我正在张力流中编写一个受限制的玻尔兹曼机器，为了理解算法，我想在计算它们的过程中打印东西。我在算法的第一部分做了一个简单的尝试：
import tensorflow as tf
import numpy as np
X_train = np.genfromtxt("binMNIST_data/bindigit_trn.csv", dtype=float, delimiter=",")
Y_train = np.genfromtxt("binMNIST_data/targetdigit_trn.csv", dtype=float, delimiter=",")
X_test = np.genfromtxt("binMNIST_data/bindigit_tst.csv", dtype=float, delimiter=",")
Y_test = np.genfromtxt("binMNIST_data/targetdigit_tst.csv", dtype=float, delimiter=",")
ds_train = tf.data.Dataset.from_tensor_slices((X_train, Y_train))
ds_test = tf.data.Dataset.from_tensor_slices((X_test, Y_test))
it = tf.data.Iterator.from_structure(ds_train.output_types, ds_train.output_shapes)
train_init_op = it.make_initializer(ds_train)
test_init_op = it.make_initializer(ds_test)
vb = tf.placeholder(tf.float64, [784])
hb = tf.placeholder(tf.float64, [500])
W = tf.placeholder(tf.float64, [784, 500])
# Features and labels
x, y = it.get_next()
_h = tf.nn.sigmoid(tf.matmul(tf.reshape(x, [1, 784]), W)+hb)
h = tf.nn.relu(tf.sign(_h-tf.random_uniform(tf.shape(_h), dtype=tf.float64)))
#Initial bias values
vb_init = np.zeros([784])
hb_init = np.zeros([500])
#Initial W value
W_init = np.zeros([784, 500])
with tf.Session() as sess:
sess.run(train_init_op)
print(sess.run(_h, feed_dict={vb:vb_init, hb:hb_init, W:W_init}))
print(sess.run(h))
但不幸的是，该程序的最后一行导致了这个错误：
Traceback (most recent call last):
File "/Users/sahandzarrinkoub/.virtualenvs/untitled/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1361, in _do_call
return fn(*args)
File "/Users/sahandzarrinkoub/.virtualenvs/untitled/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1340, in _run_fn
target_list, status, run_metadata)
File "/Users/sahandzarrinkoub/.virtualenvs/untitled/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 516, in __exit__
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_2' with dtype double and shape [784,500]
[[Node: Placeholder_2 = Placeholder[dtype=DT_DOUBLE, shape=[784,500], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/sahandzarrinkoub/Documents/Programming/ANN/lab4/notebook/RBM.py", line 41, in <module>
print(sess.run(h))
File "/Users/sahandzarrinkoub/.virtualenvs/untitled/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 905, in run
run_metadata_ptr)
File "/Users/sahandzarrinkoub/.virtualenvs/untitled/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1137, in _run
feed_dict_tensor, options, run_metadata)
File "/Users/sahandzarrinkoub/.virtualenvs/untitled/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1355, in _do_run
options, run_metadata)
File "/Users/sahandzarrinkoub/.virtualenvs/untitled/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1374, 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_2' with dtype double and shape [784,500]
[[Node: Placeholder_2 = Placeholder[dtype=DT_DOUBLE, shape=[784,500], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Caused by op 'Placeholder_2', defined at:
File "/Users/sahandzarrinkoub/Documents/Programming/ANN/lab4/notebook/RBM.py", line 24, in <module>
W = tf.placeholder(tf.float64, [784, 500])
File "/Users/sahandzarrinkoub/.virtualenvs/untitled/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py", line 1746, in placeholder
return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)
File "/Users/sahandzarrinkoub/.virtualenvs/untitled/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py", line 3051, in _placeholder
"Placeholder", dtype=dtype, shape=shape, name=name)
File "/Users/sahandzarrinkoub/.virtualenvs/untitled/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/Users/sahandzarrinkoub/.virtualenvs/untitled/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 3271, in create_op
op_def=op_def)
File "/Users/sahandzarrinkoub/.virtualenvs/untitled/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1650, 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_2' with dtype double and shape [784,500]
[[Node: Placeholder_2 = Placeholder[dtype=DT_DOUBLE, shape=[784,500], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
简而言之，它抱怨我没有为占位符提供任何值。当然我不想这样做，而是使用张量流来使用我在之前的sess.run()调用中给出的任何东西。这可能吗？
即使您的解决方案适用于您的问题，您也可以考虑采用更通用的方法在一次通话中获取所需的一切。
sess.run(train_init_op)
print(sess.run(_h, feed_dict={vb:vb_init, hb:hb_init, W:W_init}))
print(sess.run(h))
变
_, _h_val, h_val = sess.run([train_init_op, _h, h], feed_dict={vb:vb_init, hb:hb_init, W:W_init})
print(_h_val)
print(h_val)
因为Session是有状态的，所以你无法保证连续的调用是一致的。最常见的示例是每次查询时绘制新数字的随机生成器。
当您对Session.run进行一次调用时，您（几乎）确定返回的值是一致的。
我自己找到了答案：
只需将_h传递给最后一个feed_dict电话的sess_run()。无需再次发送所有内容：
印刷：
with tf.Session() as sess:
sess.run(train_init_op)
h0 = sess.run(_h, feed_dict={vb:vb_init, hb:hb_init, W:W_init})
print(h0)
print(sess.run(h, feed_dict={_h:h0}))