Related
Is anyone else facing an issue while running the Prophet model after the latest v1.1.2 release?
I get the following error when executing model.fit():
RuntimeError Traceback (most recent call last)
<ipython-input-86-1e4ae74985f6> in <module>
----> 1 model_training(top_5_aro, X_trainARO, X_validARO, df_ARO, predict_index)
<ipython-input-85-9ee9b229fcde> in model_training(list_accounts, df_train, df_validation, df_original, predict_index)
98 model = Prophet()
99 # fit the model
--> 100 model.fit(train_data3)
101
102 # use the model to make a forecast
/opt/app-root/lib64/python3.8/site-packages/prophet/forecaster.py in fit(self, df, **kwargs)
1179 self.params = self.stan_backend.sampling(stan_init, dat, self.mcmc_samples, **kwargs)
1180 else:
-> 1181 self.params = self.stan_backend.fit(stan_init, dat, **kwargs)
1182
1183 self.stan_fit = self.stan_backend.stan_fit
/opt/app-root/lib64/python3.8/site-packages/prophet/models.py in fit(self, stan_init, stan_data, **kwargs)
98 # Fall back on Newton
99 if not self.newton_fallback or args['algorithm'] == 'Newton':
--> 100 raise e
101 logger.warning('Optimization terminated abnormally. Falling back to Newton.')
102 args['algorithm'] = 'Newton'
/opt/app-root/lib64/python3.8/site-packages/prophet/models.py in fit(self, stan_init, stan_data, **kwargs)
94
95 try:
---> 96 self.stan_fit = self.model.optimize(**args)
97 except RuntimeError as e:
98 # Fall back on Newton
/opt/app-root/lib64/python3.8/site-packages/cmdstanpy/model.py in optimize(self, data, seed, inits, output_dir, sig_figs, save_profile, algorithm, init_alpha, tol_obj, tol_rel_obj, tol_grad, tol_rel_grad, tol_param, history_size, iter, save_iterations, require_converged, show_console, refresh, time_fmt, timeout)
736 get_logger().warning(msg)
737 else:
--> 738 raise RuntimeError(msg)
739 mle = CmdStanMLE(runset)
740 return mle
RuntimeError: Error during optimization! Command '/opt/app-root/lib/python3.8/site-packages/prophet/stan_model/prophet_model.bin random seed=97108 data file=/tmp/tmplilnrs7m/fk4f5y6m.json init=/tmp/tmplilnrs7m/q_6vjmch.json output file=/tmp/tmplilnrs7m/prophet_model1ny0mums/prophet_model-20230125181922.csv method=optimize algorithm=newton iter=10000' failed
I did not have this issue in the previous version. Any help appreciated!
Python version: 3.8.3
Prophet version: 1.1.2
When using the earlier version I was successfully able to train the model and view the model predictions. After upgrading to the latest v1.1.2 of prophet, I face this error when training the model.
I am using a custom-defined metric in SKlearn's KNeighborsClassifier. Here's my code:
def chi_squared(x,y):
return np.divide(np.square(np.subtract(x,y)), np.sum(x,y))
Above function implementation of chi squared distance function. I have used NumPy functions because according to scikit-learn docs, metric function takes two one-dimensional numpy arrays.
I have passed the chi_squared function as an argument to KNeighborsClassifier().
knn = KNeighborsClassifier(algorithm='ball_tree', metric=chi_squared)
However, I keep getting following error:
TypeError Traceback (most recent call last)
<ipython-input-29-d2a365ebb538> in <module>
4
5 knn = KNeighborsClassifier(algorithm='ball_tree', metric=chi_squared)
----> 6 knn.fit(X_train, Y_train)
7 predictions = knn.predict(X_test)
8 print(accuracy_score(Y_test, predictions))
~/.local/lib/python3.8/site-packages/sklearn/neighbors/_classification.py in fit(self, X, y)
177 The fitted k-nearest neighbors classifier.
178 """
--> 179 return self._fit(X, y)
180
181 def predict(self, X):
~/.local/lib/python3.8/site-packages/sklearn/neighbors/_base.py in _fit(self, X, y)
497
498 if self._fit_method == 'ball_tree':
--> 499 self._tree = BallTree(X, self.leaf_size,
500 metric=self.effective_metric_,
501 **self.effective_metric_params_)
sklearn/neighbors/_binary_tree.pxi in sklearn.neighbors._ball_tree.BinaryTree.__init__()
sklearn/neighbors/_binary_tree.pxi in sklearn.neighbors._ball_tree.BinaryTree._recursive_build()
sklearn/neighbors/_ball_tree.pyx in sklearn.neighbors._ball_tree.init_node()
sklearn/neighbors/_binary_tree.pxi in sklearn.neighbors._ball_tree.BinaryTree.rdist()
sklearn/neighbors/_dist_metrics.pyx in sklearn.neighbors._dist_metrics.DistanceMetric.rdist()
sklearn/neighbors/_dist_metrics.pyx in sklearn.neighbors._dist_metrics.PyFuncDistance.dist()
sklearn/neighbors/_dist_metrics.pyx in sklearn.neighbors._dist_metrics.PyFuncDistance._dist()
<ipython-input-29-d2a365ebb538> in chi_squared(x, y)
1 def chi_squared(x,y):
----> 2 return np.divide(np.square(np.subtract(x,y)), np.sum(x,y))
3
4
5 knn = KNeighborsClassifier(algorithm='ball_tree', metric=chi_squared)
<__array_function__ internals> in sum(*args, **kwargs)
~/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py in sum(a, axis, dtype, out, keepdims, initial, where)
2239 return res
2240
-> 2241 return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims,
2242 initial=initial, where=where)
2243
~/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs)
85 return reduction(axis=axis, out=out, **passkwargs)
86
---> 87 return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
88
89
TypeError: only integer scalar arrays can be converted to a scalar index
I can reproduce your error message with:
In [173]: x=np.arange(3); y=np.array([2,3,4])
In [174]: np.sum(x,y)
Traceback (most recent call last):
File "<ipython-input-174-1a1a267ebd82>", line 1, in <module>
np.sum(x,y)
File "<__array_function__ internals>", line 5, in sum
File "/usr/local/lib/python3.8/dist-packages/numpy/core/fromnumeric.py", line 2247, in sum
return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims,
File "/usr/local/lib/python3.8/dist-packages/numpy/core/fromnumeric.py", line 87, in _wrapreduction
return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
TypeError: only integer scalar arrays can be converted to a scalar index
Correct use(s) of np.sum:
In [175]: np.sum(x)
Out[175]: 3
In [177]: np.sum(np.arange(6).reshape(2,3), axis=0)
Out[177]: array([3, 5, 7])
In [178]: np.sum(np.arange(6).reshape(2,3), 0)
Out[178]: array([3, 5, 7])
(re)read the np.sum docs if necessary!
Using np.add instead of np.sum:
In [179]: np.add(x,y)
Out[179]: array([2, 4, 6])
In [180]: x+y
Out[180]: array([2, 4, 6])
The following should be equivalent:
np.divide(np.square(np.subtract(x,y)), np.add(x,y))
(x-y)**2/(x+y)
I am trying to load an existing MODFLOW-USG model with FloPy (Windows environment). The model has a Voronoi mesh, and this seems to trip the "load" function:
m1=flopy.modflow.Modflow.load(model_name+".nam",model_ws=model_dir,verbose=True,check=False,exe_name="mfusg.exe",version='mfusg')
I get the following error, which appears to relate to the fact that FloPy is expecting a structured grid with rows and columns:
TypeError Traceback (most recent call last)
<ipython-input-33-62420c415719> in <module>
6 head_file = os.path.join(model_dir,model_name+'.hds')
7 print(head_file)
----> 8 m1=flopy.modflow.Modflow.load(model_name+".nam",model_ws=model_dir,verbose=True,check=False,exe_name="mfusg.exe",version='mfusg')
9 headobj = bf.HeadUFile(head_file,verbose=True,text='HEADU')
10 headobj.list_records()
~\Anaconda3\lib\site-packages\flopy\modflow\mf.py in load(f, version, exe_name, verbose, model_ws, load_only, forgive, check)
797 item.package.load(item.filehandle, ml,
798 ext_unit_dict=ext_unit_dict,
--> 799 check=False)
800 else:
801 item.package.load(item.filehandle, ml,
~\Anaconda3\lib\site-packages\flopy\modflow\mfrch.py in load(f, model, nper, ext_unit_dict, check)
408 print(txt)
409 t = Util2d.load(f, model, (nrow, ncol), np.float32, 'rech',
--> 410 ext_unit_dict)
411 else:
412 parm_dict = {}
~\Anaconda3\lib\site-packages\flopy\utils\util_array.py in load(f_handle, model, shape, dtype, name, ext_unit_dict, array_free_format, array_format)
2699
2700 elif cr_dict['type'] == 'internal':
-> 2701 data = Util2d.load_txt(shape, f_handle, dtype, cr_dict['fmtin'])
2702 u2d = Util2d(model, shape, dtype, data, name=name,
2703 iprn=cr_dict['iprn'], fmtin="(FREE)",
~\Anaconda3\lib\site-packages\flopy\utils\util_array.py in load_txt(shape, file_in, dtype, fmtin)
2376 elif len(shape) == 2:
2377 nrow, ncol = shape
-> 2378 num_items = nrow * ncol
2379 else:
2380 raise ValueError(
TypeError: unsupported operand type(s) for *: 'NoneType' and 'int'
I could not find any documentation or Jupyter notebooks with examples of loading an existing model with Voronoi mesh, only creating new triangular meshes or structured / local-grid-refined grids.
Try the code with forgive = True.
m1=flopy.modflow.Modflow.load(model_name+".nam",model_ws=model_dir,verbose=True,check=False,exe_name="mfusg.exe",version='mfusg', forgive = True)
So I have a dataframe X which looks something like this:
X.head()
0 My wife took me here on my birthday for breakf...
1 I have no idea why some people give bad review...
3 Rosie, Dakota, and I LOVE Chaparral Dog Park!!...
4 General Manager Scott Petello is a good egg!!!...
6 Drop what you're doing and drive here. After I...
Name: text, dtype: object
And then,
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer()
X = cv.fit_transform(X)
But I get this error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-61-8ff79b91e317> in <module>()
----> 1 X = cv.fit_transform(X)
~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in fit_transform(self, raw_documents, y)
867
868 vocabulary, X = self._count_vocab(raw_documents,
--> 869 self.fixed_vocabulary_)
870
871 if self.binary:
~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in _count_vocab(self, raw_documents, fixed_vocab)
790 for doc in raw_documents:
791 feature_counter = {}
--> 792 for feature in analyze(doc):
793 try:
794 feature_idx = vocabulary[feature]
~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in <lambda>(doc)
264
265 return lambda doc: self._word_ngrams(
--> 266 tokenize(preprocess(self.decode(doc))), stop_words)
267
268 else:
~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in <lambda>(x)
230
231 if self.lowercase:
--> 232 return lambda x: strip_accents(x.lower())
233 else:
234 return strip_accents
~/anaconda3/lib/python3.6/site-packages/scipy/sparse/base.py in __getattr__(self, attr)
574 return self.getnnz()
575 else:
--> 576 raise AttributeError(attr + " not found")
577
578 def transpose(self, axes=None, copy=False):
AttributeError: lower not found
No idea why.
You need to specify the column name of the text data even if the dataframe has single column.
X_countMatrix = cv.fit_transform(X['text'])
Because a CountVectorizer expects an iterable as input and when you supply a dataframe as an argument, only thing thats iterated is the column names. So even if you did not have any errors, that would be incorrect. Lucky that you got an error and got a chance to correct it.
I am using a while loop to calculate a cost function for memory reasons. When calculating the gradient, tensorflow will store Nm tensors where Nm is the number of iterations in my while loop (this cuases the same memory issues I had with the original energy functions). I do not want that as I don't have enough memory. So I want to register a new op along with a gradient function that both use a while loop. However I am having issues with using function.defun and a while loop. To simplify things, I have a small test example below:
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.framework import function
def _run(tensor):
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
res = sess.run(tensor)
return res
#function.Defun(tf.float32,tf.float32,func_name ='tf_test_log')#,grad_func=tf_test_logGrad)
def tf_test_log(t_x,t_y):
#N = t_x.shape[0].value
condition = lambda i,m1: i<N
def body(index,x):
#return[(index+1),tf.concat([x, tf.expand_dims(tf.exp( tf.add( t_x[:,index],t_y[:,index]) ),1) ],1 ) ]
return[(index+1),tf.add(x, tf.exp( tf.add( t_x[:,0],t_y[:,0]) ) ) ]
i0 = tf.constant(0,dtype=tf.int32)
m0 = tf.zeros([N,1],dType)
ijk_0 = [i0,m0]
L,t_log_x = tf.while_loop(condition,body,ijk_0,
shape_invariants=[i0.get_shape(),
tf.TensorShape([N,None])]
)
return t_log_x
dType = tf.float32
N = np.int32(100)
t_N = tf.constant(N,dtype = tf.int32)
t_x = tf.constant(np.random.randn(N,N),dtype = dType)
t_y = tf.constant(np.random.randn(N,N),dtype = dType)
ys = _run(tf_test_log(t_x,t_y))
I then try to test the new op:
I get a Value error: The shape for while/Merge_1:0 is not an invariant for the loop. It enters the loop with shape (100, ?), but has shape after one iteration. Provide shape invariants using either the shape_invariants argument of tf.while_loop or set_shape() on the loop variables.
Note that calling
If i use a concatenate operation (instead of the add operation that gets returned by my while loop), I do not get any issues.
However, If I do not set N as a global variable (i.e. I do N = t_x.shape[0]) inside the body of the tf_test_log function, I get a Value error.
ValueError: Cannot convert a partially known TensorShape to a Tensor: (?, 1)
What is wrong with my code? Any help is greatly appreciated!
I am using python 3.5 on ubuntu 16.04 and tensorflow 1.4
full output:
ValueError Traceback (most recent call last)
~/Documents/TheEffingPhDHatersGonnaHate/PAM/defun_while.py in <module>()
51 t_x = tf.constant(np.random.randn(N,N),dtype = dType)
52 t_y = tf.constant(np.random.randn(N,N),dtype = dType)
---> 53 ys = _run(tf_test_log(t_x,t_y))
54
55
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/function.py in __call__(self, *args, **kwargs)
503
504 def __call__(self, *args, **kwargs):
--> 505 self.add_to_graph(ops.get_default_graph())
506 args = [ops.convert_to_tensor(_) for _ in args] + self._extra_inputs
507 ret, op = _call(self._signature, *args, **kwargs)
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/function.py in add_to_graph(self, g)
484 def add_to_graph(self, g):
485 """Adds this function into the graph g."""
--> 486 self._create_definition_if_needed()
487
488 # Adds this function into 'g'.
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/function.py in _create_definition_if_needed(self)
319 """Creates the function definition if it's not created yet."""
320 with context.graph_mode():
--> 321 self._create_definition_if_needed_impl()
322
323 def _create_definition_if_needed_impl(self):
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/function.py in _create_definition_if_needed_impl(self)
336 # Call func and gather the output tensors.
337 with vs.variable_scope("", custom_getter=temp_graph.getvar):
--> 338 outputs = self._func(*inputs)
339
340 # There is no way of distinguishing between a function not returning
~/Documents/TheEffingPhDHatersGonnaHate/PAM/defun_while.py in tf_test_log(t_x, t_y)
39 L,t_log_x = tf.while_loop(condition,body,ijk_0,
40 shape_invariants=[i0.get_shape(),
---> 41 tf.TensorShape([N,None])]
42 )
43 return t_log_x
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py in while_loop(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, name)
2814 loop_context = WhileContext(parallel_iterations, back_prop, swap_memory) # pylint: disable=redefined-outer-name
2815 ops.add_to_collection(ops.GraphKeys.WHILE_CONTEXT, loop_context)
-> 2816 result = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants)
2817 return result
2818
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py in BuildLoop(self, pred, body, loop_vars, shape_invariants)
2638 self.Enter()
2639 original_body_result, exit_vars = self._BuildLoop(
-> 2640 pred, body, original_loop_vars, loop_vars, shape_invariants)
2641 finally:
2642 self.Exit()
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py in _BuildLoop(self, pred, body, original_loop_vars, loop_vars, shape_invariants)
2619 for m_var, n_var in zip(merge_vars, next_vars):
2620 if isinstance(m_var, ops.Tensor):
-> 2621 _EnforceShapeInvariant(m_var, n_var)
2622
2623 # Exit the loop.
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py in _EnforceShapeInvariant(merge_var, next_var)
576 "Provide shape invariants using either the `shape_invariants` "
577 "argument of tf.while_loop or set_shape() on the loop variables."
--> 578 % (merge_var.name, m_shape, n_shape))
579 else:
580 if not isinstance(var, (ops.IndexedSlices, sparse_tensor.SparseTensor)):
ValueError: The shape for while/Merge_1:0 is not an invariant for the loop. It enters the loop with shape (100, ?), but has shape <unknown> after one iteration. Provide shape invariants using either the `shape_invariants` argument of tf.while_loop or set_shape() on the loop variables.
Thanks #Alexandre Passos for the suggestion in the comment above!
The following piece of code is a modification of the original with a set_shape function added inside the body.
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.framework import function
def _run(tensor):
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
res = sess.run(tensor)
return res
#function.Defun(tf.float32,tf.float32,tf.float32,func_name ='tf_test_logGrad')
def tf_test_logGrad(t_x,t_y,grad):
return grad
#function.Defun(tf.float32,tf.float32,func_name ='tf_test_log')#,grad_func=tf_test_logGrad)
def tf_test_log(t_x,t_y):
#N = t_x.shape[0].value
condition = lambda i,m1: i<N
def body(index,x):
#return[(index+1),tf.concat([x, tf.expand_dims(tf.exp( tf.add( t_x[:,index],t_y[:,index]) ),1) ],1 ) ]
x = tf.add(x, tf.exp( tf.add( t_x[:,0],t_y[:,0]) ) )
x.set_shape([N])
return[(index+1), x]
i0 = tf.constant(0,dtype=tf.int32)
m0 = tf.zeros([N],dType)
ijk_0 = [i0,m0]
L,t_log_x = tf.while_loop(condition,body,ijk_0,
shape_invariants=[i0.get_shape(),
tf.TensorShape([N])]
)
return t_log_x
dType = tf.float32
N = np.int32(100)
t_N = tf.constant(N,dtype = tf.int32)
t_x = tf.constant(np.random.randn(N,N),dtype = dType)
t_y = tf.constant(np.random.randn(N,N),dtype = dType)
ys = _run(tf_test_log(t_x,t_y))
The Issue of global N still persists.
You still need to set the shape of the loop tensors as a global variable outside of the defun decorator. If you try to get it from the shape of the inputs of the defun decorator, you get:
TypeError Traceback (most recent call last)
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py in zeros(shape, dtype, name)
1438 shape = tensor_shape.as_shape(shape)
-> 1439 output = constant(zero, shape=shape, dtype=dtype, name=name)
1440 except (TypeError, ValueError):
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name, verify_shape)
207 tensor_util.make_tensor_proto(
--> 208 value, dtype=dtype, shape=shape, verify_shape=verify_shape))
209 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape)
379 # exception when dtype is set to np.int64
--> 380 if shape is not None and np.prod(shape, dtype=np.int64) == 0:
381 nparray = np.empty(shape, dtype=np_dt)
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/numpy/core/fromnumeric.py in prod(a, axis, dtype, out, keepdims)
2517 return _methods._prod(a, axis=axis, dtype=dtype,
-> 2518 out=out, **kwargs)
2519
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/numpy/core/_methods.py in _prod(a, axis, dtype, out, keepdims)
34 def _prod(a, axis=None, dtype=None, out=None, keepdims=False):
---> 35 return umr_prod(a, axis, dtype, out, keepdims)
36
TypeError: __int__ returned non-int (type NoneType)
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
~/Documents/TheEffingPhDHatersGonnaHate/PAM/defun_while.py in <module>()
52 t_x = tf.constant(np.random.randn(N,N),dtype = dType)
53 t_y = tf.constant(np.random.randn(N,N),dtype = dType)
---> 54 ys = _run(tf_test_log(t_x,t_y))
55
56
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/function.py in __call__(self, *args, **kwargs)
503
504 def __call__(self, *args, **kwargs):
--> 505 self.add_to_graph(ops.get_default_graph())
506 args = [ops.convert_to_tensor(_) for _ in args] + self._extra_inputs
507 ret, op = _call(self._signature, *args, **kwargs)
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/function.py in add_to_graph(self, g)
484 def add_to_graph(self, g):
485 """Adds this function into the graph g."""
--> 486 self._create_definition_if_needed()
487
488 # Adds this function into 'g'.
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/function.py in _create_definition_if_needed(self)
319 """Creates the function definition if it's not created yet."""
320 with context.graph_mode():
--> 321 self._create_definition_if_needed_impl()
322
323 def _create_definition_if_needed_impl(self):
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/function.py in _create_definition_if_needed_impl(self)
336 # Call func and gather the output tensors.
337 with vs.variable_scope("", custom_getter=temp_graph.getvar):
--> 338 outputs = self._func(*inputs)
339
340 # There is no way of distinguishing between a function not returning
~/Documents/TheEffingPhDHatersGonnaHate/PAM/defun_while.py in tf_test_log(t_x, t_y)
33
34 i0 = tf.constant(0,dtype=tf.int32)
---> 35 m0 = tf.zeros([N],dType)
36
37
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py in zeros(shape, dtype, name)
1439 output = constant(zero, shape=shape, dtype=dtype, name=name)
1440 except (TypeError, ValueError):
-> 1441 shape = ops.convert_to_tensor(shape, dtype=dtypes.int32, name="shape")
1442 output = fill(shape, constant(zero, dtype=dtype), name=name)
1443 assert output.dtype.base_dtype == dtype
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in convert_to_tensor(value, dtype, name, preferred_dtype)
834 name=name,
835 preferred_dtype=preferred_dtype,
--> 836 as_ref=False)
837
838
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx)
924
925 if ret is None:
--> 926 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
927
928 if ret is NotImplemented:
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py in _tensor_shape_tensor_conversion_function(s, dtype, name, as_ref)
248 if not s.is_fully_defined():
249 raise ValueError(
--> 250 "Cannot convert a partially known TensorShape to a Tensor: %s" % s)
251 s_list = s.as_list()
252 int64_value = 0
ValueError: Cannot convert a partially known TensorShape to a Tensor: (?,)