在TensorFlow中,是否有任何函数或方法可以帮助我找出网络中的学习参数数量?
我不知道有任何函数可以实现此功能,但您仍然可以使用 tf.trainable_variables()
上的 for 循环来计算自己:
total_parameters = 0
for variable in tf.trainable_variables():
variable_parameters = 1
for dim in variable.get_shape():
variable_parameters *= dim.value
total_parameters += variable_parameters
print("Total number of trainable parameters: %d" % total_parameters)
np.sum([np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()])
def show_params():
total = 0
for v in tf.trainable_variables():
dims = v.get_shape().as_list()
num = int(np.prod(dims))
total += num
print(' %s \t\t Num: %d \t\t Shape %s ' % (v.name, num, dims))
print('\nTotal number of params: %d' % total)
params/weights/W1:0 Num: 34992 Shape [3, 3, 18, 216]
params/weights/W2:0 Num: 839808 Shape [3, 3, 216, 432]
params/weights/W3:0 Num: 839808 Shape [3, 3, 432, 216]
params/weights/W4:0 Num: 57856 Shape [226, 256]
params/weights/W5:0 Num: 32768 Shape [256, 128]
params/weights/W6:0 Num: 8192 Shape [128, 64]
params/weights/W7:0 Num: 64 Shape [64, 1]
params/biases/b1:0 Num: 216 Shape [216]
params/biases/b2:0 Num: 432 Shape [432]
params/biases/b3:0 Num: 216 Shape [216]
params/biases/b4:0 Num: 256 Shape [256]
params/biases/b5:0 Num: 128 Shape [128]
params/biases/b6:0 Num: 64 Shape [64]
params/biases/b7:0 Num: 1 Shape [1]
Total number of params: 1814801