使用Tensorflow通过2层RNN方法对MNIST数字进行分类。培训工作正常，但在评估准确性时，会报告不兼容的测试数据形状。
import tensorflow as tf
import inspect
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)
hm_epochs = 1
n_classes = 10
batch_size = 128
chunk_size = 28
n_chunks = 28
rnn_size = 128
x = tf.placeholder('float', [None, n_chunks,chunk_size])
y = tf.placeholder('float')
def lstm_cell():
if 'reuse' in inspect.getargspec(
tf.contrib.rnn.BasicLSTMCell.__init__).args:
return tf.contrib.rnn.BasicLSTMCell(
rnn_size, forget_bias=0.0, state_is_tuple=True,
reuse=tf.get_variable_scope().reuse)
else:
return tf.contrib.rnn.BasicLSTMCell(
rnn_size, forget_bias=0.0, state_is_tuple=True)
def attn_cell():
return tf.contrib.rnn.DropoutWrapper(
lstm_cell())
def recurrent_neural_network(x):
layer = {'weights':tf.Variable(tf.random_normal([rnn_size,n_classes])),
'biases':tf.Variable(tf.random_normal([n_classes]))}
x = tf.transpose(x, [1,0,2])
x = tf.reshape(x, [-1, chunk_size])
x = tf.split(x, n_chunks, 0)
stacked_lstm = tf.contrib.rnn.MultiRNNCell([attn_cell(),attn_cell()], state_is_tuple=True)
initial_state = state = stacked_lstm.zero_state(batch_size, tf.float32)
outputs, states = tf.contrib.rnn.static_rnn(stacked_lstm, x,state)
output = tf.matmul(outputs[-1],layer['weights']) + layer['biases']
return output
def train_neural_network(x):
prediction = recurrent_neural_network(x)
cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction) )
optimizer = tf.train.AdamOptimizer().minimize(cost)
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for epoch in range(hm_epochs):
epoch_loss = 0
for _ in range(int(mnist.train.num_examples/batch_size)):
epoch_x, epoch_y = mnist.train.next_batch(batch_size)
epoch_x = epoch_x.reshape((batch_size,n_chunks,chunk_size))
_, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
epoch_loss += c
print('Epoch', epoch, 'completed out of',hm_epochs,'loss:',epoch_loss)
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
testdata= np.reshape( mnist.test.images, (10000, n_chunks, chunk_size))
print("Testdata ",testdata.shape)
print("x ",x)
print('Accuracy:',accuracy.eval({x:testdata, y:mnist.test.labels}))
train_neural_network(x)
但是，测试数据和占位符的形状打印如下。 Aren他们兼容吗？
Epoch 0 completed out of 1 loss: 228.159379691
Testdata (10000, 28, 28)
x Tensor("Placeholder:0", shape=(?, 28, 28), dtype=float32)
错误：
Caused by op 'rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/basic_lstm_ce
ll/concat', defined at:
File "main.py", line 90, in <module>
train_neural_network(x)
File "main.py", line 59, in train_neural_network
prediction = recurrent_neural_network(x)
File "main.py", line 52, in recurrent_neural_network
outputs, states = tf.contrib.rnn.static_rnn(stacked_lstm, x,state)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn.py"
, line 1212, in static_rnn
(output, state) = call_cell()
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn.py"
, line 1199, in <lambda>
call_cell = lambda: cell(input_, state)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cel
l_impl.py", line 180, in __call__
return super(RNNCell, self).__call__(inputs, state)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\layers\base
.py", line 441, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cel
l_impl.py", line 916, in call
cur_inp, new_state = cell(cur_inp, cur_state)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cel
l_impl.py", line 752, in __call__
output, new_state = self._cell(inputs, state, scope)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cel
l_impl.py", line 180, in __call__
return super(RNNCell, self).__call__(inputs, state)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\layers\base
.py", line 441, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cel
l_impl.py", line 383, in call
concat = _linear([inputs, h], 4 * self._num_units, True)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn_cel
l_impl.py", line 1021, in _linear
res = math_ops.matmul(array_ops.concat(args, 1), weights)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\array_o
ps.py", line 1048, in concat
name=name)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_arr
ay_ops.py", line 495, in _concat_v2
name=name)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\framework\o
p_def_library.py", line 767, in apply_op
op_def=op_def)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\framework\o
ps.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
File "C:\Users\henry\Anaconda3\lib\site-packages\tensorflow\python\framework\o
ps.py", line 1269, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): ConcatOp : Dimensions of inputs
should match: shape[0] = [10000,28] vs. shape[1] = [128,128]
[[Node: rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/basic_lstm
_cell/concat = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32, _device="/job:localhost/
replica:0/task:0/cpu:0"](split, MultiRNNCellZeroState/DropoutWrapperZeroState/Ba
sicLSTMCellZeroState/zeros_1, rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_ce
ll/basic_lstm_cell/concat/axis)]]
当我打印训练数据的形状时，它是（128,28,28）。我很困惑，为什么测试数据导致错误，因为训练数据和测试数据都以相同的方式格式化，即（？，n_chunks，chunk_size）。提前谢谢。
答案 0 :(得分：0)
问题是，您总是创建形状设置为训练批次大小而不是评估批次大小的初始状态。
这是罪魁祸首：
initial_state = state = stacked_lstm.zero_state(batch_size, tf.float32)