Keras：模型

Question

我在所有图层中设置trainable=False，通过Model API实现，但我想验证这是否有效。 model.count_params()会返回参数总数，但除了查看model.summary()的最后几行之外，还有哪些方法可以获取可训练参数的总数？

Answer 1

from keras import backend as K

trainable_count = int(
    np.sum([K.count_params(p) for p in set(model.trainable_weights)]))
non_trainable_count = int(
    np.sum([K.count_params(p) for p in set(model.non_trainable_weights)]))

print('Total params: {:,}'.format(trainable_count + non_trainable_count))
print('Trainable params: {:,}'.format(trainable_count))
print('Non-trainable params: {:,}'.format(non_trainable_count))

上述代码段可以在layer_utils.print_summary()定义的末尾发现，summary()正在调用。

Answer 2

计数可训练参数的另一种方法是：

model.count_params()

Answer 3

对于tensorflow.keras这对我有用。来自tensorflow github代码中layer_utils.py中的函数print_layer_summary_with_connections（）

import numpy as np
from tensorflow.python.util import object_identity

def count_params(weights):
    return int(sum(np.prod(p.shape.as_list())
      for p in object_identity.ObjectIdentitySet(weights)))

if hasattr(model, '_collected_trainable_weights'):
    trainable_count = count_params(model._collected_trainable_weights)
else:
    trainable_count = count_params(model.trainable_weights)

print (trainable_count)

Answer 4

对于 TensorFlow 2.0 ：

import tensorflow.keras.backend as K

trainable_count = np.sum([K.count_params(w) for w in model.trainable_weights])
non_trainable_count = np.sum([K.count_params(w) for w in model.non_trainable_weights])

print('Total params: {:,}'.format(trainable_count + non_trainable_count))
print('Trainable params: {:,}'.format(trainable_count))
print('Non-trainable params: {:,}'.format(non_trainable_count))