我想将中间张量结果传递到while循环体内的完全连接的神经网络中。
我的问题与所述神经网络的实例化有关。 我的第一个尝试是为NN制作一个占位符，然后使用Session实例来填充它。它不起作用，因为feed_dict不接受张量。考虑到图表的数据流性质，这很公平。
第二次尝试时，我在循环体内实例化了NN并直接传递了中间张量。 但是，当我这样做时，将显示以下堆栈跟踪：
Caused by op u'net/fc1_W/read', defined at:
File "./main.py", line 176, in <module>
offline_indexing(sys.argv[1])
File "./main.py", line 128, in offline_indexing
test.run(a, f)
File "/home/lsv/Desktop/gitlab/Gencoding/test.py", line 86, in run
print sess.run(graph_embed(), feed_dict={adj: x, features: y})
File "/home/lsv/Desktop/gitlab/Gencoding/test.py", line 73, in graph_embed
final_mus = tf.while_loop(cond, body, [mus, features, adj, 0])[0]
File "/home/lsv/.local/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 3291, in while_loop
return_same_structure)
File "/home/lsv/.local/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 3004, in BuildLoop
pred, body, original_loop_vars, loop_vars, shape_invariants)
File "/home/lsv/.local/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2939, in _BuildLoop
body_result = body(*packed_vars_for_body)
File "/home/lsv/Desktop/gitlab/Gencoding/test.py", line 63, in body
h2 = fc_NN(mu_neigh_sum)
File "/home/lsv/Desktop/gitlab/Gencoding/test.py", line 53, in fc_NN
fc1 = fc_layer(ac2, 64, "fc1")
File "/home/lsv/Desktop/gitlab/Gencoding/test.py", line 43, in fc_layer
W = tf.get_variable(name+'_W', dtype=tf.float32, shape=[embedding_size, embedding_size], initializer=initer)
File "/home/lsv/.local/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 1487, in get_variable
aggregation=aggregation)
File "/home/lsv/.local/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 1237, in get_variable
aggregation=aggregation)
File "/home/lsv/.local/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 540, in get_variable
aggregation=aggregation)
File "/home/lsv/.local/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 492, in _true_getter
aggregation=aggregation)
File "/home/lsv/.local/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 922, in _get_single_variable
aggregation=aggregation)
File "/home/lsv/.local/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 183, in __call__
return cls._variable_v1_call(*args, **kwargs)
File "/home/lsv/.local/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 146, in _variable_v1_call
aggregation=aggregation)
FailedPreconditionError (see above for traceback): Attempting to use uninitialized value net/fc1_W
[[node net/fc1_W/read (defined at /home/lsv/Desktop/gitlab/Gencoding/test.py:43) = Identity[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](net/fc1_W)]]
请注意，使用密集层也会导致相同的错误。 这是我的代码：
def fc_layer(bottom, n_weight, name):
assert len(bottom.get_shape()) == 2
n_prev_weight = bottom.get_shape()[1]
initer = tf.truncated_normal_initializer(stddev=0.01)
W = tf.get_variable(name+'_W', dtype=tf.float32, shape=[embedding_size, embedding_size], initializer=initer)
b = tf.get_variable(name+'_b', dtype=tf.float32, shape=[embedding_size], initializer=tf.zeros_initializer)
fc = tf.nn.bias_add(tf.matmul(bottom, W), b)
return fc
def fc_NN(x):
fc2 = fc_layer(x, 64, "fc2")
ac2 = tf.nn.relu(fc2)
fc1 = fc_layer(ac2, 64, "fc1")
return fc1
fcnn = fc_NN()
def cond(m, f, a, i):
return tf.less(i, T)
def body(m, f, a, i):
mu_neigh_sum = tf.tensordot(a, m, 1)
h1 = tf.matmul(f, W1)
# First incriminating try - with x as a placeholder
h2 = tf.Session().run(fcnn, {x: mu_neigh_sum})
# Second incriminating try - with x as a tensor
h2bis = fc_NN(mu_neigh_sum)
return tf.tanh(h1 + h2), f, a, i+1
final_mus = tf.while_loop(cond, body, [mus, features, adj, 0])[0]