When I run this code, why do I get the warning: "saver not created?"
sentences=['this is one', 'this is two', 'and this is three']
import tensorflow as tf
import tensorflow_hub as hub
url = "https://tfhub.dev/google/elmo/2"
embed = hub.Module(url)
embeddings = embed(sentences, signature="default", as_dict=True)["default"]
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
I do not want to save anything. Can't I test the model without saving?
This is just a "INFO" and not "Warning". Simply ignore it and it makes no difference.
Related
I have this code which based on t5 notebook (https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/master/notebooks/t5-trivia.ipynb)
FINETUNE_STEPS = 3000##param {type: "integer"}
model.finetune(
mixture_or_task_name="text_diacritization_short",
pretrained_model_dir=PRETRAINED_DIR,
finetune_steps=FINETUNE_STEPS
)
my code was working fine in 8 Augustus then something happened resulting of this error.
these two lines appeared when my model worked so i don't think they are the problem.
INFO:root:system_path_file_exists:gs://my_bucket/my_file/models/small/operative_config.gin
ERROR:root:Path not found: gs://my_bucket/my_file/models/small/operative_config.gin
Rest of the error.
From /usr/local/lib/python3.7/dist-packages/tensorflow/python/training/training_util.py:399: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
WARNING:absl:Using an uncached FunctionDataset for training is not recommended since it often results in insufficient shuffling on restarts, resulting in overfitting. It is highly recommended that you cache this task before training with it or use a data source that supports lower-level shuffling (e.g., FileDataSource).
SimdMeshImpl ignoring devices ['', '', '', '', '', '', '', '']
Using default tf glorot_uniform_initializer for variable encoder/block_000/layer_000/SelfAttention/relative_attention_bias The initialzer will guess the input and output dimensions based on dimension order.
Using default tf glorot_uniform_initializer for variable decoder/block_000/layer_000/SelfAttention/relative_attention_bias The initialzer will guess the input and output dimensions based on dimension order.
From /usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saver.py:1161: get_checkpoint_mtimes (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file utilities to get mtimes.
From /usr/local/lib/python3.7/dist-packages/tensorflow_estimator/python/estimator/tpu/tpu_estimator.py:758: Variable.load (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Prefer Variable.assign which has equivalent behavior in 2.X.
I changed the google cloud account and the Colab notebook to completely new gmail account, I think the problem is that something got updated in google Colab regarding connecting to Google Cloud TPUs.
Also, I can connect to my bucket normally using this code.
BASE_DIR = "gs://my_bucket/my_file" ##param { type: "string" }
if not BASE_DIR or BASE_DIR == "gs://":
raise ValueError("You must enter a BASE_DIR.")
DATA_DIR = os.path.join(BASE_DIR, "data")
FINETUNE_MODELS_DIR = os.path.join(BASE_DIR, "models")
ON_CLOUD = True
if ON_CLOUD:
print("Setting up GCS access...")
import tensorflow_gcs_config
from google.colab import auth
# Set credentials for GCS reading/writing from Colab and TPU.
TPU_TOPOLOGY = "v2-8"
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver() # TPU detection
TPU_ADDRESS = tpu.get_master()
print('Running on TPU:', TPU_ADDRESS)
except ValueError:
raise BaseException('ERROR: Not connected to a TPU runtime; please see the previous cell in this notebook for instructions!')
auth.authenticate_user()
tf.enable_eager_execution()
tf.config.experimental_connect_to_host(TPU_ADDRESS)
tensorflow_gcs_config.configure_gcs_from_colab_auth()
tf.disable_v2_behavior()
# Improve logging.
from contextlib import contextmanager
import logging as py_logging
if ON_CLOUD:
tf.get_logger().propagate = False
py_logging.root.setLevel('INFO')
#contextmanager
def tf_verbosity_level(level):
og_level = tf.logging.get_verbosity()
tf.logging.set_verbosity(level)
yield
tf.logging.set_verbosity(og_level)
it would be great if someone can help me I have been looking in the issue for a week and found nothing, is there any changes to how Google Colab works that I am not aware of.
Thanks in advance.
I have customized NER pipeline with following procedure
doc = nlp("I am going to Vallila. I am going to Sörnäinen.")
for ent in doc.ents:
print(ent.text, ent.label_)
LABEL = 'DISTRICT'
TRAIN_DATA = [
(
'We need to deliver it to Vallila', {
'entities': [(25, 32, 'DISTRICT')]
}),
(
'We need to deliver it to somewhere', {
'entities': []
}),
]
ner = nlp.get_pipe("ner")
ner.add_label(LABEL)
nlp.disable_pipes("tagger")
nlp.disable_pipes("parser")
nlp.disable_pipes("attribute_ruler")
nlp.disable_pipes("lemmatizer")
nlp.disable_pipes("tok2vec")
optimizer = nlp.get_pipe("ner").create_optimizer()
import random
from spacy.training import Example
for i in range(25):
random.shuffle(TRAIN_DATA)
for text, annotation in TRAIN_DATA:
example = Example.from_dict(nlp.make_doc(text), annotation)
nlp.update([example], sgd=optimizer)
I tried to save that customized NER to disk and load it again with following code
ner.to_disk('/home/feru/ner')
import spacy
from spacy.pipeline import EntityRecognizer
nlp = spacy.load("en_core_web_lg", disable=['ner'])
ner = EntityRecognizer(nlp.vocab)
ner.from_disk('/home/feru/ner')
nlp.add_pipe(ner)
I got however following error:
---> 10 ner = EntityRecognizer(nlp.vocab)
11 ner.from_disk('/home/feru/ner')
12 nlp.add_pipe(ner)
~/.local/lib/python3.8/site-packages/spacy/pipeline/ner.pyx in
spacy.pipeline.ner.EntityRecognizer.init()
TypeError: init() takes at least 2 positional arguments (1 given)
This method to save and load custom component from disk seems to be from some erly SpaCy version. What's the second argument EntityRecognizer needs?
The general process you are following of serializing a single component and reloading it is not the recommended way to do this in spaCy. You can do it - it has to be done internally, of course - but you generally want to save and load pipelines using high-level wrappers. In this case this means that you would save like this:
nlp.to_disk("my_model") # NOT ner.to_disk
And then load it with spacy.load("my_model").
You can find more detail about this in the saving and loading docs. Since it seems you're just getting started with spaCy, you might want to go through the course too. It covers the new config-based training in v3, which is much easier than using your own custom training loop like in your code sample.
If you want to mix and match components from different pipelines, you still will generally want to save entire pipelines, and you can then combine components from them using the "sourcing" feature.
I am using tensorflow-gpu==1.10.0 and keras from tensorflow as tf.keras.
I am trying to use source code written by someone else to implement it on my network.
I saved my network using save_model and load it using load_model. when I use model.get_config(), I expect a dictionary, but i"m getting a list. Keras source documentation also says that get_config returns a dictionary (https://keras.io/models/about-keras-models/).
I tried to check if it has to do with saving type : save_model or model.save that makes the difference in how it is saved, but both give me this error:
TypeError: list indices must be integers or slices, not str
my code block :
model_config = self.keras_model.get_config()
for layer in model_config['layers']:
name = layer['name']
if name in update_layers:
layer['config']['filters'] = update_layers[name]['filters']
my pip freeze :
absl-py==0.6.1
astor==0.7.1
bitstring==3.1.5
coverage==4.5.1
cycler==0.10.0
decorator==4.3.0
Django==2.1.3
easydict==1.7
enum34==1.1.6
futures==3.1.1
gast==0.2.0
geopy==1.11.0
grpcio==1.16.1
h5py==2.7.1
image==1.5.15
ImageHash==3.7
imageio==2.5.0
imgaug==0.2.5
Keras==2.1.3
kiwisolver==1.1.0
lxml==4.1.1
Markdown==3.0.1
matplotlib==2.1.0
networkx==2.2
nose==1.3.7
numpy==1.14.1
olefile==0.46
opencv-python==3.3.0.10
pandas==0.20.3
Pillow==4.2.1
prometheus-client==0.4.2
protobuf==3.6.1
pyparsing==2.3.0
pyquaternion==0.9.2
python-dateutil==2.7.5
pytz==2018.7
PyWavelets==1.0.1
PyYAML==3.12
Rtree==0.8.3
scikit-image==0.13.1
scikit-learn==0.19.1
scipy==0.19.1
Shapely==1.6.4.post1
six==1.11.0
sk-video==1.1.8
sklearn-porter==0.6.2
tensorboard==1.10.0
tensorflow-gpu==1.10.0
termcolor==1.1.0
tqdm==4.19.4
utm==0.4.2
vtk==8.1.0
Werkzeug==0.14.1
xlrd==1.1.0
xmltodict==0.11.0
I copied the code from tfjs-examples/mobilenet/ and try to run my own frozen model, the model was loaded, but producing error when I try to use predict method.
I'm using tfjs of version 0.14.2 and Google Chrome, version 71.0.3578.98
I used the mobilenet example shown in tfjs-examples repo and started the server by yarn watch.
Secondly, I loaded my own FrozenModel successfully.
But when I use the predict method of the loaded model with a input of correct shape, it shown the error below:
ERROR: 0:163: 'updates' : left of '[' is not of type array, matrix, or vecto
I just slightly modified the original index.js in the mobilenet example and the script look like this:
import * as tf from '#tensorflow/tfjs';
const MODEL_URL = 'path_to_tensorflowjs_model.pb';
const WEIGHTS_URL = 'path_to_weights_manifest.json';
let gan;
const ganDemo = async () => {
status('Loading model...');
gan = await tf.loadFrozenModel(MODEL_URL, WEIGHTS_URL);
gan.predict(tf.zeros([1, 3, 450, 300])).dispose(); # error here
...
I had made sure the model was loaded successfully, and the shape of the input is correct (I intentionally tried other shape, and if the shape is not correct, it will throw another error)
Any suggestions is appreciated.
What is the version of tfjs npm you are using?
Can you try to use the latest version v1.0.0-alpha2 or v0.15.1?
There is a bug fix related to sparseToDense op.
I'm working on my first deep learning model using TensorFlow in a Jupyter notebook, and I would like to generate simplified graphs which illustrate the various layers of the network. Specifically, graphs such as those pictured in this answer:
This is very simple and clean and I can understand what's going on. This is more important than capturing 100% of the details. Contrast with the graph generated by TensorBoard which is a complete fustercluck:
How can I take a tf.Graph object and automatically generate a graph similar to the one above? Bonus points if it can be displayed in the Jupyter Notebook, too.
In short - you cannot. TF is a low-level library, which has no concept of "high level operations", it has ops, and this is the only thing it can visualise in a way you are thinking about. In particular, from math perspective there are no "neurons" in your graph, there are just tensors being multiplied by each other, this additional "semantics" is there only to make it easier for humans to talk about this, but it is not really encoded in your graph.
What you can do is to group nodes by yourself by specifing variable_scope for sections of your graph, then, after displaying in TB they will be displayed as a single node. It will not give you this "per-neuron-like" flavour of visualisation but at least it will hide many details. Creating a nice, visually appealing visualisations of neural nets is an "art" on its own rights, and a hard task to do in general.
Here's a snippet of code that we use in our PipelineAI notebooks to display our TensorFlow graphs inline within our Jupyter notebooks:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
from google.protobuf import text_format
from tensorflow.core.framework import graph_pb2
def convert_graph_to_dot(input_graph, output_dot, is_input_graph_binary):
graph = graph_pb2.GraphDef()
with open(input_graph, "rb") as fh:
if is_input_graph_binary:
graph.ParseFromString(fh.read())
else:
text_format.Merge(fh.read(), graph)
with open(output_dot, "wt") as fh:
print("digraph graphname {", file=fh)
for node in graph.node:
output_name = node.name
print(" \"" + output_name + "\" [label=\"" + node.op + "\"];", file=fh)
for input_full_name in node.input:
parts = input_full_name.split(":")
input_name = re.sub(r"^\^", "", parts[0])
print(" \"" + input_name + "\" -> \"" + output_name + "\";", file=fh)
print("}", file=fh)
print("Created dot file '%s' for graph '%s'." % (output_dot, input_graph))
input_graph='/root/models/optimize_me/linear/cpu/unoptimized_cpu.pb'
output_dot='/root/notebooks/unoptimized_cpu.dot'
convert_graph_to_dot(input_graph=input_graph, output_dot=output_dot, is_input_graph_binary=True)
Using graphviz, you can convert the .dot to .png using a %%bash magic within your notebook cell:
%%bash
dot -T png /root/notebooks/unoptimized_cpu.dot \
-o /root/notebooks/unoptimized_cpu.png > /tmp/a.out
and finally, display the graph in your notebook:
from IPython.display import Image
Image('/root/notebooks/unoptimized_cpu.png', width=1024, height=768)
here's an example of a simple Linear Regression model implemented in TensorFlow:
Here's the optimized version used to deploy and serve the TensorFlow Model in production (also rendered using the above code snippets):
More examples and details of these types of optimizations at http://pipeline.ai