收到我不知道如何解决的错误。有任何想法吗？形状输入可能有问题吗？
import numpy as np
import tensorflow as tf
from tensorflow.contrib import rnn
import random
import collections
import time
start_time = time.time()
def elapsed(sec):
if sec<60:
return str(sec) + " sec"
elif sec<(60*60):
return str(sec/60) + " min"
else:
return str(sec/60*60) + " hr"
log_path = '/tmp/tensorflow/rnn_words'
writer = tf.summary.FileWriter(log_path)
training_file = r'C:\Users\danie\Desktop\versuch.txt'
def read_data(file):
with open (file) as f:
content = f.readlines()
content = [x.strip() for x in content]
content = [content[i].split() for i in range (len(content))]
content = np.array(content)
content = np.reshape(content,[-1,])
return content
training_data = read_data(training_file)
def build_dataset(words):
count = collections.Counter(words).most_common()
dictionary = dict()
for word, _ in count:
dictionary[word] = len(dictionary)
reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
return dictionary, reverse_dictionary
dictionary, reverse_dictionary = build_dataset(training_data)
vocab_size = len(dictionary)
learning_rate = 0.001
training_iters = 50000
display_step = 1000
n_input = 3
n_hidden = 512
x = tf.placeholder("float", [None, n_input,1])
y = tf.placeholder("float", [None,vocab_size])
weights = {
'out': tf.Variable(tf.random_normal([n_hidden, vocab_size]))
}
biases = {
'out': tf.Variable(tf.random_normal([vocab_size]))
}
def RNN(x, weights, biases):
x = tf.reshape (x,[-1, n_input])
x = tf.split(x,n_input,1)
#rnn_cell = rnn.MultiRNNCell([rnn.BasicLSTMCell(n_hidden), rnn.BasicLSTMCell(n_hidden)])
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)
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))
init = tf.global_variables_initializer()
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
writer.add_graph(session.graph)
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
_, 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]
symbolds_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)
print("Optimizer finished")
print('Elapsed Time: ', elapsed(time.time()-start_time))
print("Run Command line")
print('')
print("Point your web browser to: http://localhost:6006/")
while True:
promt = "%s words: " % n_input
sentence = input(prompt)
sentence = sentence.strip()
words = sentence.split(' ')
if len(words) != n_input:
continue
try:
symbols_in_keys = [dictionary[str(words[i])] for i in range(len(words))]
for i in range(32):
keys = np.reshape(np.array(symbols_in_keys), [-1, n_input,1])
onehot_pred = session.run(pred, feed_dict={x: keys})
onehot_pred_index = int(tf.argmax(onehot_pred,1).eval())
sentense = "%s %s" (sentence, reverse_dictionary[onehot_pred_index])
symbols_in_keys = symbols_in_keys[1:]
symbols_in_keys.append(onehot_pred_index)
print(sentence)
except:
print("Word not in dictionary")
收到我不知道如何解决的错误。有任何想法吗？形状输入可能有问题吗？谢谢 感谢您的回复，我添加了收到的错误消息。希望有所帮助。 编辑：这是错误
ValueError Traceback (most recent call last)
<ipython-input-1-e2e4bba12bcc> in <module>()
103 symbols_out_onehot[dictionary[str(training_data[offset+n_input])]] = 1.0
104
--> 105 _, acc, loss, onehot_pred = session.run([optimizer, accuracy, cost, pred], feed_dict = {x: symbols_in_keys, y: symbols_out_onehot})
106
107 loss_total+= loss
~\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
787 try:
788 result = self._run(None, fetches, feed_dict, options_ptr,
--> 789 run_metadata_ptr)
790 if run_metadata:
791 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
~\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
973 'Cannot feed value of shape %r for Tensor %r, '
974 'which has shape %r'
--> 975 % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
976 if not self.graph.is_feedable(subfeed_t):
977 raise ValueError('Tensor %s may not be fed.' % subfeed_t)
ValueError: Cannot feed value of shape (39,) for Tensor 'Placeholder_1:0', which has shape '(?, 39)'
............................................... .................................................. .................................................. .........................................
答案 0 :(得分：1)
发生错误是因为您的占位符与您的数据不匹配：
y = tf.placeholder("float", [None,vocab_size])
symbols_out_onehot = np.zeros([vocab_size], dtype = float)
feed_dict = {..., y: symbols_out_onehot})
y的排名为[None, vocab_size]，排名为2，但symbols_out_onehot的排名为[vocab_size]，排名为1，导致错误。
您应该更改占位符y或symbols_out_onehot。通常，将第一个维度作为batch_size是很常见的，因此最好将输入数据更改为：
symbols_out_onehot = np.zeros([1, vocab_size], dtype = float)