我已经在Tensorflow中训练了RNN模型，并使用tf.train.Saver()保存了它，但是当我尝试恢复它时，它会很生气。我一直在寻找原因，但仍然找不到任何解决方案。在训练期间，我使用features/testing.fest来查找准确性，该准确性没有任何错误。但是当我在还原后尝试相同的操作时，会出现错误
RNN模型值也与我训练的相同。
这是predict.py的代码
import tensorflow as tf
import numpy as np
import pickle
from models import RecurNet_Models
from preprocess_data import *
timesteps = 36
input_size = 52
hidden_features = 512
lexicon_file = 'dataset.lexi'
saved_model_path = 'trained_model/model'
model = RecurNet_Models.Simple_RNN(hidden_features=hidden_features, no_classes=2, timesteps=timesteps)
x = tf.placeholder('float', [None, timesteps, input_size])
y = tf.placeholder('float', [None, 2])
def predict(x, model=None, saved_model_path=None, string=None, y=None):
prediction = model(x)
saver = tf.train.Saver()
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, saved_model_path)
print("Accuracy: {}".format(accuracy.eval({x: test_img, y: test_labels})))
with open(lexicon_file, 'rb') as f:
lexicon = pickle.load(f)
with open('features/testing.feat', 'rb') as f:
[string, label] = pickle.load(f)
string = np.reshape(string, [-1, timesteps, input_size])
predict(x, model=model.model, saved_model_path=saved_model_path, string=string, y=label)
这是错误：
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1322, in _do_call
return fn(*args)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1307, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1409, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [564,2048] rhs shape= [104,208]
[[Node: save/Assign_3 = Assign[T=DT_FLOAT, _class=["loc:@rnn/basic_lstm_cell/kernel"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](rnn/basic_lstm_cell/kernel, save/RestoreV2:3)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "predict.py", line 42, in <module>
predict(x, model=model.model, saved_model_path=saved_model_path, string=string, y=label)
File "predict.py", line 29, in predict
saver.restore(sess, saved_model_path)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py", line 1752, in restore
{self.saver_def.filename_tensor_name: save_path})
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 900, in run
run_metadata_ptr)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1135, in _run
feed_dict_tensor, options, run_metadata)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1316, in _do_run
run_metadata)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1335, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [564,2048] rhs shape= [104,208]
[[Node: save/Assign_3 = Assign[T=DT_FLOAT, _class=["loc:@rnn/basic_lstm_cell/kernel"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](rnn/basic_lstm_cell/kernel, save/RestoreV2:3)]]
Caused by op 'save/Assign_3', defined at:
File "predict.py", line 42, in <module>
predict(x, model=model.model, saved_model_path=saved_model_path, string=string, y=label)
File "predict.py", line 21, in predict
saver = tf.train.Saver()
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py", line 1284, in __init__
self.build()
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py", line 1296, in build
self._build(self._filename, build_save=True, build_restore=True)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py", line 1333, in _build
build_save=build_save, build_restore=build_restore)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py", line 781, in _build_internal
restore_sequentially, reshape)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py", line 422, in _AddRestoreOps
assign_ops.append(saveable.restore(saveable_tensors, shapes))
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py", line 113, in restore
self.op.get_shape().is_fully_defined())
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/state_ops.py", line 219, in assign
validate_shape=validate_shape)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_state_ops.py", line 60, in assign
use_locking=use_locking, name=name)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 3414, in create_op
op_def=op_def)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 1740, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs shape= [564,2048] rhs shape= [104,208]
[[Node: save/Assign_3 = Assign[T=DT_FLOAT, _class=["loc:@rnn/basic_lstm_cell/kernel"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](rnn/basic_lstm_cell/kernel, save/RestoreV2:3)]]
我正在Google Colab中训练模型，测试相同代码时出现的错误可以解决这个问题，希望对您有帮助
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-41-4c07d32efced> in <module>()
41
42 string = np.reshape(string, [-1, timesteps, input_size])
---> 43 predict(x, model=model.model, saved_model_path=saved_model_path, string=string, y=label)
<ipython-input-41-4c07d32efced> in predict(x, model, saved_model_path, string, y)
18 def predict(x, model=None, saved_model_path=None, string=None, y=None):
19 tf.reset_default_graph()
---> 20 prediction = model(x)
21
22 saver = tf.train.Saver()
/content/sentiment/models/RecurNet_Models.py in model(self, x)
15
16 lstm_instance = rnn.BasicLSTMCell(self.hidden_features)
---> 17 rnn_output, states = rnn.static_rnn(lstm_instance, x, dtype=tf.float32)
18
19 output = tf.add(tf.matmul(rnn_output[-1], weights), biases)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/rnn.py in static_rnn(cell, inputs, initial_state, dtype, sequence_length, scope)
1313 state_size=cell.state_size)
1314 else:
-> 1315 (output, state) = call_cell()
1316
1317 outputs.append(output)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/rnn.py in <lambda>()
1300 varscope.reuse_variables()
1301 # pylint: disable=cell-var-from-loop
-> 1302 call_cell = lambda: cell(input_, state)
1303 # pylint: enable=cell-var-from-loop
1304 if sequence_length is not None:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/rnn_cell_impl.py in __call__(self, inputs, state, scope, *args, **kwargs)
337 # method. See the class docstring for more details.
338 return base_layer.Layer.__call__(self, inputs, state, scope=scope,
--> 339 *args, **kwargs)
340
341
/usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/base.py in __call__(self, inputs, *args, **kwargs)
327
328 # Actually call layer
--> 329 outputs = super(Layer, self).__call__(inputs, *args, **kwargs)
330
331 if not context.executing_eagerly():
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
686
687 if not in_deferred_mode:
--> 688 outputs = self.call(inputs, *args, **kwargs)
689 if outputs is None:
690 raise ValueError('A layer\'s `call` method should return a Tensor '
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/rnn_cell_impl.py in call(self, inputs, state)
636
637 gate_inputs = math_ops.matmul(
--> 638 array_ops.concat([inputs, h], 1), self._kernel)
639 gate_inputs = nn_ops.bias_add(gate_inputs, self._bias)
640
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py in matmul(a, b, transpose_a, transpose_b, adjoint_a, adjoint_b, a_is_sparse, b_is_sparse, name)
1944 are both set to True.
1945 """
-> 1946 with ops.name_scope(name, "MatMul", [a, b]) as name:
1947 if transpose_a and adjoint_a:
1948 raise ValueError("Only one of transpose_a and adjoint_a can be True.")
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in __enter__(self)
6001 if self._values is None:
6002 self._values = []
-> 6003 g = _get_graph_from_inputs(self._values)
6004 self._g_manager = g.as_default()
6005 self._g_manager.__enter__()
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in _get_graph_from_inputs(op_input_list, graph)
5661 graph = graph_element.graph
5662 elif original_graph_element is not None:
-> 5663 _assert_same_graph(original_graph_element, graph_element)
5664 elif graph_element.graph is not graph:
5665 raise ValueError("%s is not from the passed-in graph." % graph_element)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in _assert_same_graph(original_item, item)
5597 if original_item.graph is not item.graph:
5598 raise ValueError("%s must be from the same graph as %s." % (item,
-> 5599 original_item))
5600
5601
ValueError: Tensor("rnn/basic_lstm_cell/kernel:0", shape=(563, 2048), dtype=float32_ref) must be from the same graph as Tensor("concat:0", shape=(?, 563), dtype=float32).