我正在研究预测二元选择的NN。当我尝试提取预测时，它不起作用，并给我这个痕迹：
2017-09-03 13:52:59.302796: W tensorflow/core/framework/op_kernel.cc:1148] Invalid argument: Shape [-1,2] has negative dimensions
2017-09-03 13:52:59.302843: E tensorflow/core/common_runtime/executor.cc:644] Executor failed to create kernel. Invalid argument: Shape [-1,2] has negative dimensions
[[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[?,2], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
2017-09-03 13:52:59.302922: W tensorflow/core/framework/op_kernel.cc:1148] Invalid argument: Shape [-1,2] has negative dimensions
2017-09-03 13:52:59.302939: E tensorflow/core/common_runtime/executor.cc:644] Executor failed to create kernel. Invalid argument: Shape [-1,2] has negative dimensions
[[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[?,2], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Traceback (most recent call last):
File "/home/tucker/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1139, in _do_call
return fn(*args)
File "/home/tucker/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1121, in _run_fn
status, run_metadata)
File "/home/tucker/anaconda3/lib/python3.5/contextlib.py", line 66, in __exit__
next(self.gen)
File "/home/tucker/anaconda3/lib/python3.5/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: Shape [-1,2] has negative dimensions
[[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[?,2], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "train.py", line 104, in <module>
print(sess.run(y, feed_dict={x: future_x}))
File "/home/tucker/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 789, in run
run_metadata_ptr)
File "/home/tucker/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 997, in _run
feed_dict_string, options, run_metadata)
File "/home/tucker/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1132, in _do_run
target_list, options, run_metadata)
File "/home/tucker/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1152, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Shape [-1,2] has negative dimensions
[[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[?,2], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Caused by op 'Placeholder_1', defined at:
File "train.py", line 37, in <module>
y = tf.placeholder("float32", [None, num_classes])
File "/home/tucker/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py", line 1530, in placeholder
return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)
File "/home/tucker/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py", line 1954, in _placeholder
name=name)
File "/home/tucker/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/home/tucker/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/home/tucker/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1269, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): Shape [-1,2] has negative dimensions
[[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[?,2], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
看起来它来自我定义我的＆＃34; y＆＃34;占位符，但我无法弄清楚这个问题。它只发生在我试图得到一个铭文时
这是我的代码（它看起来很奇怪，因为我从一个jupyter笔记本中取出它）：
# coding: utf-8
# # Imports
# In[1]:
from config import train
from config.data_parameters import data_params
from utils import train_utils
import tensorflow as tf
import numpy as np
sess = tf.InteractiveSession()
#get_ipython().magic('load_ext autoreload')
#get_ipython().magic('autoreload 2')
# # Get Data
# In[2]:
train_x, train_y, test_x, test_y, future_x = train_utils.get_shelve()
print (future_x)
# # Begin TF Stuff
# ## Initialize Vars
# In[3]:
num_chunks = len(train_x[0])
look_back = data_params['look_back']
num_classes = len(test_y[0])
x = tf.placeholder("float32", [None, num_chunks, look_back])
y = tf.placeholder("float32", [None, num_classes])
# ## Create Weights and Biases
# In[4]:
neurons = train.network['neurons']
weights = {
'layer':tf.Variable(tf.random_normal([neurons, num_classes])),
}
biases = {
'layer':tf.Variable(tf.random_normal([num_classes])),
}
print (num_chunks)
# ## Create Network
# In[5]:
num_layers = train.network['layers']
output = train_utils.create_network(x = x,
weights = weights,
biases = biases,
neurons = neurons,
layers = num_layers,
num_chunks = num_chunks,
look_back = look_back)
# ## The Rest
# In[6]:
prediction = output
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = prediction, labels = y))
optimizer = tf.train.AdamOptimizer(train.parameters['learning_rate']).minimize(loss)
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
# In[8]:
batch_size = train.parameters['batch_size']
sess.run(tf.initialize_all_variables())
for epoch in range(train.parameters['epochs']):
epoch_loss = 0
i=0
accs = []
while i < len(train_x):
start = i
end = i+batch_size
batch_x = np.array(train_x[start:end])
batch_y = np.array(train_y[start:end])
_, c, acc = sess.run([optimizer, loss, accuracy], feed_dict={x: batch_x,
y: batch_y})
accs.append(acc)
epoch_loss += c
i+=batch_size
print ("Epoch %s completed out of %s | Loss: %s | Acc: %s" % (epoch + 1,
train.parameters['epochs'],
epoch_loss,
np.mean(accs)))
print(sess.run(y, feed_dict={x: future_x}))
# In[ ]:
# In[ ]:
以下是＆＃34; train_utils.create_network（x = x，权重=权重，偏差=偏差，神经元=神经元，图层= num_layers，num_chunks = num_chunks，look_back = look_back）＆＃34时执行的代码;行运行：
def create_network(x, weights, biases, neurons, layers, num_chunks, look_back):
# x: tf var
# weights: weights defined in file
# biases: biases defined in file
# num_chunks: num_chunks defined in file
# look_back: look_back defined in file
#return: idk just take it
x = tf.transpose(x, [1,0,2])
x = tf.reshape(x, [-1, look_back])
x = tf.split(x, num_chunks, 0)
cell = rnn.LSTMCell(neurons, state_is_tuple=True)
def lstm_cell():
return rnn.LSTMCell(neurons, state_is_tuple=True)
stacked_lstm = rnn.MultiRNNCell([lstm_cell() for _ in range(layers)])
outputs, states = rnn.static_rnn(cell, x, dtype=tf.float32)
output = tf.matmul(outputs[-1], weights['layer']) + biases['layer']
return output
提前致谢
答案 0 :(得分：2)
预测由张量prediction（或output给出，因为它是相同的），而不是占位符y（在训练期间放置标签的位置） 。预测代码应该是这样的：
print(sess.run(prediction, feed_dict={x: future_x}))
答案 1 :(得分：0)
由于此行print(sess.run(y, feed_dict={x: future_x}))而导致错误，您试图通过提供另一个占位符y来获取x。此处x和y是独立的。
应纠正如下;您需要为y
print(sess.run(y, feed_dict={y: y_test}))