我使用Tensorflow 1.3实现了一个RNN来执行情感分析。当我训练和测试模型时，我想创建一个函数，将"审查"作为输入，并找到审查的情绪。当我运行程序时，我得到这个错误，我不明白为什么:
Traceback (most recent call last):
File "/home/andreas/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1327, in _do_call
return fn(*args)
File "/home/andreas/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1306, in _run_fn
status, run_metadata)
File "/usr/lib/python3.6/contextlib.py", line 88, in __exit__
next(self.gen)
File "/home/andreas/.local/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype int32 and shape [?,250]
[[Node: Placeholder = Placeholder[dtype=DT_INT32, shape=[?,250], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/andreas/sentiment_analysis_with_rnn/sentiment_analysis_rnn.py", line 169, in <module>
find_sentiment("This is bad")
File "/home/andreas/sentiment_analysis_with_rnn/sentiment_analysis_rnn.py", line 166, in find_sentiment
print(sess.run([prediction], {x: review_array}))
File "/home/andreas/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 895, in run
run_metadata_ptr)
File "/home/andreas/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1124, in _run
feed_dict_tensor, options, run_metadata)
File "/home/andreas/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1321, in _do_run
options, run_metadata)
File "/home/andreas/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1340, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype int32 and shape [?,250]
[[Node: Placeholder = Placeholder[dtype=DT_INT32, shape=[?,250], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Caused by op 'Placeholder', defined at:
File "/home/andreas/sentiment_analysis_with_rnn/sentiment_analysis_rnn.py", line 95, in <module>
x = tf.placeholder(tf.int32, [None, MAX_WORDS])
File "/home/andreas/.local/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py", line 1548, in placeholder
return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)
File "/home/andreas/.local/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py", line 2094, in _placeholder
name=name)
File "/home/andreas/.local/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/home/andreas/.local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2630, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/home/andreas/.local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1204, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder' with dtype int32 and shape [?,250]
[[Node: Placeholder = Placeholder[dtype=DT_INT32, shape=[?,250], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
这是我的功能
def find_sentiment(text):
vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(MAX_WORDS)
review_array = np.array(list(vocab_processor.fit_transform(text)))
print(review_array)
x = tf.placeholder(tf.int32, [None, MAX_WORDS])
with tf.Session() as sess:
saver = tf.train.Saver()
saver.restore(sess, 'saved/model.ckpt')
print(sess.run([prediction], {x: review_array}))
find_sentiment("This is bad")
我是这样训练和测试RNN的:
MAX_WORDS = 250
# pad short reviews and truncate long reviews so that all reviews have the same size
vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(MAX_WORDS)
x_data = np.array(list(vocab_processor.fit_transform(data)))
y_output = np.array(labels)
vocabulary_size = len(vocab_processor.vocabulary_)
np.random.seed(22)
shuffle_indices = np.random.permutation(np.arange(len(x_data)))
x_shuffled = x_data[shuffle_indices]
y_shuffled = y_output[shuffle_indices]
TRAIN_DATA = 24000
TOTAL_DATA = len(x_data)
# neural networks perform better when datasets are huge
train_data = x_shuffled[:TRAIN_DATA]
train_target = y_shuffled[:TRAIN_DATA]
tf.reset_default_graph()
x = tf.placeholder(tf.int32, [None, MAX_WORDS])
y = tf.placeholder(tf.int32, [None])
with tf.Session() as session:
session.run(tf.global_variables_initializer())
for epoch in range(epochs):
num_batches = len(train_data) // batch_size
num_batches += 1
for i in range(num_batches):
lower_bound = i * batch_size # lower bound of batch
upper_bound = np.min([len(train_data), ((i+1) * batch_size)]) # upper bound of batch
x_train_batch = train_data[lower_bound:upper_bound]
y_train_batch = train_target[lower_bound:upper_bound]
session.run(train_step, feed_dict={x: x_train_batch, y: y_train_batch})
train_loss, train_acc = session.run([loss, accuracy], feed_dict={x: x_train_batch, y: y_train_batch})
# hyper parameters that can be used to improve the model is to increase the number of epochs, batch size and use
# a larger dataset for training
test_loss, test_acc = session.run([loss, accuracy], feed_dict={x: test_data, y: test_target})
print("Epoch:", epoch+1, "Test loss:", test_loss, "Test accuracy:", test_acc)
# saver = tf.train.Saver()
# save_path = saver.save(session, "saved/model.ckpt")
我做错了什么?我的代码工作在火车数据是长度为250的二维数组:当我运行find_sentiment:
def find_sentiment(text):
vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(MAX_WORDS)
review_array = np.array(list(vocab_processor.fit_transform(text)))
print(review_array)
x = tf.placeholder(tf.int32, [MAX_WORDS])
print(len(review_array))
review_array.reshape(MAX_WORDS)
with tf.Session() as sess:
saver = tf.train.Saver()
saver.restore(sess, 'saved/model.ckpt')
print(sess.run([prediction], {x: review_array}))
我得到这样的输出:
[[1 0 0 ... 0 0 0]
[2 0 0 ... 0 0 0]
[3 0 0 ... 0 0 0]
...
[5 0 0 ... 0 0 0]
[6 0 0 ... 0 0 0]
[7 0 0 ... 0 0 0]]
11
它说我的矩阵的长度是11它的长度应该是250。还出现这个错误:
Traceback (most recent call last):
File "/home/andreas/sentiment_analysis_with_rnn/sentiment_analysis_rnn.py", line 173, in <module>
find_sentiment("This is bad")
File "/home/andreas/sentiment_analysis_with_rnn/sentiment_analysis_rnn.py", line 165, in find_sentiment
review_array.reshape(MAX_WORDS)
ValueError: cannot reshape array of size 2750 into shape (250,)
2750/11=250，等于MAX_WORDS。我错过了什么?
我不太确定Tensorflow占位符是如何工作的，但从堆栈跟踪和我所理解的，当做
时，您没有向y占位符提供任何数据print(sess.run([prediction], {x: review_array}))
这将解释You must feed a value for placeholder tensor 'Placeholder_1'消息