Python 3.x 未找到:键变量\uUlt;x>;在检查点中找不到
我试图保存一个经过训练的模型,稍后在另一个实例(函数)中使用它。但是,不知怎的,这让我产生了变量not found错误。在阅读了SO和其他论坛之后,我明白了问题在于我存储它的方式Python 3.x 未找到:键变量\uUlt;x>;在检查点中找不到,python-3.x,tensorflow,Python 3.x,Tensorflow,我试图保存一个经过训练的模型,稍后在另一个实例(函数)中使用它。但是,不知怎的,这让我产生了变量not found错误。在阅读了SO和其他论坛之后,我明白了问题在于我存储它的方式 dictionary, reverse_dictionary = build_dataset(training_data) vocab_size = len(dictionary) n_input = 3 n_hidden = 512 # RNN output node we
dictionary, reverse_dictionary = build_dataset(training_data)
vocab_size = len(dictionary)
n_input = 3
n_hidden = 512
# RNN output node weights and biases
weights = {'out': tf.Variable(tf.random_normal([n_hidden, vocab_size]))}
biases = {'out': tf.Variable(tf.random_normal([vocab_size]))}
# tf Graph input
x = tf.placeholder("float", [None, n_input, 1])
y = tf.placeholder("float", [None, vocab_size])
# RNN implementation in Tensorflow
def RNN(x,weights,biases):
x = tf.reshape(x, [-1, n_input])
x = tf.split(x, n_input, 1)
rnn_cell = rnn.BasicLSTMCell(n_hidden)
outputs, states = rnn.static_rnn(rnn_cell, x, dtype=tf.float32)
return tf.matmul(outputs[-1], weights['out']) + biases['out']
pred = RNN(x, weights, biases)
learning_rate = 0.001
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate).minimize(cost)
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Initializing the variables
init = tf.global_variables_initializer()
training_iters = 1000
display_step = 500
saver = tf.train.Saver()
# Launch the graph
with tf.Session() as session:
session.run(init)
step = 0
offset = random.randint(0, n_input+1)
end_offset = n_input + 1
acc_total = 0
loss_total = 0
while step < training_iters:
if offset > (len(training_data)-end_offset):
offset = random.randint(0, n_input+1)
symbols_in_keys = [ [dictionary[ str(training_data[i])]] for i in range(offset, offset+n_input) ]
symbols_in_keys = np.reshape(np.array(symbols_in_keys), [-1, n_input, 1])
symbols_out_onehot = np.zeros([vocab_size], dtype=float)
symbols_out_onehot[dictionary[str(training_data[offset+n_input])]] = 1.0
symbols_out_onehot = np.reshape(symbols_out_onehot, [1, -1])
_, acc, loss, onehot_pred = session.run([optimizer, accuracy, cost, pred], \
feed_dict={x: symbols_in_keys, y: symbols_out_onehot})
loss_total += loss
acc_total += acc
if (step+1) % display_step == 0:
print("Iter= " + str(step+1) + ", Average Loss= " + \
"{:.6f}".format(loss_total/display_step) + ", Average Accuracy= " + \
"{:.2f}%".format(100*acc_total/display_step))
acc_total = 0
loss_total = 0
symbols_in = [training_data[i] for i in range(offset, offset + n_input)]
symbols_out = training_data[offset + n_input]
symbols_out_pred = reverse_dictionary[int(tf.argmax(onehot_pred, 1).eval())]
print("%s - [%s] vs [%s]" % (symbols_in,symbols_out,symbols_out_pred))
step += 1
offset += (n_input+1)
saver.save(session, 'userLocation/Model')
错误
任何关于我在保存时遗漏了什么的提示。我将在两个不同的部分中解释这一点-
import tensorflow as tf
import numpy as np
tf.set_random_seed(10)
#define graph location in variable
meta_file = 'userLocation/Model.meta'
#importing the graph
ns = tf.train.import_meta_graph(meta_file , clear_devices=True)
#create a session
with tf.Session().as_default() as sess:
#import variables
ns.restore(sess, meta_file[0:len(meta_file)-5])
# for example, if you have 'x' tenbsor in graph
x=tf.get_default_graph().get_tensor_by_name("x:0")
.
.
.
#Further processing/prediction etc
tensorflow.python.framework.errors_impl.NotFoundError: Key Variable_3 not found in checkpoint
[[Node: save_1/RestoreV2_7 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save_1/Const_0, save_1/RestoreV2_7/tensor_names, save_1/RestoreV2_7/shape_and_slices)]]
import tensorflow as tf
import numpy as np
tf.set_random_seed(10)
#define graph location in variable
meta_file = 'userLocation/Model.meta'
#importing the graph
ns = tf.train.import_meta_graph(meta_file , clear_devices=True)
#create a session
with tf.Session().as_default() as sess:
#import variables
ns.restore(sess, meta_file[0:len(meta_file)-5])
# for example, if you have 'x' tenbsor in graph
x=tf.get_default_graph().get_tensor_by_name("x:0")
.
.
.
#Further processing/prediction etc