有一种方法可以手动更改 tf.layers.Conv2d ( https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/layers/Conv2D ) 的权重吗?因为此类仅接受输入、要使用的内核数量等...并且权重由 Tensorflow 自动存储和计算,但我想办法(如 tf.nn.conv2d - https://www.tensorflow.org/api_docs/python/tf/nn/conv2d )来直接将权重传递给类。
有人有什么建议吗?
也许可以手动加载和更改该层关联变量的值?我发现这个解决方案非常糟糕,但它可以工作。
谢谢。
最佳答案
假设您有一个像这样的基本卷积神经网络:
import tensorflow as tf
import numpy as np
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(filters=16, kernel_size=(3, 3),
strides=(1, 1), activation='relu'),
tf.keras.layers.MaxPool2D(pool_size=(2, 2)),
tf.keras.layers.Conv2D(filters=32, kernel_size=(3, 3),
strides=(1, 1), activation='relu'),
tf.keras.layers.MaxPool2D(pool_size=(2, 2)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dropout(5e-1),
tf.keras.layers.Dense(10, activation='softmax')
])
默认情况下,所有卷积层的名称都是'conv2d...'
。
list(map(lambda x: x.name, model.layers))
['conv2d_19',
'max_pooling2d_19',
'conv2d_20',
'max_pooling2d_20',
'flatten_8',
'dense_16',
'dropout_8',
'dense_17']
使用它,您可以遍历所有卷积层。
for layer in filter(lambda x: 'conv2d' in x.name, model.layers):
print(layer)
<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x00000295BE4EB048>
<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x00000295C1617448>
对于所有这些层,您可以获得权重形状和偏置形状。
for layer in filter(lambda x: 'conv' in x.name, model.layers):
weights_shape, bias_shape = map(lambda x: x.shape, layer.get_weights())
然后您可以将 layer.set_weights()
与您想要的值一起使用,因为您知道正确的形状。比方说 0.12345
。让我们使用 np.full
来做到这一点,它用您想要的任何值填充指定形状的数组。
for layer in filter(lambda x: 'conv2d' in x.name, model.layers):
weights_shape, bias_shape = map(lambda x: x.shape, layer.get_weights())
layer.set_weights([np.full(weights_shape, 0.12345),
np.full(bias_shape, 0.12345)])
现在的权重:
[array([[[[0.12345, 0.12345, 0.12345, ..., 0.12345, 0.12345, 0.12345],
[0.12345, 0.12345, 0.12345, ..., 0.12345, 0.12345, 0.12345],
[0.12345, 0.12345, 0.12345, ..., 0.12345, 0.12345, 0.12345],
...,
[0.12345, 0.12345, 0.12345, ..., 0.12345, 0.12345, 0.12345],
[0.12345, 0.12345, 0.12345, ..., 0.12345, 0.12345, 0.12345],
[0.12345, 0.12345, 0.12345, ..., 0.12345, 0.12345, 0.12345]]]],
dtype=float32),
array([0.12345, 0.12345, 0.12345, 0.12345, 0.12345, 0.12345, 0.12345,
0.12345, 0.12345, 0.12345, 0.12345, 0.12345, 0.12345, 0.12345,
0.12345, 0.12345, 0.12345, 0.12345, 0.12345, 0.12345, 0.12345,
0.12345, 0.12345, 0.12345, 0.12345, 0.12345, 0.12345, 0.12345,
0.12345, 0.12345, 0.12345, 0.12345], dtype=float32)]
完全复制/粘贴示例:
import tensorflow as tf
import numpy as np
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(filters=16, kernel_size=(3, 3),
strides=(1, 1), activation='relu'),
tf.keras.layers.MaxPool2D(pool_size=(2, 2)),
tf.keras.layers.Conv2D(filters=32, kernel_size=(3, 3),
strides=(1, 1), activation='relu'),
tf.keras.layers.MaxPool2D(pool_size=(2, 2)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dropout(5e-1),
tf.keras.layers.Dense(10, activation='softmax')
])
model.build(input_shape=(None, 28, 28, 1))
for layer in filter(lambda x: 'conv2d' in x.name, model.layers):
weights_shape, bias_shape = map(lambda x: x.shape, layer.get_weights())
layer.set_weights([np.full(weights_shape, 0.12345),
np.full(bias_shape, 0.12345)])
关于python - 手动更改 Keras 卷积层的权重,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/64612657/