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blei-lab
index out of range error with tensorflow r1.8
Versions: 'edward': '1.3.5', 'tensorflow': '1.8.0'
Trying to run example http://nbviewer.jupyter.org/github/blei-lab/edward/blob/master/notebooks/tensorboard.ipynb
The statement
inference.run(n_samples=5, n_iter=250, logdir='log/n_samples_5')
results in
/usr/local/lib/python3.6/site-packages/edward-1.3.5-py3.6.egg/edward/util/random_variables.py:54: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-5-57315b7843b6> in <module>()
1 inference = ed.KLqp({w: qw, b: qb}, data={X: X_train, y: y_train})
----> 2 inference.run(n_samples=5, n_iter=250, logdir='log/n_samples_5')
/usr/local/lib/python3.6/site-packages/edward-1.3.5-py3.6.egg/edward/inferences/inference.py in run(self, variables, use_coordinator, *args, **kwargs)
123 Passed into `initialize`.
124 """
--> 125 self.initialize(*args, **kwargs)
126
127 if variables is None:
/usr/local/lib/python3.6/site-packages/edward-1.3.5-py3.6.egg/edward/inferences/klqp.py in initialize(self, n_samples, kl_scaling, *args, **kwargs)
111 self.n_samples = n_samples
112 self.kl_scaling = kl_scaling
--> 113 return super(KLqp, self).initialize(*args, **kwargs)
114
115 def build_loss_and_gradients(self, var_list):
/usr/local/lib/python3.6/site-packages/edward-1.3.5-py3.6.egg/edward/inferences/variational_inference.py in initialize(self, optimizer, var_list, use_prettytensor, global_step, *args, **kwargs)
66 var_list = list(var_list)
67
---> 68 self.loss, grads_and_vars = self.build_loss_and_gradients(var_list)
69
70 if self.logging:
/usr/local/lib/python3.6/site-packages/edward-1.3.5-py3.6.egg/edward/inferences/klqp.py in build_loss_and_gradients(self, var_list)
146 if is_reparameterizable:
147 if is_analytic_kl:
--> 148 return build_reparam_kl_loss_and_gradients(self, var_list)
149 # elif is_analytic_entropy:
150 # return build_reparam_entropy_loss_and_gradients(self, var_list)
/usr/local/lib/python3.6/site-packages/edward-1.3.5-py3.6.egg/edward/inferences/klqp.py in build_reparam_kl_loss_and_gradients(inference, var_list)
738 for z, qz in six.iteritems(inference.latent_vars):
739 # Copy q(z) to obtain new set of posterior samples.
--> 740 qz_copy = copy(qz, scope=scope)
741 dict_swap[z] = qz_copy.value()
742
/usr/local/lib/python3.6/site-packages/edward-1.3.5-py3.6.egg/edward/util/random_variables.py in copy(org_instance, dict_swap, scope, replace_itself, copy_q, copy_parent_rvs)
292 else:
293 kwargs[key] = _copy_default(
--> 294 value, dict_swap, scope, True, copy_q, False)
295
296 kwargs['name'] = new_name
/usr/local/lib/python3.6/site-packages/edward-1.3.5-py3.6.egg/edward/util/random_variables.py in _copy_default(x, *args, **kwargs)
134 def _copy_default(x, *args, **kwargs):
135 if isinstance(x, (RandomVariable, tf.Operation, tf.Tensor, tf.Variable)):
--> 136 x = copy(x, *args, **kwargs)
137
138 return x
/usr/local/lib/python3.6/site-packages/edward-1.3.5-py3.6.egg/edward/util/random_variables.py in copy(org_instance, dict_swap, scope, replace_itself, copy_q, copy_parent_rvs)
316 # op. Therefore copy the op itself.
317 op = tensor.op
--> 318 new_op = copy(op, dict_swap, scope, True, copy_q, False)
319
320 output_index = op.outputs.index(tensor)
/usr/local/lib/python3.6/site-packages/edward-1.3.5-py3.6.egg/edward/util/random_variables.py in copy(org_instance, dict_swap, scope, replace_itself, copy_q, copy_parent_rvs)
369 [], # input types; will add them afterwards
370 original_op,
--> 371 op_def)
372
373 # advertise op early to break recursions
/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in __init__(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)
1730 # Refactor so we don't have to do this here.
1731 grouped_inputs = self._reconstruct_sequence_inputs(
-> 1732 op_def, inputs, node_def.attr)
1733 self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,
1734 control_input_ops)
/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in _reconstruct_sequence_inputs(self, op_def, inputs, attrs)
1801 grouped_inputs.append(inputs[i:i + input_len])
1802 else:
-> 1803 grouped_inputs.append(inputs[i])
1804 i += input_len
1805
IndexError: list index out of range
Current version of edward does not have compatibility with TensorFlow 1.8 release.
Run this (or set in the environment) before importing edward
import os
os.environ['TF_C_API_GRAPH_CONSTRUCTION']='0'
Evening after import os os.environ['TF_C_API_GRAPH_CONSTRUCTION']='0' running this before importing edward still I am facing IndexError: list index out of range.
Edward:1.3.5 TensorFlow:1.11.0
Can anyone help me in resolving the issue?
Not yet
On Wed, Feb 6, 2019, 5:37 AM Sonam [email protected] wrote:
Has anyone resolved this issue?
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Which version of keras is ok?
On 8/10/2019 13:01，diwu93[email protected] wrote：
TensorFlow==1.7.0 is OK!
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I get the solution as following: you should move back to the version 1.5.0 of Tensorflow using this command pip install tensorflow==1.5.0..if you can not get it..do the following: create new Env using command: conda create --name myenv conda activate myenv after that downgrade your python to python=3.6.5 then install TensorFlow using pip install tensorflow==1.5.0 then install Edward Good Luck