如何对自己的模型进行剪枝操作,代码实现
时间: 2024-05-06 19:22:07 浏览: 7
模型剪枝操作通常包括两个步骤:确定要剪枝的权重和进行剪枝。在确定要剪枝的权重时,常用的方法是根据权重的大小排序,选择权重最小的一定比例(例如10%)进行剪枝。在进行剪枝时,可以将被选择的权重置为0,或者直接将对应的神经元删除。
以下是一个简单的模型剪枝实现示例:
```python
import tensorflow as tf
import numpy as np
# 构建一个简单的全连接神经网络模型
inputs = tf.keras.Input(shape=(784,))
x = tf.keras.layers.Dense(64, activation='relu')(inputs)
x = tf.keras.layers.Dense(32, activation='relu')(x)
outputs = tf.keras.layers.Dense(10, activation='softmax')(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
# 训练模型
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train.reshape((60000, 784))
x_test = x_test.reshape((10000, 784))
x_train, x_test = x_train / 255.0, x_test / 255.0
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test))
# 确定要剪枝的权重
weights = model.get_weights()
weights_flatten = np.concatenate([w.flatten() for w in weights])
threshold = np.percentile(np.abs(weights_flatten), 90) # 选择绝对值最小的10%作为要剪枝的权重
# 进行剪枝
pruned_weights = []
for w in weights:
pruned_w = np.where(np.abs(w) < threshold, 0, w) # 将小于阈值的权重置为0
pruned_weights.append(pruned_w)
model.set_weights(pruned_weights)
# 测试剪枝后的模型
model.evaluate(x_test, y_test)
```