如何计算tensorflow计算model的大小size（2）

1,加adam优化器，保存模型

import os
import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.python import pywrap_tensorflow
 
 
 
CURRENT_DIR = os.getcwd()
train_dir  = CURRENT_DIR + '/logs/'

lr =0.01
 
a = tf.placeholder(tf.float32, shape=[None, 6, 512], name='a')
print a.shape
b     = slim.fully_connected(a, 1 * 512, activation_fn=tf.nn.relu, weights_initializer=slim.variance_scaling_initializer(), scope='fc1')
print b.shape
  
y = tf.placeholder(tf.float32, [None, 10])
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=b, labels=y))
    
optimizer = tf.train.AdamOptimizer(lr).minimize(cost)
# optimizer = tf.train.GradientDescentOptimizer(lr).minimize(cost)
 
for tv in tf.trainable_variables():
    print (tv.name)
  
w = tf.get_default_graph().get_tensor_by_name("fc1/weights:0")   
b = tf.get_default_graph().get_tensor_by_name("fc1/biases:0") 

  
      
with tf.Session() as sess:
    tf.global_variables_initializer().run()
    print(sess.run(b))
    print(sess.run(w))
    print(sess.run(b).shape)
    print(sess.run(w).shape)    
    print(type(sess.run(b)))
    print(type(sess.run(w)[0,0]))   
    checkpoint_path = os.path.join(train_dir,'abc.ckpt')
    saver = tf.train.Saver()
    saver.save(sess, checkpoint_path)
    print '*********    model saved    *********'

模型比上一个增加了大概3倍

2，原因是加了adam优化器增大了3倍

通过读model来测试

model_dir = train_dir
 
ckpt = tf.train.get_checkpoint_state(model_dir)
ckpt_path = ckpt.model_checkpoint_path
 
reader = pywrap_tensorflow.NewCheckpointReader(ckpt_path)
param_dict = reader.get_variable_to_shape_map()
 
for key, val in param_dict.items():
    try:
        print key, val
    except:
        pass

输出：
fc1/weights/Adam [512, 512]
fc1/biases/Adam [512]
fc1/weights [512, 512]
beta2_power []
fc1/weights/Adam_1 [512, 512]
fc1/biases/Adam_1 [512]
beta1_power []
fc1/biases [512]

比上一个Model增加了三倍。

而tf.train.GradientDescentOptimizer就没有这种影响

所以，肯定是Adam的原因。至于细节，谁能提供一下。

如何计算tensorflow计算model的大小size（2）

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