I just started (self) learning TensorFlow and I decided to follow the book "Learning TensorFlow" which I found in my local library.
Unfortunately in the book they are using TensorFlow 1.x, while I want to use the 2.4 version.
I have some troubles in replicating an example from Chapter 3. The point of the code is to create a new empty computing graph, create a node (i.e. in this case a constant) and then figure out whether the node belongs to the default graph or to the newly created one.
Here is the code from the book, which should work fine with TensorFlow1:
import tensorflow as tf
print(tf.get_default_graph())
g = tf.Graph() # This creates a new empty graph
a = tf.constant(5) # This creates a node
print(a.graph is g)
print(a.graph is tf.get_default_graph())
I did realize that the attribute get_default_graph() is no longer available in TensorFlow 2 and I substituted with tf.compat.v1.get_default_graph() but I still get the following error:
AttributeError: Tensor.graph is meaningless when eager execution is enabled.
Any help will be very much appreciated! Thanks in advance!
After import tensorflow need disable eager execution, like below:
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
print(tf.compat.v1.get_default_graph())
g = tf.Graph() # This creates a new empty graph
a = tf.constant(5) # This creates a node
print(a.graph is g)
print(a.graph is tf.compat.v1.get_default_graph())
Related
I'm just trying to mark subparts of a document as spans as per Spacy's documentation
import spacy
nlp = spacy.load('en_core_web_sm')
sentence = "The car with the white wheels was being confiscated by the police when the owner returns from robbing a bank"
doc = nlp(sentence)
doc.spans['remove_parts'] = [doc[2:6], doc[9:12]]
doc.spans['remove_parts']
This looks pretty straight forward, but Spacy returns the following error (and attributes it to the second line i.e. the assignment):
AttributeError: 'spacy.tokens.doc.Doc' object has no attribute 'spans'
I can't see what's going on at all. Is this a Spacy bug? Has spans property been removed even though it is still in the documentation? If not what am I missing?
PD: I'm using Colab for this. And spacy.info shows:
spaCy version 2.2.4
Location /usr/local/lib/python3.7/dist-packages/spacy
Platform Linux-4.19.112+-x86_64-with-Ubuntu-18.04-bionic
Python version 3.7.10
Models en
This code:
nlp = English()
text = "The car with the white wheels was being confiscated by the police when the owner returns from robbing a bank"
doc = nlp(text)
doc.spans['remove_parts'] = [doc[2:6], doc[9:12]]
doc.spans['remove_parts']
should work correctly from spaCy v3.0 onwards. If it doesn't - can you verify that you are in fact running the code from the correct virtual environment within colab (and not a different environment using spaCy v2)? We have previously seen issues where Colab would still be accessing older installations of spaCy on the system, instead of sourcing the code from the correct venv. To double check, you can try running the code in a Python console directly instead of through Colab.
I'm trying to visualize my model graph on TensorBoard. I'm using Keras 2.1.5 and tensorflow-gpu 1.13.1. My model is a concatenation of convolutional layers and, at the end, a custom layer where I make some operations with tensors.
Everything works fine, although I defined some prints at the end and at the beggining of my custom layer and then check that Python enters two time my code when I just call it once.
I'm running some trainings and check the model graph on TensorBoard and then found out something I haven't seen in any other example on the web:
Graph of custom layer
There is a connected graph of my custom layer (trans2img) and another one unconnected and with empty placeholders as inputs. I don't understand the reason.
Here is a simple example as my code:
def custom_layer(inputs):
with tf.name_scope('trans2img'):
a = inputs[0]
def some_operation(a):
with tf.name_scope('op1'):
b = 2*a
return b
def some_other_op(b, c):
with tf.name_scope('op2'):
d = b/c
return d
b = some_operation(a)
d = some_other_op(b, inputs[1])
return d
After that, in my network definition file, I load this custom layer as from custom_layer import custom_layer, and then use it as a Lambda layer:
net = Lambda(custom_layer)[branch1, branch2]
I don't know if it is because the way I define the inner operations in my custom_layer or the way I call them. I would like to know how to interpret this second unconnected graph I get and if it's an indicator of unnineficientcode. I would appreciate any clue and help.
This issue happens when you are using multiple tf scope.
For each scope it creates a new op with "op_{integer}".
You need to make use of "absolute_name_scope(scope)" to resolve your issue.
Please refer to below link on how to make use of it.
https://github.com/tensorflow/tensorflow/issues/9545
https://github.com/tensorflow/tensorflow/pull/23250/commits/1169eaca048b660905fc5776e617cc824a19b125
I'm building a CNN text classifier using TensorFlow which I want to load in tensorflow-serving and query using the serving apis. When I call the Predict() method on the grcp stub I receive this error: AttributeError: 'grpc._cython.cygrpc.Channel' object has no attribute 'unary_unary'
What I've done to date:
I have successfully trained and exported a model suitable for serving (i.e., the signatures are verified and using tf.Saver I can successfully return a prediction). I can also load the model in tensorflow_model_server without error.
Here is a snippet of the client code (simplified for readability):
with tf.Session() as sess:
host = FLAGS.server
channel = grpc.insecure_channel('localhost:9001')
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
request = predict_pb2.PredictRequest()
request.model_spec.name = 'predict_text'
request.model_spec.signature_name = 'predict_text'
x_text = ["space"]
# restore vocab processor
# then create a ndarray with transform_fit using the vocabulary
vocab = learn.preprocessing.VocabularyProcessor.restore('/some_path/model_export/1/assets/vocab')
x = np.array(list(vocab.fit_transform(x_text)))
# data
temp_data = tf.contrib.util.make_tensor_proto(x, shape=[1, 15], verify_shape=True)
request.inputs['input'].CopyFrom(tf.contrib.util.make_tensor_proto(x, shape=[1, 15], verify_shape=True))
# get classification prediction
result = stub.Predict(request, 5.0)
Where I'm bending the rules: I am using tensorflow-serving-apis in Python 3.5.3 when pip install is not officially supported. Various posts (example: https://github.com/tensorflow/serving/issues/581) have reported that using tensorflow-serving with Python 3 has been successful. I have downloaded tensorflow-serving-apis package from pypi (https://pypi.python.org/pypi/tensorflow-serving-api/1.5.0)and manually pasted into the environment.
Versions: tensorflow: 1.5.0, tensorflow-serving-apis: 1.5.0, grpcio: 1.9.0rc3, grcpio-tools: 1.9.0rcs, protobuf: 3.5.1 (all other dependency version have been verified but are not included for brevity -- happy to add if they have utility)
Environment: Linux Mint 17 Qiana; x64, Python 3.5.3
Investigations:
A github issue (https://github.com/GoogleCloudPlatform/google-cloud-python/issues/2258) indicated that a historical package triggered this error was related to grpc beta.
What data or learning or implementation am I missing?
beta_create_PredictionService_stub() is deprecated. Try this:
from tensorflow_serving.apis import prediction_service_pb2_grpc
...
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
Try to use grpc.beta.implementations.insecure_channel instead of grpc.insecure_channel.
See example code here.
I have some experience with writing machine learning programs in python, but I'm new to TensorFlow and am checking it out. My dev environment is a lubuntu 14.04 64-bit virtual machine. I've created a python 3.5 conda environment from miniconda and installed TensorFlow 0.12 and its dependencies. I began trying to run some example code from TensorFlow's tutorials and encountered this warning when calling fit() in the boston.py example for input functions: source.
WARNING:tensorflow:Rank of input Tensor (1) should be the same as
output_rank (2) for column. Will attempt to expand dims. It is highly
recommended that you resize your input, as this behavior may change.
After some searching in Google, I found other people encountered this same warning:
https://github.com/tensorflow/tensorflow/issues/6184
https://github.com/tensorflow/tensorflow/issues/5098
Tensorflow - Boston Housing Data Tutorial Errors
However, they also experienced errors which prevent code execution from completing. In my case, the code executes with the above warning. Unfortunately, I couldn't find a single answer in those links regarding what caused the warning and how to fix the warning. They all focused on the error. How does one remove the warning? Or is the warning safe to ignore?
Cheers!
Extra info, I also see the following warnings when running the aforementioned boston.py example.
WARNING:tensorflow:*******************************************************
WARNING:tensorflow:TensorFlow's V1 checkpoint format has been
deprecated. WARNING:tensorflow:Consider switching to the more
efficient V2 format: WARNING:tensorflow:
'tf.train.Saver(write_version=tf.train.SaverDef.V2)'
WARNING:tensorflow:now on by default.
WARNING:tensorflow:*******************************************************
and
WARNING:tensorflow:From
/home/kade/miniconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py:1053
in predict.: calling BaseEstimator.predict (from
tensorflow.contrib.learn.python.learn.estimators.estimator) with x is
deprecated and will be removed after 2016-12-01. Instructions for
updating: Estimator is decoupled from Scikit Learn interface by moving
into separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion: est = Estimator(...) -> est =
SKCompat(Estimator(...))
UPDATE (2016-12-22):
I've tracked the warning to this file:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/feature_column_ops.py
and this code block:
except NotImplementedError:
with variable_scope.variable_scope(
None,
default_name=column.name,
values=columns_to_tensors.values()):
tensor = column._to_dense_tensor(transformed_tensor)
tensor = fc._reshape_real_valued_tensor(tensor, 2, column.name)
variable = [
contrib_variables.model_variable(
name='weight',
shape=[tensor.get_shape()[1], num_outputs],
initializer=init_ops.zeros_initializer(),
trainable=trainable,
collections=weight_collections)
]
predictions = math_ops.matmul(tensor, variable[0], name='matmul')
Note the line: tensor = fc._reshape_real_valued_tensor(tensor, 2, column.name)
The method signature is: _reshape_real_valued_tensor(input_tensor, output_rank, column_name=None)
The value 2 is hardcoded as the value of output_rank, but the boston.py example is passing in an input_tensor of rank 1. I will continue to investigate.
If you specify the shape of your tensor explicitly:
tf.constant(df[k].values, shape=[df[k].size, 1])
the warning should go away.
After I specify the shape of the tensor explicitly.
continuous_cols = {k: tf.constant(df[k].values, shape=[df[k].size, 1]) for k in CONTINUOUS_COLUMNS}
It works!
I am following a tutorial to create convolution neural network with Theano. Although, I got a problem in a piece of code:
>> x = theano.floatX.xmatrix(theano.config.floatX) # rasterized images
AttributeError: 'module' object has no attribute 'floatX'
I loaded floatX with:
>> from theano import config
and checked with:
>> print(theano.config.floatX)
float 32
But still cannot load the module xmatrix, which should be in theano.config.floatX, judging from documentation. Does somebody know where can I find it?
Thank you in advance!
This sections of the convnet tutorial has a bug or is very outdated. Symbolic variables in Theano are located in theano.tensor package. This package theano.floatX even doesn't exist!
The current version in the tutorial github repository works fine. They allocate the symbolic variable the right way:
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
Browsing the tutorial repository I found the revision where this bug was corrected.
They seem to have forgotten to update the tutorial text with this fix.