Saver 保存读取
保存
import tensorflow as tf
import numpy as np
## Save to file
# remember to define the same dtype and shape when restore
W = tf.Variable([[1,2,3],[3,4,5]], dtype=tf.float32, name='weights')
b = tf.Variable([[1,2,3]], dtype=tf.float32, name='biases')
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
save_path = saver.save(sess, "my_net/save_net.ckpt")
print("Save to path: ", save_path)
"""
Save to path: my_net/save_net.ckpt
"""
提取
# 先建立 W, b 的容器
W = tf.Variable(np.arange(6).reshape((2, 3)), dtype=tf.float32, name="weights")
b = tf.Variable(np.arange(3).reshape((1, 3)), dtype=tf.float32, name="biases")
# 这里不需要初始化步骤 init= tf.initialize_all_variables()
saver = tf.train.Saver()
with tf.Session() as sess:
\# 提取变量
saver.restore\(sess, "my\_net/save\_net.ckpt"\)
print\("weights:", sess.run\(W\)\)
print\("biases:", sess.run\(b\)\)
"""
weights: [[ 1. 2. 3.]
\[ 3. 4. 5.\]\]
biases: [[ 1. 2. 3.]]
"""
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
tf.set_random_seed(1)
np.random.seed(1)
# fake data
x = np.linspace(-1, 1, 100)[:, np.newaxis] # shape (100, 1)
noise = np.random.normal(0, 0.1, size=x.shape)
y = np.power(x, 2) + noise # shape (100, 1) + some noise
def save():
print('This is save')
# build neural network
tf_x = tf.placeholder(tf.float32, x.shape) # input x
tf_y = tf.placeholder(tf.float32, y.shape) # input y
l = tf.layers.dense(tf_x, 10, tf.nn.relu) # hidden layer
o = tf.layers.dense(l, 1) # output layer
loss = tf.losses.mean_squared_error(tf_y, o) # compute cost
train_op = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(loss)
sess = tf.Session()
sess.run(tf.global_variables_initializer()) # initialize var in graph
saver = tf.train.Saver() # define a saver for saving and restoring
for step in range(100): # train
sess.run(train_op, {tf_x: x, tf_y: y})
saver.save(sess,'my_net/save_net.ckpt', write_meta_graph=False) # meta_graph is not recommended
# plotting
pred, l = sess.run([o, loss], {tf_x: x, tf_y: y})
plt.figure(1, figsize=(10, 5))
plt.subplot(121)
plt.scatter(x, y)
plt.plot(x, pred, 'r-', lw=5)
plt.text(-1, 1.2, 'Save Loss=%.4f' % l, fontdict={'size': 15, 'color': 'red'})
def reload():
print('This is reload')
# build entire net again and restore
tf_x = tf.placeholder(tf.float32, x.shape) # input x
tf_y = tf.placeholder(tf.float32, y.shape) # input y
l_ = tf.layers.dense(tf_x, 10, tf.nn.relu) # hidden layer
o_ = tf.layers.dense(l_, 1) # output layer
loss_ = tf.losses.mean_squared_error(tf_y, o_) # compute cost
sess = tf.Session()
# don't need to initialize variables, just restoring trained variables
saver = tf.train.Saver() # define a saver for saving and restoring
saver.restore(sess,'my_net/save_net.ckpt')
# plotting
pred, l = sess.run([o_, loss_], {tf_x: x, tf_y: y})
plt.subplot(122)
plt.scatter(x, y)
plt.plot(x, pred, 'r-', lw=5)
plt.text(-1, 1.2, 'Reload Loss=%.4f' % l, fontdict={'size': 15, 'color': 'red'})
plt.show()
save()
# destroy previous net
tf.reset_default_graph()
reload()
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