我是TensorFlow和机器学习的新手。我正在尝试使用张量流创建情感分析神经网络。
我已经设置好架构，正在尝试训练模型，但是遇到错误
ValueError：无法为张量为'（？，100）'的张量'InputData / X：0'输入形状（32，2）的值
我认为该错误与我的输入“ layer net = tflearn.input_data（[None，100]）”有关。 我遵循的教程建议此输入形状，批处理大小为“无”，长度为100，因为这是序列长度。因此，（无，100），据我所知，这是输入到网络中的训练数据所需的维度，对吗？
有人可以解释为什么建议的批量大小输入形状为何为None，以及为什么Tensor流试图馈入放置了形状的网络（32,2）。 2的序列长度从何而来？
如果我对这种解释的理解有误，请随时纠正我，我仍在尝试学习该理论。
预先感谢
In [1]:
import tflearn
from tflearn.data_utils import to_categorical, pad_sequences
from tflearn.datasets import imdb
In [2]:
#Loading IMDB dataset
train, test, _ = imdb.load_data(path='imdb.pkl', n_words=10000,
valid_portion=0.1)
trainX, trainY = train
testX, testY = test
In [3]:
#Data sequence padding
trainX = pad_sequences(trainX, maxlen=100, value=0.)
testX = pad_sequences(testX, maxlen=100, value=0.)
#converting labels of each review to vectors
trainY = to_categorical(trainY, nb_classes=2)
trainX = to_categorical(testY, nb_classes=2)
In [4]:
#network building
net = tflearn.input_data([None, 100])
net = tflearn.embedding(net, input_dim=10000, output_dim=128)
net = tflearn.lstm(net, 128, dropout = 0.8)
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net, optimizer = 'adam', learning_rate=0.0001,
loss='categorical_crossentropy')
WARNING:tensorflow:From C:\Users\Nason\Anaconda33\envs\TensorFlow1.8CPU\lib\site-packages\tflearn\objectives.py:66: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
In [5]:
#Training
model = tflearn.DNN(net, tensorboard_verbose=0) #train using tensorflow Deep nueral net
model.fit(trainX, trainY, validation_set=(testX, testY), show_metric=True, #fit launches training process for training and validation data, metric displays data as its training.
batch_size=32)
---------------------------------
Run id: U7NONK
Log directory: /tmp/tflearn_logs/
INFO:tensorflow:Summary name Accuracy/ (raw) is illegal; using Accuracy/__raw_ instead.
---------------------------------
Training samples: 2500
Validation samples: 2500
--
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-7ffd0a8836f9> in <module>()
2 model = tflearn.DNN(net, tensorboard_verbose=0) #train using tensorflow Deep nueral net
3 model.fit(trainX, trainY, validation_set=(testX, testY), show_metric=True, #fit launches training process for training and validation data, metric displays data as its training.
----> 4 batch_size=32)
~\Anaconda33\envs\TensorFlow1.8CPU\lib\site-packages\tflearn\models\dnn.py in fit(self, X_inputs, Y_targets, n_epoch, validation_set, show_metric, batch_size, shuffle, snapshot_epoch, snapshot_step, excl_trainops, validation_batch_size, run_id, callbacks)
214 excl_trainops=excl_trainops,
215 run_id=run_id,
--> 216 callbacks=callbacks)
217
218 def fit_batch(self, X_inputs, Y_targets):
~\Anaconda33\envs\TensorFlow1.8CPU\lib\site-packages\tflearn\helpers\trainer.py in fit(self, feed_dicts, n_epoch, val_feed_dicts, show_metric, snapshot_step, snapshot_epoch, shuffle_all, dprep_dict, daug_dict, excl_trainops, run_id, callbacks)
337 (bool(self.best_checkpoint_path) | snapshot_epoch),
338 snapshot_step,
--> 339 show_metric)
340
341 # Update training state
~\Anaconda33\envs\TensorFlow1.8CPU\lib\site-packages\tflearn\helpers\trainer.py in _train(self, training_step, snapshot_epoch, snapshot_step, show_metric)
816 tflearn.is_training(True, session=self.session)
817 _, train_summ_str = self.session.run([self.train, self.summ_op],
--> 818 feed_batch)
819
820 # Retrieve loss value from summary string
~\Anaconda33\envs\TensorFlow1.8CPU\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
898 try:
899 result = self._run(None, fetches, feed_dict, options_ptr,
--> 900 run_metadata_ptr)
901 if run_metadata:
902 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
~\Anaconda33\envs\TensorFlow1.8CPU\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1109 'which has shape %r' %
1110 (np_val.shape, subfeed_t.name,
-> 1111 str(subfeed_t.get_shape())))
1112 if not self.graph.is_feedable(subfeed_t):
1113 raise ValueError('Tensor %s may not be fed.' % subfeed_t)
ValueError: Cannot feed value of shape (32, 2) for Tensor 'InputData/X:0', which has shape '(?, 100)'
答案 0 :(得分：0)
错误来自trainX = to_categorical(testY, nb_classes=2)。这需要更改为testY = to_categorical(testY, nb_classes=2)
此外，将批次大小设置为None意味着它应该期望该批次为任何大小。在您的情况下，请将批量大小设置为32，因此也可以将输入形状设置为[32, 100]
答案 1 :(得分：0)
您将trainX的类别数保留为2，但是您的模型期望使用100。
编辑：
我只是注意到您在这段代码中将trainX设置为testY：
trainX = to_categorical(testY, nb_classes=2)
应为：
trainX = to_categorical(trainX, nb_classes=100)
因此，您需要将代码更改为：
#Data sequence padding
trainX = pad_sequences(trainX, maxlen=100, value=0.)
testX = pad_sequences(testX, maxlen=100, value=0.)
#converting labels of each review to vectors
trainY = to_categorical(trainY, nb_classes=2)
#change the number of Classes
trainX = to_categorical(trainX, nb_classes=100) #CHANGE HERE!!
进行此更改后，您会没事的。我刚刚测试过，就可以了！
可以使用[None，100]设置输入的形状，这样可以在以后需要时提供更大的灵活性来更改批量大小！
答案 2 :(得分：0)
tflearn.input_data([None, 100])
您期望输入是具有100个要素的任意数量实例的张量。
trainX = pad_sequences(trainX, maxlen=100, value=0.)
testX = pad_sequences(testX, maxlen=100, value=0.)
#converting labels of each review to vectors
trainY = to_categorical(trainY, nb_classes=2)
trainX = to_categorical(testY, nb_classes=2) #HEREEEEEE
这在您的代码中有问题。您正在将trainX重置为具有其他形状，而不是填充的形状。我想你的意思是：
testY = to_categorical(testY, nb_classes=2)
如果这仍然行不通。
我怀疑您缺少数据的重塑。实际上，您确实在使用填充，但是在整个trainX，trainY等上。尝试分别填充每个“行”。然后，每个实例的长度将与您期望的一样为“ 100”。
在此之前，打印张量的形状（如print(trainX.shape)），以查看您是否真的在预处理数据（我还建议做两个脚本，一个脚本进行整体加载，预处理，重塑和填充，以及其他带有tensorFlow逻辑）