，但在以下範例中，之後載入權重不起作用。
Functional 範例
# Use `prune_low_magnitude` to make the `Dense` layer train with pruning.
i = keras.Input(shape=(20,))
x = tfmot.sparsity.keras.prune_low_magnitude(keras.layers.Dense(10))(i)
o = keras.layers.Flatten()(x)
model_for_pruning = keras.Model(inputs=i, outputs=o)
model_for_pruning.summary()
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 20)] 0
prune_low_magnitude_dense_ (None, 10) 412
4 (PruneLowMagnitude)
flatten_4 (Flatten) (None, 10) 0
=================================================================
Total params: 412 (1.61 KB)
Trainable params: 210 (840.00 Byte)
Non-trainable params: 202 (812.00 Byte)
_________________________________________________________________
Sequential 範例
# Use `prune_low_magnitude` to make the `Dense` layer train with pruning.
model_for_pruning = keras.Sequential([
tfmot.sparsity.keras.prune_low_magnitude(keras.layers.Dense(20, input_shape=input_shape)),
keras.layers.Flatten()
])
model_for_pruning.summary()
Model: "sequential_4"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
prune_low_magnitude_dense_ (None, 20) 822
5 (PruneLowMagnitude)
flatten_5 (Flatten) (None, 20) 0
=================================================================
Total params: 822 (3.21 KB)
Trainable params: 420 (1.64 KB)
Non-trainable params: 402 (1.57 KB)
_________________________________________________________________
剪枝自訂 Keras 層或修改層的部分以進行剪枝
常見錯誤：剪枝偏差通常會嚴重損害模型準確度。
tfmot.sparsity.keras.PrunableLayer 適用於兩種使用情境
- 剪枝自訂 Keras 層
- 修改內建 Keras 層的部分以進行剪枝。
例如，API 預設僅剪枝 Dense 層的核心。以下範例也剪枝偏差。
class MyDenseLayer(keras.layers.Dense, tfmot.sparsity.keras.PrunableLayer):
def get_prunable_weights(self):
# Prune bias also, though that usually harms model accuracy too much.
return [self.kernel, self.bias]
# Use `prune_low_magnitude` to make the `MyDenseLayer` layer train with pruning.
model_for_pruning = keras.Sequential([
tfmot.sparsity.keras.prune_low_magnitude(MyDenseLayer(20, input_shape=input_shape)),
keras.layers.Flatten()
])
model_for_pruning.summary()
Model: "sequential_5"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
prune_low_magnitude_my_den (None, 20) 843
se_layer (PruneLowMagnitud
e)
flatten_6 (Flatten) (None, 20) 0
=================================================================
Total params: 843 (3.30 KB)
Trainable params: 420 (1.64 KB)
Non-trainable params: 423 (1.66 KB)
_________________________________________________________________
訓練模型
Model.fit
在訓練期間呼叫 tfmot.sparsity.keras.UpdatePruningStep 回呼。
為了幫助偵錯訓練，請使用 tfmot.sparsity.keras.PruningSummaries 回呼。
# Define the model.
base_model = setup_model()
base_model.load_weights(pretrained_weights) # optional but recommended for model accuracy
model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(base_model)
log_dir = tempfile.mkdtemp()
callbacks = [
tfmot.sparsity.keras.UpdatePruningStep(),
# Log sparsity and other metrics in Tensorboard.
tfmot.sparsity.keras.PruningSummaries(log_dir=log_dir)
]
model_for_pruning.compile(
loss=keras.losses.categorical_crossentropy,
optimizer='adam',
metrics=['accuracy']
)
model_for_pruning.fit(
x_train,
y_train,
callbacks=callbacks,
epochs=2,
)
#docs_infra: no_execute
%tensorboard --logdir={log_dir}
對於非 Colab 使用者，您可以在 TensorBoard.dev 上查看先前執行此程式碼區塊的結果。
自訂訓練迴圈
在訓練期間呼叫 tfmot.sparsity.keras.UpdatePruningStep 回呼。
為了幫助偵錯訓練，請使用 tfmot.sparsity.keras.PruningSummaries 回呼。
# Define the model.
base_model = setup_model()
base_model.load_weights(pretrained_weights) # optional but recommended for model accuracy
model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(base_model)
# Boilerplate
loss = keras.losses.categorical_crossentropy
optimizer = keras.optimizers.Adam()
log_dir = tempfile.mkdtemp()
unused_arg = -1
epochs = 2
batches = 1 # example is hardcoded so that the number of batches cannot change.
# Non-boilerplate.
model_for_pruning.optimizer = optimizer
step_callback = tfmot.sparsity.keras.UpdatePruningStep()
step_callback.set_model(model_for_pruning)
log_callback = tfmot.sparsity.keras.PruningSummaries(log_dir=log_dir) # Log sparsity and other metrics in Tensorboard.
log_callback.set_model(model_for_pruning)
step_callback.on_train_begin() # run pruning callback
for _ in range(epochs):
log_callback.on_epoch_begin(epoch=unused_arg) # run pruning callback
for _ in range(batches):
step_callback.on_train_batch_begin(batch=unused_arg) # run pruning callback
with tf.GradientTape() as tape:
logits = model_for_pruning(x_train, training=True)
loss_value = loss(y_train, logits)
grads = tape.gradient(loss_value, model_for_pruning.trainable_variables)
optimizer.apply_gradients(zip(grads, model_for_pruning.trainable_variables))
step_callback.on_epoch_end(batch=unused_arg) # run pruning callback
#docs_infra: no_execute
%tensorboard --logdir={log_dir}
對於非 Colab 使用者，您可以在 TensorBoard.dev 上查看先前執行此程式碼區塊的結果。
提升剪枝模型準確度
首先，請查看 tfmot.sparsity.keras.prune_low_magnitude API 文件以瞭解剪枝排程是什麼以及每種類型剪枝排程的數學原理。
訣竅:
當模型進行剪枝時，學習率不要太高或太低。將剪枝排程視為超參數。
作為快速測試，嘗試在訓練開始時將模型剪枝到最終稀疏度，方法是使用
tfmot.sparsity.keras.ConstantSparsity排程將begin_step設定為 0。您可能會幸運地獲得良好的結果。不要太頻繁地剪枝，讓模型有時間恢復。剪枝排程提供了合理的預設頻率。
如需提升模型準確度的一般想法，請在「定義模型」下尋找適用於您的使用情境的訣竅。
檢查點和還原序列化
您必須在檢查點期間保留最佳化器步驟。這表示雖然您可以使用 Keras HDF5 模型進行檢查點，但您不能使用 Keras HDF5 權重。
# Define the model.
base_model = setup_model()
base_model.load_weights(pretrained_weights) # optional but recommended for model accuracy
model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(base_model)
_, keras_model_file = tempfile.mkstemp('.h5')
# Checkpoint: saving the optimizer is necessary (include_optimizer=True is the default).
model_for_pruning.save(keras_model_file, include_optimizer=True)
WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://tensorflow.dev.org.tw/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.
WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://tensorflow.dev.org.tw/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._iterations
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._iterations
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._learning_rate
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._learning_rate
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.1
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.1
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.2
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.2
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.3
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.3
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.4
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.4
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tf_keras/src/engine/training.py:3098: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native TF-Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
以上內容普遍適用。以下程式碼僅適用於 HDF5 模型格式（而非 HDF5 權重和其他格式）。
# Deserialize model.
with tfmot.sparsity.keras.prune_scope():
loaded_model = keras.models.load_model(keras_model_file)
loaded_model.summary()
WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.
WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.
Model: "sequential_6"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
prune_low_magnitude_dense_ (None, 20) 822
6 (PruneLowMagnitude)
prune_low_magnitude_flatte (None, 20) 1
n_7 (PruneLowMagnitude)
=================================================================
Total params: 823 (3.22 KB)
Trainable params: 420 (1.64 KB)
Non-trainable params: 403 (1.58 KB)
_________________________________________________________________
部署剪枝模型
匯出具有大小壓縮的模型
常見錯誤：strip_pruning 和套用標準壓縮演算法（例如透過 gzip）都是看到剪枝壓縮優勢所必需的。
# Define the model.
base_model = setup_model()
base_model.load_weights(pretrained_weights) # optional but recommended for model accuracy
model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(base_model)
# Typically you train the model here.
model_for_export = tfmot.sparsity.keras.strip_pruning(model_for_pruning)
print("final model")
model_for_export.summary()
print("\n")
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)))
WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://tensorflow.dev.org.tw/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.
WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://tensorflow.dev.org.tw/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._iterations
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._iterations
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._learning_rate
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._learning_rate
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.1
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.1
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.2
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.2
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.3
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.3
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.4
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.4
final model
Model: "sequential_7"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_7 (Dense) (None, 20) 420
flatten_8 (Flatten) (None, 20) 0
=================================================================
Total params: 420 (1.64 KB)
Trainable params: 420 (1.64 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
Size of gzipped pruned model without stripping: 3455.00 bytes
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
Size of gzipped pruned model with stripping: 2939.00 bytes
硬體特定的最佳化
一旦不同的後端 啟用剪枝以改善延遲，使用區塊稀疏性可以改善某些硬體的延遲。
增加區塊大小會降低目標模型準確度可實現的峰值稀疏性。儘管如此，延遲仍然可以改善。
如需區塊稀疏性支援哪些功能的詳細資訊，請參閱 tfmot.sparsity.keras.prune_low_magnitude API 文件。
base_model = setup_model()
# For using intrinsics on a CPU with 128-bit registers, together with 8-bit
# quantized weights, a 1x16 block size is nice because the block perfectly
# fits into the register.
pruning_params = {'block_size': [1, 16]}
model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(base_model, **pruning_params)
model_for_pruning.summary()
WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://tensorflow.dev.org.tw/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.
WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://tensorflow.dev.org.tw/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._iterations
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._iterations
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._learning_rate
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._learning_rate
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.1
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.1
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.2
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.2
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.3
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.3
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.4
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.4
Model: "sequential_8"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
prune_low_magnitude_dense_ (None, 20) 822
8 (PruneLowMagnitude)
prune_low_magnitude_flatte (None, 20) 1
n_9 (PruneLowMagnitude)
=================================================================
Total params: 823 (3.22 KB)
Trainable params: 420 (1.64 KB)
Non-trainable params: 403 (1.58 KB)
_________________________________________________________________