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我正努力從張量流中恢復NN的值。我試圖按照網上的例子，這裏是我的代碼：從NN恢復TensorFlow不起作用
import tensorflow as tf
import numpy as np
import math, random
import matplotlib.pyplot as plt
np.random.seed(1000) # for repro
function_to_learn = lambda x: np.sin(x) + 0.1*np.random.randn(*x.shape)
NUM_HIDDEN_NODES = 2
NUM_EXAMPLES = 1000
TRAIN_SPLIT = .8
MINI_BATCH_SIZE = 100
NUM_EPOCHS = 500
all_x = np.float32(np.random.uniform(-2*math.pi, 2*math.pi, (1, NUM_EXAMPLES))).T
np.random.shuffle(all_x)
train_size = int(NUM_EXAMPLES*TRAIN_SPLIT)
trainx = all_x[:train_size]
validx = all_x[train_size:]
trainy = function_to_learn(trainx)
validy = function_to_learn(validx)
plt.figure()
plt.scatter(trainx, trainy, c='green', label='train')
plt.scatter(validx, validy, c='red', label='validation')
plt.legend()
X = tf.placeholder(tf.float32, [None, 1], name="X")
Y = tf.placeholder(tf.float32, [None, 1], name="Y")
w_h = tf.Variable(tf.zeros([1, NUM_HIDDEN_NODES],name="w_h"))
b_h = tf.Variable(tf.zeros([1, NUM_HIDDEN_NODES],name="b_h"))
w_o = tf.Variable(tf.zeros([NUM_HIDDEN_NODES,1],name="w_o"))
b_o = tf.Variable(tf.zeros([1, 1],name="b_o"))
def init_weights(shape, init_method='xavier', xavier_params = (None, None)):
if init_method == 'zeros':
return tf.Variable(tf.zeros(shape, dtype=tf.float32))
elif init_method == 'uniform':
return tf.Variable(tf.random_normal(shape, stddev=0.01, dtype=tf.float32))
def model(X, num_hidden = NUM_HIDDEN_NODES):
w_h = init_weights([1, num_hidden], 'uniform')
b_h = init_weights([1, num_hidden], 'zeros')
h = tf.nn.sigmoid(tf.matmul(X, w_h) + b_h)
w_o = init_weights([num_hidden, 1], 'xavier', xavier_params=(num_hidden, 1))
b_o = init_weights([1, 1], 'zeros')
return tf.matmul(h, w_o) + b_o
yhat = model(X, NUM_HIDDEN_NODES)
train_op = tf.train.AdamOptimizer().minimize(tf.nn.l2_loss(yhat - Y))
plt.figure()
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for v in tf.all_variables():
print v.name
saver = tf.train.Saver()
errors = []
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for i in range(NUM_EPOCHS):
for start, end in zip(range(0, len(trainx), MINI_BATCH_SIZE), range(MINI_BATCH_SIZE, len(trainx), MINI_BATCH_SIZE)):
sess.run(train_op, feed_dict={X: trainx[start:end], Y: trainy[start:end]})
mse = sess.run(tf.nn.l2_loss(yhat - validy), feed_dict={X:validx})
errors.append(mse)
if i%100 == 0:
print "epoch %d, validation MSE %g" % (i, mse)
print sess.run(w_h)
saver.save(sess,"/Python/tensorflow/res/save_net.ckpt", global_step = i)
print " ******* AFTR *******"
for v in tf.all_variables():
print v.name
plt.plot(errors)
plt.xlabel('#epochs')
plt.ylabel('MSE')
*******得到恢復價值觀，我想：**
import tensorflow as tf
import numpy as np
import math, random
import matplotlib.pyplot as plt
NUM_HIDDEN_NODES = 2
#SECOND PART TO GET THE STORED VALUES
w_h = tf.Variable(np.arange(NUM_HIDDEN_NODES).reshape(1, NUM_HIDDEN_NODES), dtype=tf.float32, name='w_h')
b_h = tf.Variable(np.arange(NUM_HIDDEN_NODES).reshape(1, NUM_HIDDEN_NODES), dtype=tf.float32, name='b_h')
w_o = tf.Variable(np.arange(NUM_HIDDEN_NODES).reshape(NUM_HIDDEN_NODES, 1), dtype=tf.float32, name='w_o')
b_o = tf.Variable(np.arange(1).reshape(1, 1), dtype=tf.float32, name='b_o')
saver = tf.train.Saver()
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state("/Python/tensorflow/res/")
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
saver.restore(sess, "/Python/tensorflow/res/save_net.ckpt-400")
print "Model loaded"
else:
print "No checkpoint file found"
print("weights:", sess.run(w_h))
print("biases:", sess.run(b_h))
您的幫助非常感謝，我幾乎放棄了這一點。
非常感謝再次