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TensorFlow-Examples | TensorFlow Tutorial and Examples for Beginners | Machine Learning library

 by   aymericdamien Jupyter Notebook Version: Current License: Non-SPDX

 by   aymericdamien Jupyter Notebook Version: Current License: Non-SPDX

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kandi X-RAY | TensorFlow-Examples Summary

TensorFlow-Examples is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. TensorFlow-Examples has no bugs, it has no vulnerabilities and it has medium support. However TensorFlow-Examples has a Non-SPDX License. You can download it from GitHub.
This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanation, for both TF v1 & v2. It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as layers, estimator, dataset, ...).
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  • TensorFlow-Examples has a medium active ecosystem.
  • It has 41052 star(s) with 15081 fork(s). There are 2116 watchers for this library.
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  • There are 157 open issues and 68 have been closed. On average issues are closed in 65 days. There are 52 open pull requests and 0 closed requests.
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  • TensorFlow-Examples has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
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  • TensorFlow-Examples has a Non-SPDX License.
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  • TensorFlow-Examples releases are not available. You will need to build from source code and install.
  • Installation instructions, examples and code snippets are available.
  • It has 3878 lines of code, 98 functions and 65 files.
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TensorFlow-Examples Key Features

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

Installation

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git clone https://github.com/aymericdamien/TensorFlow-Examples

No module named 'tensorflow_examples' after installing

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!pip install -q git+https://github.com/tensorflow/examples.git

Unable to import tensorflow lite image classifier

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PYTHONPATH=path\to\models

Why the accuracy of TF-lite is not correct after quantization

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real_input_value = (quantized_input_value - mean_value) / std_dev_value
quantized_input_value = real_input_value * std_dev_value + mean_value
input_data = input_data.astype(np.uint8)
# Use the same values provided to the converter
mean_value = 0
std_dev_value = 255

input_data = input_data * std_dev_value + mean_value
input_data = input_data.astype(np.uint8)
real_output_value = (quantized_output_value - mean_value) / std_dev_value
tflite_convert --output_file model_lite/conv_net_post_quant.tflite \
  --graph_def_file frozen_graphs/conv_net.pb  \
  --input_arrays "input" \
  --input_shapes "1,784" \
  --output_arrays output \
  --output_format TFLITE \
  --post_training_quantize 1
real_input_value = (quantized_input_value - mean_value) / std_dev_value
quantized_input_value = real_input_value * std_dev_value + mean_value
input_data = input_data.astype(np.uint8)
# Use the same values provided to the converter
mean_value = 0
std_dev_value = 255

input_data = input_data * std_dev_value + mean_value
input_data = input_data.astype(np.uint8)
real_output_value = (quantized_output_value - mean_value) / std_dev_value
tflite_convert --output_file model_lite/conv_net_post_quant.tflite \
  --graph_def_file frozen_graphs/conv_net.pb  \
  --input_arrays "input" \
  --input_shapes "1,784" \
  --output_arrays output \
  --output_format TFLITE \
  --post_training_quantize 1
real_input_value = (quantized_input_value - mean_value) / std_dev_value
quantized_input_value = real_input_value * std_dev_value + mean_value
input_data = input_data.astype(np.uint8)
# Use the same values provided to the converter
mean_value = 0
std_dev_value = 255

input_data = input_data * std_dev_value + mean_value
input_data = input_data.astype(np.uint8)
real_output_value = (quantized_output_value - mean_value) / std_dev_value
tflite_convert --output_file model_lite/conv_net_post_quant.tflite \
  --graph_def_file frozen_graphs/conv_net.pb  \
  --input_arrays "input" \
  --input_shapes "1,784" \
  --output_arrays output \
  --output_format TFLITE \
  --post_training_quantize 1
real_input_value = (quantized_input_value - mean_value) / std_dev_value
quantized_input_value = real_input_value * std_dev_value + mean_value
input_data = input_data.astype(np.uint8)
# Use the same values provided to the converter
mean_value = 0
std_dev_value = 255

input_data = input_data * std_dev_value + mean_value
input_data = input_data.astype(np.uint8)
real_output_value = (quantized_output_value - mean_value) / std_dev_value
tflite_convert --output_file model_lite/conv_net_post_quant.tflite \
  --graph_def_file frozen_graphs/conv_net.pb  \
  --input_arrays "input" \
  --input_shapes "1,784" \
  --output_arrays output \
  --output_format TFLITE \
  --post_training_quantize 1
real_input_value = (quantized_input_value - mean_value) / std_dev_value
quantized_input_value = real_input_value * std_dev_value + mean_value
input_data = input_data.astype(np.uint8)
# Use the same values provided to the converter
mean_value = 0
std_dev_value = 255

input_data = input_data * std_dev_value + mean_value
input_data = input_data.astype(np.uint8)
real_output_value = (quantized_output_value - mean_value) / std_dev_value
tflite_convert --output_file model_lite/conv_net_post_quant.tflite \
  --graph_def_file frozen_graphs/conv_net.pb  \
  --input_arrays "input" \
  --input_shapes "1,784" \
  --output_arrays output \
  --output_format TFLITE \
  --post_training_quantize 1
real_input_value = (quantized_input_value - mean_value) / std_dev_value
quantized_input_value = real_input_value * std_dev_value + mean_value
input_data = input_data.astype(np.uint8)
# Use the same values provided to the converter
mean_value = 0
std_dev_value = 255

input_data = input_data * std_dev_value + mean_value
input_data = input_data.astype(np.uint8)
real_output_value = (quantized_output_value - mean_value) / std_dev_value
tflite_convert --output_file model_lite/conv_net_post_quant.tflite \
  --graph_def_file frozen_graphs/conv_net.pb  \
  --input_arrays "input" \
  --input_shapes "1,784" \
  --output_arrays output \
  --output_format TFLITE \
  --post_training_quantize 1

Community Discussions

Trending Discussions on TensorFlow-Examples
  • No module named 'tensorflow_examples' after installing
  • Unable to import tensorflow lite image classifier
  • Why the accuracy of TF-lite is not correct after quantization
Trending Discussions on TensorFlow-Examples

QUESTION

No module named 'tensorflow_examples' after installing

Asked 2020-Aug-25 at 22:13

In my Notebook's first cell, I wrote :

!pip install git+https://github.com/tensorflow/examples.git
!pip install -U tfds-nightly

On the next cell :

import tensorflow as tf
from tensorflow_examples.models.pix2pix import pix2pix

But it gives me : ModuleNotFoundError: No module named 'tensorflow_examples'

Notebook - version : 6.0.3
Tensorflow - version : 2.0.0

Do I need to install additional some modules ?

Here is the pip install log :

Collecting git+https://github.com/tensorflow/examples.git
  Cloning https://github.com/tensorflow/examples.git to c:\users\mua\appdata\local\temp\pip-req-build-qtmqgj7m
Requirement already satisfied (use --upgrade to upgrade): tensorflow-examples===b30a40f9416fc38cfa91ca03d835ba1fc432a824- from git+https://github.com/tensorflow/examples.git in e:\software\python 3.5\lib\site-packages
Requirement already satisfied: absl-py in e:\software\python 3.5\lib\site-packages (from tensorflow-examples===b30a40f9416fc38cfa91ca03d835ba1fc432a824-) (0.8.1)
Requirement already satisfied: six in e:\software\python 3.5\lib\site-packages (from tensorflow-examples===b30a40f9416fc38cfa91ca03d835ba1fc432a824-) (1.14.0)
Building wheels for collected packages: tensorflow-examples
  Building wheel for tensorflow-examples (setup.py): started
  Building wheel for tensorflow-examples (setup.py): finished with status 'done'
  Created wheel for tensorflow-examples: filename=tensorflow_examples-b30a40f9416fc38cfa91ca03d835ba1fc432a824_-py3-none-any.whl size=136427 sha256=40d5b23f277f4634313116bf6205588e8668a499798fe1c7fdad143fc6144b68
  Stored in directory: C:\Users\MUA\AppData\Local\Temp\pip-ephem-wheel-cache-5wvvxv8d\wheels\e2\f1\08\a5d8eb62f62cc814d511a70115a5467b1135ec8270dd16d620
Successfully built tensorflow-examples
  Running command git clone -q https://github.com/tensorflow/examples.git 'C:\Users\MUA\AppData\Local\Temp\pip-req-build-qtmqgj7m'
WARNING: You are using pip version 20.1.1; however, version 20.2.2 is available.
You should consider upgrading via the 'e:\software\python 3.5\python.exe -m pip install --upgrade pip' command.
Collecting tfds-nightly
  Downloading tfds_nightly-3.2.1.dev202007220105-py3-none-any.whl (3.4 MB)
Requirement already satisfied, skipping upgrade: wrapt in e:\software\python 3.5\lib\site-packages (from tfds-nightly) (1.11.2)
Requirement already satisfied, skipping upgrade: numpy in e:\software\python 3.5\lib\site-packages (from tfds-nightly) (1.17.4)
Requirement already satisfied, skipping upgrade: tqdm in e:\software\python 3.5\lib\site-packages (from tfds-nightly) (4.45.0)
Collecting tensorflow-metadata
  Downloading tensorflow_metadata-0.23.0-py3-none-any.whl (43 kB)
Collecting promise
  Downloading promise-2.3.tar.gz (19 kB)
Collecting dill
  Downloading dill-0.3.2.zip (177 kB)
Requirement already satisfied, skipping upgrade: six in e:\software\python 3.5\lib\site-packages (from tfds-nightly) (1.14.0)
Collecting attrs>=18.1.0
  Downloading attrs-19.3.0-py2.py3-none-any.whl (39 kB)
Requirement already satisfied, skipping upgrade: protobuf>=3.6.1 in e:\software\python 3.5\lib\site-packages (from tfds-nightly) (3.10.0)
Requirement already satisfied, skipping upgrade: absl-py in e:\software\python 3.5\lib\site-packages (from tfds-nightly) (0.8.1)
Requirement already satisfied, skipping upgrade: termcolor in e:\software\python 3.5\lib\site-packages (from tfds-nightly) (1.1.0)
Requirement already satisfied, skipping upgrade: requests>=2.19.0 in e:\software\python 3.5\lib\site-packages (from tfds-nightly) (2.23.0)
Collecting future
  Downloading future-0.18.2.tar.gz (829 kB)
Collecting googleapis-common-protos
  Downloading googleapis_common_protos-1.52.0-py2.py3-none-any.whl (100 kB)
Requirement already satisfied, skipping upgrade: setuptools in e:\software\python 3.5\lib\site-packages (from protobuf>=3.6.1->tfds-nightly) (41.6.0)
Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in e:\software\python 3.5\lib\site-packages (from requests>=2.19.0->tfds-nightly) (1.25.8)
Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in e:\software\python 3.5\lib\site-packages (from requests>=2.19.0->tfds-nightly) (2.9)
Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in e:\software\python 3.5\lib\site-packages (from requests>=2.19.0->tfds-nightly) (2019.11.28)
Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in e:\software\python 3.5\lib\site-packages (from requests>=2.19.0->tfds-nightly) (3.0.4)
Building wheels for collected packages: promise, dill, future
  Building wheel for promise (setup.py): started
  Building wheel for promise (setup.py): finished with status 'done'
  Created wheel for promise: filename=promise-2.3-py3-none-any.whl size=21498 sha256=82af1fb81258e76c2ddec82ec8870ec0901b560e2547ddff0f81e096cd65fdc2
  Stored in directory: c:\users\mua\appdata\local\pip\cache\wheels\b6\3e\4e\d80f74df03a8059f631b23ec49939d8fa0a2633522596b6ffd
  Building wheel for dill (setup.py): started
  Building wheel for dill (setup.py): finished with status 'done'
  Created wheel for dill: filename=dill-0.3.2-py3-none-any.whl size=78977 sha256=22a67eb861aca650bc9f1e039c15cb8ef9a87fbe88a139952868f352dd8f51aa
  Stored in directory: c:\users\mua\appdata\local\pip\cache\wheels\5c\4b\fd\db4143df7b4a4301b4068a2ed49f300b76b13d87b23bf375da
  Building wheel for future (setup.py): started
  Building wheel for future (setup.py): finished with status 'done'
  Created wheel for future: filename=future-0.18.2-py3-none-any.whl size=491061 sha256=f19a6fd742fd80f4a1995a132c1c10b6de14b112ef5110bcb31eff13a2379306
  Stored in directory: c:\users\mua\appdata\local\pip\cache\wheels\c4\f0\ae\d4689c4532d1f111462ed6a884a7767d502e511ee65f0d8e1b
Successfully built promise dill future
Installing collected packages: googleapis-common-protos, tensorflow-metadata, promise, dill, attrs, future, tfds-nightly
Successfully installed attrs-19.3.0 dill-0.3.2 future-0.18.2 googleapis-common-protos-1.52.0 promise-2.3 tensorflow-metadata-0.23.0 tfds-nightly-3.2.1.dev202007220105
WARNING: You are using pip version 20.1.1; however, version 20.2.2 is available.
You should consider upgrading via the 'e:\software\python 3.5\python.exe -m pip install --upgrade pip' command.

ANSWER

Answered 2020-Aug-25 at 22:13

From here:

!pip install -q git+https://github.com/tensorflow/examples.git

It worked for me.

Source https://stackoverflow.com/questions/63463856

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install TensorFlow-Examples

To download all the examples, simply clone this repository:.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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