TensorFlow Hub

https://www.tensorflow.org/hub/tutorials
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### Install Object Detection API

Source: https://www.tensorflow.org/hub/tutorials/tf2_object_detection

Commands to install protobuf compiler, compile protos, and install the Object Detection package.

```bash
sudo apt install -y protobuf-compiler
cd models/research/
protoc object_detection/protos/*.proto --python_out=.
cp object_detection/packages/tf2/setup.py .
python -m pip install .
```

--------------------------------

### Setup Evaluation Metric

Source: https://www.tensorflow.org/hub/tutorials/wav2vec2_saved_model_finetuning

Installs the datasets library and initializes the Word Error Rate (WER) metric.

```python
!pip3 install -q datasets

from datasets import load_metric
metric = load_metric("wer")
```

--------------------------------

### Install Dependencies

Source: https://www.tensorflow.org/hub/tutorials/action_recognition_with_tf_hub

Install the necessary libraries for video processing and documentation visualization.

```bash
pip install -q imageio
pip install -q opencv-python
pip install -q git+https://github.com/tensorflow/docs
```

--------------------------------

### Install Kaggle CLI

Source: https://www.tensorflow.org/hub/tutorials/text_classification_with_tf_hub_on_kaggle

Installs the Kaggle command-line tool in the environment.

```bash
pip install -q kaggle
```

--------------------------------

### Setup Environment

Source: https://www.tensorflow.org/hub/tutorials/biggan_generation_with_tf_hub

Import required libraries and configure TensorFlow for compatibility.

```python
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

import os
import io
import IPython.display
import numpy as np
import PIL.Image
from scipy.stats import truncnorm
import tensorflow_hub as hub
```

--------------------------------

### Install dependencies

Source: https://www.tensorflow.org/hub/tutorials/tf_hub_generative_image_module

Install required libraries for image processing and documentation visualization.

```bash
# Install imageio for creating animations.
pip -q install imageio
pip -q install scikit-image
pip install git+https://github.com/tensorflow/docs
```

--------------------------------

### Environment output example

Source: https://www.tensorflow.org/hub/tutorials/object_detection

Example output showing the TensorFlow version and GPU device status.

```text
2.16.1
The following GPU devices are available:
2024-03-09 13:48:48.238338: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
```

--------------------------------

### Install system dependencies

Source: https://www.tensorflow.org/hub/tutorials/spice

Install required system packages for audio processing.

```bash
sudo apt-get install -q -y timidity libsndfile1
```

--------------------------------

### Output example

Source: https://www.tensorflow.org/hub/tutorials/yamnet

Example output string from the inference process.

```text
The main sound is: Animal
```

--------------------------------

### Install dependencies

Source: https://www.tensorflow.org/hub/tutorials/senteval_for_universal_sentence_encoder_cmlm

Installs the required TensorFlow Text and PyTorch libraries.

```bash
pip install --quiet "tensorflow-text==2.11.*"
pip install --quiet torch==1.8.1
```

--------------------------------

### Install system unzip package

Source: https://www.tensorflow.org/hub/tutorials/bangla_article_classifier

Installs the unzip utility on the system using apt-get.

```bash
sudo apt-get install -y unzip
```

--------------------------------

### Install Required Libraries

Source: https://www.tensorflow.org/hub/tutorials/semantic_approximate_nearest_neighbors

Install Apache Beam, scikit-learn, and Annoy for data processing and nearest neighbor search.

```bash
pip install -q apache_beam
pip install -q sklearn
pip install -q annoy
```

--------------------------------

### Install System and Python Dependencies

Source: https://www.tensorflow.org/hub/tutorials/movinet

Install ffmpeg and required Python packages for video processing.

```bash
sudo apt install -y ffmpeg
pip install -q mediapy
```

```bash
pip uninstall -q -y opencv-python-headless
pip install -q "opencv-python-headless<4.3"
```

--------------------------------

### Load and Prepare Example Images

Source: https://www.tensorflow.org/hub/tutorials/tf2_arbitrary_image_stylization

Downloads and processes content and style images for the stylization model.

```python
# @title Load example images  { display-mode: "form" }

content_image_url = 'https://upload.wikimedia.org/wikipedia/commons/thumb/f/fd/Golden_Gate_Bridge_from_Battery_Spencer.jpg/640px-Golden_Gate_Bridge_from_Battery_Spencer.jpg'  # @param {type:"string"}
style_image_url = 'https://upload.wikimedia.org/wikipedia/commons/0/0a/The_Great_Wave_off_Kanagawa.jpg'  # @param {type:"string"}
output_image_size = 384  # @param {type:"integer"}

# The content image size can be arbitrary.
content_img_size = (output_image_size, output_image_size)
# The style prediction model was trained with image size 256 and it's the 
# recommended image size for the style image (though, other sizes work as 
# well but will lead to different results).
style_img_size = (256, 256)  # Recommended to keep it at 256.

content_image = load_image(content_image_url, content_img_size)
style_image = load_image(style_image_url, style_img_size)
style_image = tf.nn.avg_pool(style_image, ksize=[3,3], strides=[1,1], padding='SAME')
show_n([content_image, style_image], ['Content image', 'Style image'])
```

--------------------------------

### Install Dependencies

Source: https://www.tensorflow.org/hub/tutorials/tf2_object_detection

Install specific versions of numpy and protobuf required for the environment.

```bash
# This Colab requires a recent numpy version.
pip install numpy==1.24.3
pip install protobuf==3.20.3
```

--------------------------------

### Install Required Libraries

Source: https://www.tensorflow.org/hub/tutorials/tf2_semantic_approximate_nearest_neighbors

Install the necessary packages for data processing, machine learning, and approximate nearest neighbor search.

```bash
pip install -q apache_beam
pip install -q 'scikit_learn~=0.23.0'  # For gaussian_random_matrix.
pip install -q annoy
```

--------------------------------

### Example Output

Source: https://www.tensorflow.org/hub/tutorials/image_classification

Sample console output after selecting a model and configuring image size.

```text
Selected model: efficientnetv2-s : https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_s/classification/2
Images will be converted to 384x384
```

--------------------------------

### Environment Setup

Source: https://www.tensorflow.org/hub/tutorials/image_retraining

Initializes necessary libraries and verifies the TensorFlow version and GPU availability.

```python
import itertools
import os

import matplotlib.pylab as plt
import numpy as np

import tensorflow as tf
import tensorflow_hub as hub

print("TF version:", tf.__version__)
print("Hub version:", hub.__version__)
print("GPU is", "available" if tf.config.list_physical_devices('GPU') else "NOT AVAILABLE")
```

--------------------------------

### Install Python audio libraries

Source: https://www.tensorflow.org/hub/tutorials/spice

Install necessary Python packages for handling sound data.

```bash
# All the imports to deal with sound data
pip install pydub librosa music21
```

--------------------------------

### Install dependencies

Source: https://www.tensorflow.org/hub/tutorials/tf_hub_delf_module

Install the required scikit-image library before running the DELF module.

```bash
pip install scikit-image
```

--------------------------------

### Install dependencies for Wav2Vec2 fine-tuning

Source: https://www.tensorflow.org/hub/tutorials/wav2vec2_saved_model_finetuning

Install the required gsoc-wav2vec2 package, system audio libraries, and SoundFile for data processing.

```bash
pip3 install -q git+https://github.com/vasudevgupta7/gsoc-wav2vec2@main
sudo apt-get install -y libsndfile1-dev
pip3 install -q SoundFile
```

--------------------------------

### Display Labeled Training Examples

Source: https://www.tensorflow.org/hub/tutorials/cord_19_embeddings

Retrieves a batch of training examples and displays them in a pandas DataFrame.

```python
NUM_EXAMPLES = 20 
data = get_example_data(THE_DATASET, NUM_EXAMPLES, for_eval=False)
display_df(
    pd.DataFrame({
        TEXT_FEATURE_NAME: [ex.decode('utf8') for ex in data[0]],
        LABEL_NAME: [THE_DATASET.class_names()[x] for x in data[1]]
    }))
```

--------------------------------

### Example Output

Source: https://www.tensorflow.org/hub/tutorials/cropnet_on_device

Displays the expected console output after merging the datasets.

```text
Added 16968 negative examples.Training dataset has now 22624 examples in total.
```

--------------------------------

### Install Dependencies

Source: https://www.tensorflow.org/hub/tutorials/retrieval_with_tf_hub_universal_encoder_qa

Installs the required TensorFlow, text processing, and nearest neighbor libraries.

```bash
%%capture
# Install the latest Tensorflow version.
!pip install -q "tensorflow-text==2.11.*"
!pip install -q simpleneighbors[annoy]
!pip install -q nltk
!pip install -q tqdm
```

--------------------------------

### Install Dependencies

Source: https://www.tensorflow.org/hub/tutorials/cropnet_on_device

Install required system libraries and Python packages for Model Maker and TensorFlow Datasets.

```bash
sudo apt install -q libportaudio2
## image_classifier library requires numpy <= 1.23.5
pip install "numpy<=1.23.5"
pip install --use-deprecated=legacy-resolver tflite-model-maker-nightly
pip install -U tensorflow-datasets
## scann library requires tensorflow < 2.9.0
pip install "tensorflow<2.9.0"
pip install "tensorflow-datasets~=4.8.0"  # protobuf>=3.12.2
pip install tensorflow-metadata~=1.10.0  # protobuf>=3.13
## tensorflowjs requires packaging < 20.10
pip install "packaging<20.10"
```

--------------------------------

### Download Example Video

Source: https://www.tensorflow.org/hub/tutorials/movinet

Download a sample GIF file for classification testing.

```python
jumpingjack_url = 'https://github.com/tensorflow/models/raw/f8af2291cced43fc9f1d9b41ddbf772ae7b0d7d2/official/projects/movinet/files/jumpingjack.gif'
jumpingjack_path = tf.keras.utils.get_file(
    fname='jumpingjack.gif',
    origin=jumpingjack_url,
    cache_dir='.', cache_subdir='.',
)
```

--------------------------------

### Directory Listing Output

Source: https://www.tensorflow.org/hub/tutorials/semantic_approximate_nearest_neighbors

Example output showing the generated index files.

```text
corpus  index.mapping         raw.tsv
index   random_projection_matrix  semantic_approximate_nearest_neighbors.ipynb
```

--------------------------------

### Install dependencies and import libraries

Source: https://www.tensorflow.org/hub/tutorials/text_to_video_retrieval_with_s3d_milnce

Install the required OpenCV library and import TensorFlow, TensorFlow Hub, and other utility modules for video processing.

```python
!pip install -q opencv-python

import os

import tensorflow.compat.v2 as tf
import tensorflow_hub as hub

import numpy as np
import cv2
from IPython import display
import math
```

--------------------------------

### View sample data output

Source: https://www.tensorflow.org/hub/tutorials/text_classification_with_tf_hub

Displays the content of the first few movie review examples.

```python
array([b"This was an absolutely terrible movie. Don't be lured in by Christopher Walken or Michael Ironside. Both are great actors, but this must simply be their worst role in history. Even their great acting could not redeem this movie's ridiculous storyline. This movie is an early nineties US propaganda piece. The most pathetic scenes were those when the Columbian rebels were making their cases for revolutions. Maria Conchita Alonso appeared phony, and her pseudo-love affair with Walken was nothing but a pathetic emotional plug in a movie that was devoid of any real meaning. I am disappointed that there are movies like this, ruining actor's like Christopher Walken's good name. I could barely sit through it.",
       b'I have been known to fall asleep during films, but this is usually due to a combination of things including, really tired, being warm and comfortable on the sette and having just eaten a lot. However on this occasion I fell asleep because the film was rubbish. The plot development was constant. Constantly slow and boring. Things seemed to happen, but with no explanation of what was causing them or why. I admit, I may have missed part of the film, but i watched the majority of it and everything just seemed to happen of its own accord without any real concern for anything else. I cant recommend this film at all.',
       b'Mann photographs the Alberta Rocky Mountains in a superb fashion, and Jimmy Stewart and Walter Brennan give enjoyable performances as they always seem to do. <br /><br />But come on Hollywood - a Mountie telling the people of Dawson City, Yukon to elect themselves a marshal (yes a marshal!) and to enforce the law themselves, then gunfighters battling it out on the streets for control of the town? <br /><br />Nothing even remotely resembling that happened on the Canadian side of the border during the Klondike gold rush. Mr. Mann and company appear to have mistaken Dawson City for Deadwood, the Canadian North for the American Wild West.<br /><br />Canadian viewers be prepared for a Reefer Madness type of enjoyable howl with this ludicrous plot, or, to shake your head in disgust.',
       b'This is the kind of film for a snowy Sunday afternoon when the rest of the world can go ahead with its own business as you descend into a big arm-chair and mellow for a couple of hours. Wonderful performances from Cher and Nicolas Cage (as always) gently row the plot along. There are no rapids to cross, no dangerous waters, just a warm and witty paddle through New York life at its best. A family film in every sense and one that deserves the praise it received.',
       b'As others have mentioned, all the women that go nude in this film are mostly absolutely gorgeous. The plot very ably shows the hypocrisy of the female libido. When men are around they want to be pursued, but when no "men" are around, they become the pursuers of a 14 year old boy. And the boy becomes a man really fast (we should all be so lucky at this age!). He then gets up the courage to pursue his true love.',
```

--------------------------------

### UCF101 dataset output example

Source: https://www.tensorflow.org/hub/tutorials/action_recognition_with_tf_hub

Example output showing the distribution of videos across various action categories in the UCF101 dataset.

```text
Found 13320 videos in 101 categories.
ApplyEyeMakeup        145 videos (v_ApplyEyeMakeup_g01_c01.avi, v_ApplyEyeMakeup_g01_c02.avi, ...)
ApplyLipstick         114 videos (v_ApplyLipstick_g01_c01.avi, v_ApplyLipstick_g01_c02.avi, ...)
Archery               145 videos (v_Archery_g01_c01.avi, v_Archery_g01_c02.avi, ...)
BabyCrawling          132 videos (v_BabyCrawling_g01_c01.avi, v_BabyCrawling_g01_c02.avi, ...)
BalanceBeam           108 videos (v_BalanceBeam_g01_c01.avi, v_BalanceBeam_g01_c02.avi, ...)
BandMarching          155 videos (v_BandMarching_g01_c01.avi, v_BandMarching_g01_c02.avi, ...)
BaseballPitch         150 videos (v_BaseballPitch_g01_c01.avi, v_BaseballPitch_g01_c02.avi, ...)
BasketballDunk        131 videos (v_BasketballDunk_g01_c01.avi, v_BasketballDunk_g01_c02.avi, ...)
Basketball            134 videos (v_Basketball_g01_c01.avi, v_Basketball_g01_c02.avi, ...)
BenchPress            160 videos (v_BenchPress_g01_c01.avi, v_BenchPress_g01_c02.avi, ...)
Biking                134 videos (v_Biking_g01_c01.avi, v_Biking_g01_c02.avi, ...)
Billiards             150 videos (v_Billiards_g01_c01.avi, v_Billiards_g01_c02.avi, ...)
BlowDryHair           131 videos (v_BlowDryHair_g01_c01.avi, v_BlowDryHair_g01_c02.avi, ...)
BlowingCandles        109 videos (v_BlowingCandles_g01_c01.avi, v_BlowingCandles_g01_c02.avi, ...)
BodyWeightSquats      112 videos (v_BodyWeightSquats_g01_c01.avi, v_BodyWeightSquats_g01_c02.avi, ...)
Bowling               155 videos (v_Bowling_g01_c01.avi, v_Bowling_g01_c02.avi, ...)
BoxingPunchingBag     163 videos (v_BoxingPunchingBag_g01_c01.avi, v_BoxingPunchingBag_g01_c02.avi, ...)
BoxingSpeedBag        134 videos (v_BoxingSpeedBag_g01_c01.avi, v_BoxingSpeedBag_g01_c02.avi, ...)
BreastStroke          101 videos (v_BreastStroke_g01_c01.avi, v_BreastStroke_g01_c02.avi, ...)
BrushingTeeth         131 videos (v_BrushingTeeth_g01_c01.avi, v_BrushingTeeth_g01_c02.avi, ...)
CleanAndJerk          112 videos (v_CleanAndJerk_g01_c01.avi, v_CleanAndJerk_g01_c02.avi, ...)
CliffDiving           138 videos (v_CliffDiving_g01_c01.avi, v_CliffDiving_g01_c02.avi, ...)
CricketBowling        139 videos (v_CricketBowling_g01_c01.avi, v_CricketBowling_g01_c02.avi, ...)
CricketShot           167 videos (v_CricketShot_g01_c01.avi, v_CricketShot_g01_c02.avi, ...)
CuttingInKitchen      110 videos (v_CuttingInKitchen_g01_c01.avi, v_CuttingInKitchen_g01_c02.avi, ...)
Diving                150 videos (v_Diving_g01_c01.avi, v_Diving_g01_c02.avi, ...)
Drumming              161 videos (v_Drumming_g01_c01.avi, v_Drumming_g01_c02.avi, ...)
Fencing               111 videos (v_Fencing_g01_c01.avi, v_Fencing_g01_c02.avi, ...)
FieldHockeyPenalty    126 videos (v_FieldHockeyPenalty_g01_c01.avi, v_FieldHockeyPenalty_g01_c02.avi, ...)
FloorGymnastics       125 videos (v_FloorGymnastics_g01_c01.avi, v_FloorGymnastics_g01_c02.avi, ...)
FrisbeeCatch          126 videos (v_FrisbeeCatch_g01_c01.avi, v_FrisbeeCatch_g01_c02.avi, ...)
FrontCrawl            137 videos (v_FrontCrawl_g01_c01.avi, v_FrontCrawl_g01_c02.avi, ...)
GolfSwing             139 videos (v_GolfSwing_g01_c01.avi, v_GolfSwing_g01_c02.avi, ...)
Haircut               130 videos (v_Haircut_g01_c01.avi, v_Haircut_g01_c02.avi, ...)
HammerThrow           150 videos (v_HammerThrow_g01_c01.avi, v_HammerThrow_g01_c02.avi, ...)
Hammering             140 videos (v_Hammering_g01_c01.avi, v_Hammering_g01_c02.avi, ...)
HandstandPushups      128 videos (v_HandstandPushups_g01_c01.avi, v_HandstandPushups_g01_c02.avi, ...)
HandstandWalking      111 videos (v_HandstandWalking_g01_c01.avi, v_HandstandWalking_g01_c02.avi, ...)
HeadMassage           147 videos (v_HeadMassage_g01_c01.avi, v_HeadMassage_g01_c02.avi, ...)
HighJump              123 videos (v_HighJump_g01_c01.avi, v_HighJump_g01_c02.avi, ...)
HorseRace             124 videos (v_HorseRace_g01_c01.avi, v_HorseRace_g01_c02.avi, ...)
HorseRiding           164 videos (v_HorseRiding_g01_c01.avi, v_HorseRiding_g01_c02.avi, ...)
HulaHoop              125 videos (v_HulaHoop_g01_c01.avi, v_HulaHoop_g01_c02.avi, ...)
IceDancing            158 videos (v_IceDancing_g01_c01.avi, v_IceDancing_g01_c02.avi, ...)
JavelinThrow          117 videos (v_JavelinThrow_g01_c01.avi, v_JavelinThrow_g01_c02.avi, ...)
JugglingBalls         121 videos (v_JugglingBalls_g01_c01.avi, v_JugglingBalls_g01_c02.avi, ...)
JumpRope              144 videos (v_JumpRope_g01_c01.avi, v_JumpRope_g01_c02.avi, ...)
```

--------------------------------

### Model loading output

Source: https://www.tensorflow.org/hub/tutorials/boundless

Example console output when loading the model.

```text
Loading model Boundless Quarter (https://tfhub.dev/google/boundless/quarter/1)
2024-03-09 13:56:07.413611: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
```

--------------------------------

### Setup Imports and Utility Functions

Source: https://www.tensorflow.org/hub/tutorials/s3gan_generation_with_tf_hub

Initializes the environment with required libraries and defines helper functions for image grid creation and display.

```python
# @title Imports and utility functions
import os

import IPython
from IPython.display import display
import numpy as np
import PIL.Image
import pandas as pd
import six

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

import tensorflow_hub as hub

def imgrid(imarray, cols=8, pad=1):
  pad = int(pad)
  assert pad >= 0
  cols = int(cols)
  assert cols >= 1
  N, H, W, C = imarray.shape
  rows = int(np.ceil(N / float(cols)))
  batch_pad = rows * cols - N
  assert batch_pad >= 0
  post_pad = [batch_pad, pad, pad, 0]
  pad_arg = [[0, p] for p in post_pad]
  imarray = np.pad(imarray, pad_arg, 'constant')
  H += pad
  W += pad
  grid = (imarray
          .reshape(rows, cols, H, W, C)
          .transpose(0, 2, 1, 3, 4)
          .reshape(rows*H, cols*W, C))
  return grid[:-pad, :-pad]


def imshow(a, format='png', jpeg_fallback=True):
  a = np.asarray(a, dtype=np.uint8)
  if six.PY3:
    str_file = six.BytesIO()
  else:
    str_file = six.StringIO()
  PIL.Image.fromarray(a).save(str_file, format)
  png_data = str_file.getvalue()
  try:
    disp = display(IPython.display.Image(png_data))
  except IOError:
    if jpeg_fallback and format != 'jpeg':
      print ('Warning: image was too large to display in format "{}"; '
             'trying jpeg instead.').format(format)
      return imshow(a, format='jpeg')
    else:
      raise
  return disp
```

--------------------------------

### Install dependencies

Source: https://www.tensorflow.org/hub/tutorials/cropnet_cassava

Install the required version of matplotlib.

```bash
pip install matplotlib==3.2.2
```

--------------------------------

### Importing libraries and configuring Wav2Vec2

Source: https://www.tensorflow.org/hub/tutorials/wav2vec2_saved_model_finetuning

Initial setup including library imports and Wav2Vec2 configuration initialization.

```python
import os

import tensorflow as tf
import tensorflow_hub as hub
from wav2vec2 import Wav2Vec2Config

config = Wav2Vec2Config()

print("TF version:", tf.__version__)
```

--------------------------------

### Load Example Videos and Queries

Source: https://www.tensorflow.org/hub/tutorials/text_to_video_retrieval_with_s3d_milnce?hl=zh-tw

Initializes video URLs and text queries, then processes the videos for use in the retrieval pipeline.

```python
# @title Load example videos and define text queries  { display-mode: "form" }

video_1_url = 'https://upload.wikimedia.org/wikipedia/commons/b/b0/YosriAirTerjun.gif' # @param {type:"string"}
video_2_url = 'https://upload.wikimedia.org/wikipedia/commons/e/e6/Guitar_solo_gif.gif' # @param {type:"string"}
video_3_url = 'https://upload.wikimedia.org/wikipedia/commons/3/30/2009-08-16-autodrift-by-RalfR-gif-by-wau.gif' # @param {type:"string"}

video_1 = load_video(video_1_url)
video_2 = load_video(video_2_url)
video_3 = load_video(video_3_url)
all_videos = [video_1, video_2, video_3]

query_1_video = 'waterfall' # @param {type:"string"}
query_2_video = 'playing guitar' # @param {type:"string"}
query_3_video = 'car drifting' # @param {type:"string"}
all_queries_video = [query_1_video, query_2_video, query_3_video]
all_videos_urls = [video_1_url, video_2_url, video_3_url]
display_video(all_videos_urls)
```

--------------------------------

### Install Seaborn dependency

Source: https://www.tensorflow.org/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder

Install the seaborn library for visualization purposes.

```python
%%capture
!pip3 install seaborn
```

--------------------------------

### Install TensorFlow Text

Source: https://www.tensorflow.org/hub/tutorials/bert_experts

Install the required version of tensorflow-text for preprocessing.

```bash
pip install --quiet "tensorflow-text==2.11.*"
```

--------------------------------

### Setup Imports, Utilities, and Model Wrappers

Source: https://www.tensorflow.org/hub/tutorials/s3gan_generation_with_tf_hub?hl=ar

Includes essential libraries, image grid/display functions, and classes for managing S3GAN generator and discriminator modules.

```python
# @title Imports and utility functions  
import os  
  
import IPython  
from IPython.display import display  
import numpy as np  
import PIL.Image  
import pandas as pd  
import six  
  
import tensorflow.compat.v1 as tf  
tf.disable_v2_behavior()  
  
import tensorflow_hub as hub  
  
def imgrid(imarray, cols=8, pad=1):  
	 pad = int(pad)  
	 assert pad >= 0  
	 cols = int(cols)  
	 assert cols >= 1  
	 N, H, W, C = imarray.shape  
	 rows = int(np.ceil(N / float(cols)))  
	 batch_pad = rows * cols - N  
	 assert batch_pad >= 0  
	 post_pad = [batch_pad, pad, pad, 0]  
	 pad_arg = [[0, p] for p in post_pad]  
	 imarray = np.pad(imarray, pad_arg, 'constant')  
	 H += pad  
	 W += pad  
	 grid = (imarray  
	           .reshape(rows, cols, H, W, C)  
	           .transpose(0, 2, 1, 3, 4)  
	           .reshape(rows*H, cols*W, C))  
	 return grid[:-pad, :-pad]  
  
  
def imshow(a, format='png', jpeg_fallback=True):  
	 a = np.asarray(a, dtype=np.uint8)  
	 if six.PY3:  
	   str_file = six.BytesIO()  
	 else:  
	   str_file = six.StringIO()  
	 PIL.Image.fromarray(a).save(str_file, format)  
	 png_data = str_file.getvalue()  
	 try:  
	   disp = display(IPython.display.Image(png_data))  
	 except IOError:  
	   if jpeg_fallback and format != 'jpeg':  
	     print ('Warning: image was too large to display in format "{}"; '  
	            'trying jpeg instead.').format(format)  
	     return imshow(a, format='jpeg')  
	   else:  
	     raise  
	 return disp  
  
  
class Generator(object):  
  
	 def __init__(self, module_spec):  
	   self._module_spec = module_spec  
	   self._sess = None  
	   self._graph = tf.Graph()  
	   self._load_model()  
  
	 @property  
	 def z_dim(self):  
	   return self._z.shape[-1].value  
  
	 @property  
	 def conditional(self):  
	   return self._labels is not None  
  
	 def _load_model(self):  
	   with self._graph.as_default():  
	     self._generator = hub.Module(self._module_spec, name="gen_module",  
	                                    tags={"gen", "bsNone"})  
	     input_info = self._generator.get_input_info_dict()  
	     inputs = {k: tf.placeholder(v.dtype, v.get_shape().as_list(), k)  
	               for k, v in self._generator.get_input_info_dict().items()}  
	     self._samples = self._generator(inputs=inputs, as_dict=True)["generated"]  
	     print("Inputs:", inputs)  
	     print("Outputs:", self._samples)  
	     self._z = inputs["z"]  
	     self._labels = inputs.get("labels", None)  
  
	 def _init_session(self):  
	   if self._sess is None:  
	     self._sess = tf.Session(graph=self._graph)  
	     self._sess.run(tf.global_variables_initializer())  
  
	 def get_noise(self, num_samples, seed=None):  
	   if np.isscalar(seed):  
	     np.random.seed(seed)  
	     return np.random.normal(size=[num_samples, self.z_dim])  
	   z = np.empty(shape=(len(seed), self.z_dim), dtype=np.float32)  
	   for i, s in enumerate(seed):  
	     np.random.seed(s)  
	     z[i] = np.random.normal(size=[self.z_dim])  
	   return z  
  
	 def get_samples(self, z, labels=None):  
	   with self._graph.as_default():  
	     self._init_session()  
	     feed_dict = {self._z: z}  
	     if self.conditional:  
	       assert labels is not None  
	       assert labels.shape[0] == z.shape[0]  
	       feed_dict[self._labels] = labels  
	     samples = self._sess.run(self._samples, feed_dict=feed_dict)  
	     return np.uint8(np.clip(256 * samples, 0, 255))  
  
  
class Discriminator(object):  
  
	 def __init__(self, module_spec):  
	   self._module_spec = module_spec  
	   self._sess = None  
	   self._graph = tf.Graph()  
	   self._load_model()  
  
	 @property  
	 def conditional(self):  
	   return "labels" in self._inputs  
  
	 @property  
	 def image_shape(self):  
	   return self._inputs["images"].shape.as_list()[1:]  
  
	 def _load_model(self):  
	   with self._graph.as_default():  
	     self._discriminator = hub.Module(self._module_spec, name="disc_module",  
	                                        tags={"disc", "bsNone"})  
	     input_info = self._discriminator.get_input_info_dict()  
	     self._inputs = {k: tf.placeholder(v.dtype, v.get_shape().as_list(), k)  
	                     for k, v in input_info.items()}  
	     self._outputs = self._discriminator(inputs=self._inputs, as_dict=True)  
	     print("Inputs:", self._inputs)  
	     print("Outputs:", self._outputs)  
  
	 def _init_session(self):  
	   if self._sess is None:  
	     self._sess = tf.Session(graph=self._graph)  
	     self._sess.run(tf.global_variables_initializer())  
  
	 def predict(self, images, labels=None):  
	   with self._graph.as_default():  
	     self._init_session()  
	     feed_dict = {self._inputs["images"]: images}  
	     if "labels" in self._inputs:  
	       assert labels is not None  
	       assert labels.shape[0] == images.shape[0]  
	       feed_dict[self._inputs["labels"]] = labels  
	     return self._sess.run(self._outputs, feed_dict=feed_dict)
```

--------------------------------

### Setup and Library Imports

Source: https://www.tensorflow.org/hub/tutorials/cord_19_embeddings_keras

Initializes the environment by importing essential data science and TensorFlow libraries required for embedding analysis and model training.

```python
import functools
import itertools
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd

import tensorflow as tf

import tensorflow_datasets as tfds
import tensorflow_hub as hub

from tqdm import trange
```

--------------------------------

### Environment Setup and Version Verification

Source: https://www.tensorflow.org/hub/tutorials/text_classification_with_tf_hub

Imports necessary libraries and prints version information to verify the environment configuration.

```python
import numpy as np

import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_datasets as tfds

import matplotlib.pyplot as plt

print("Version: ", tf.__version__)
print("Eager mode: ", tf.executing_eagerly())
print("Hub version: ", hub.__version__)
print("GPU is", "available" if tf.config.list_physical_devices('GPU') else "NOT AVAILABLE")
```

--------------------------------

### Install dependencies for FILM

Source: https://www.tensorflow.org/hub/tutorials/tf_hub_film_example

Install the required mediapy library and ffmpeg system package.

```bash
pip install mediapy
sudo apt-get install -y ffmpeg
```

--------------------------------

### Rapid prototyping configuration parameters

Source: https://www.tensorflow.org/hub/tutorials/senteval_for_universal_sentence_encoder_cmlm

Configuration dictionary for faster execution, typically taking 5-15 minutes.

```python
params = {'task_path': PATH_TO_DATA, 'usepytorch': True, 'kfold': 5}
params['classifier'] = {'nhid': 0, 'optim': 'rmsprop', 'batch_size': 128,
                                 'tenacity': 3, 'epoch_size': 2}
```

--------------------------------

### Install dependencies

Source: https://www.tensorflow.org/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder_lite

Install the required seaborn and sentencepiece packages for visualization and text processing.

```bash
# Install seaborn for pretty visualizations
pip3 install --quiet seaborn
# Install SentencePiece package
# SentencePiece package is needed for Universal Sentence Encoder Lite. We'll
# use it for all the text processing and sentence feature ID lookup.
pip3 install --quiet sentencepiece
```

--------------------------------

### Inspect first 10 examples

Source: https://www.tensorflow.org/hub/tutorials/text_classification_with_tf_hub

Retrieves and displays the first 10 entries from the training dataset.

```python
train_examples[:10]
```

--------------------------------

### Initialize Model Weights and Summary

Source: https://www.tensorflow.org/hub/tutorials/wav2vec2_saved_model_finetuning

Builds model weights by performing a dummy forward pass and displays the model architecture summary.

```python
model(tf.random.uniform(shape=(BATCH_SIZE, AUDIO_MAXLEN)))
model.summary()
```

--------------------------------

### Install and Import TensorFlow Transform

Source: https://www.tensorflow.org/hub/tutorials/semantic_approximate_nearest_neighbors

Install the specific version of TensorFlow Transform and import its modules.

```python
# TFT needs to be installed afterwards
!pip install -q tensorflow_transform==0.24
import tensorflow_transform as tft
import tensorflow_transform.beam as tft_beam
```

--------------------------------

### Initialize Environment and Helper Functions

Source: https://www.tensorflow.org/hub/tutorials/retrieval_with_tf_hub_universal_encoder_qa

Imports necessary modules and defines functions for downloading SQuAD data, tokenizing sentences, and displaying retrieval results.

```python
import json
import nltk
import os
import pprint
import random
import simpleneighbors
import urllib
from IPython.display import HTML, display
from tqdm.notebook import tqdm

import tensorflow.compat.v2 as tf
import tensorflow_hub as hub
from tensorflow_text import SentencepieceTokenizer

nltk.download('punkt')


def download_squad(url):
  return json.load(urllib.request.urlopen(url))

def extract_sentences_from_squad_json(squad):
  all_sentences = []
  for data in squad['data']:
    for paragraph in data['paragraphs']:
      sentences = nltk.tokenize.sent_tokenize(paragraph['context'])
      all_sentences.extend(zip(sentences, [paragraph['context']] * len(sentences)))
  return list(set(all_sentences)) # remove duplicates

def extract_questions_from_squad_json(squad):
  questions = []
  for data in squad['data']:
    for paragraph in data['paragraphs']:
      for qas in paragraph['qas']:
        if qas['answers']:
          questions.append((qas['question'], qas['answers'][0]['text']))
  return list(set(questions))

def output_with_highlight(text, highlight):
  output = "<li> "
  i = text.find(highlight)
  while True:
    if i == -1:
      output += text
      break
    output += text[0:i]
    output += '<b>'+text[i:i+len(highlight)]+'</b>'
    text = text[i+len(highlight):]
    i = text.find(highlight)
  return output + "</li>\n"

def display_nearest_neighbors(query_text, answer_text=None):
  query_embedding = model.signatures['question_encoder'](tf.constant([query_text]))['outputs'][0]
  search_results = index.nearest(query_embedding, n=num_results)

  if answer_text:
    result_md = '''
    <p>Random Question from SQuAD:</p>
    <p>&nbsp;&nbsp;<b>%s</b></p>
    <p>Answer:</p>
    <p>&nbsp;&nbsp;<b>%s</b></p>
    ''' % (query_text , answer_text)
  else:
    result_md = '''
    <p>Question:</p>
    <p>&nbsp;&nbsp;<b>%s</b></p>
    ''' % query_text

  result_md += '''
    <p>Retrieved sentences :
    <ol>
  '''

  if answer_text:
    for s in search_results:
      result_md += output_with_highlight(s, answer_text)
  else:
    for s in search_results:
      result_md += '<li>' + s + '</li>\n'

  result_md += "</ol>"
  display(HTML(result_md))
```

--------------------------------

### Install Dependencies

Source: https://www.tensorflow.org/hub/tutorials/cross_lingual_similarity_with_tf_hub_multilingual_universal_encoder

Install required packages including TensorFlow Text, Bokeh, and SimpleNeighbors for the environment.

```bash
%%capture
# Install the latest Tensorflow version.
!pip install "tensorflow-text==2.11.*"
!pip install bokeh
!pip install simpleneighbors[annoy]
!pip install tqdm
```

--------------------------------

### Inspect Labeled Examples

Source: https://www.tensorflow.org/hub/tutorials/cord_19_embeddings_keras

Extracts a batch of examples from the training set and displays them in a pandas DataFrame with human-readable labels.

```python
NUM_EXAMPLES =   10

TEXT_FEATURE_NAME = builder.info.supervised_keys[0]
LABEL_NAME = builder.info.supervised_keys[1]

def label2str(numeric_label):
  m = builder.info.features[LABEL_NAME].names
  return m[numeric_label]

data = next(iter(train_data.batch(NUM_EXAMPLES)))


pd.DataFrame({
    TEXT_FEATURE_NAME: [ex.numpy().decode('utf8') for ex in data[0]],
    LABEL_NAME: [label2str(x) for x in data[1]]
})
```

--------------------------------

### Preparing the Environment

Source: https://www.tensorflow.org/hub/tutorials/image_enhancing

Imports necessary libraries and sets the environment variable for TensorFlow Hub download progress.

```python
import os
import time
from PIL import Image
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
os.environ["TFHUB_DOWNLOAD_PROGRESS"] = "True"
```

--------------------------------

### Install gdown dependency

Source: https://www.tensorflow.org/hub/tutorials/bangla_article_classifier

Installs the gdown package using pip with the --no-use-pep517 flag to avoid specific setuptools issues.

```bash
pip install gdown --no-use-pep517
```

--------------------------------

### Error log output

Source: https://www.tensorflow.org/hub/tutorials/tf_hub_film_example

Example of a CUDA device error log.

```text
2024-03-09 12:18:00.216249: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
```

--------------------------------

### Import Dependencies and Setup Directory

Source: https://www.tensorflow.org/hub/tutorials/tweening_conv3d

Imports necessary libraries and defines a temporary directory for storing the BAIR dataset test split.

```python
import tensorflow as tf

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import tensorflow_hub as hub
import tensorflow_datasets as tfds

from tensorflow_datasets.core import SplitGenerator
from tensorflow_datasets.video.bair_robot_pushing import BairRobotPushingSmall

import tempfile
import pathlib

TEST_DIR = pathlib.Path(tempfile.mkdtemp()) / "bair_robot_pushing_small/softmotion30_44k/test/"
```

--------------------------------

### Model evaluation output

Source: https://www.tensorflow.org/hub/tutorials/cropnet_on_device?hl=zh-cn

Example output from the model evaluation process.

```text
59/59 [==============================] - 7s 101ms/step - loss: 0.9668 - accuracy: 0.8637
[0.9668245911598206, 0.863660454750061]
```

--------------------------------

### Downloading Sample Image

Source: https://www.tensorflow.org/hub/tutorials/image_enhancing

Downloads a sample image for super-resolution processing using wget.

```bash
wget "https://user-images.githubusercontent.com/12981474/40157448-eff91f06-5953-11e8-9a37-f6b5693fa03f.png" -O original.png
```

--------------------------------

### Index construction output log

Source: https://www.tensorflow.org/hub/tutorials/tf2_semantic_approximate_nearest_neighbors

Example output showing the progress of loading items and building the ANNOY index.

```text
rm: cannot remove 'index': No such file or directory
rm: cannot remove 'index.mapping': No such file or directory
Found 1 embedding file(s).
Loading embeddings in file 1 of 1...
100000 items loaded to the index
200000 items loaded to the index
300000 items loaded to the index
400000 items loaded to the index
500000 items loaded to the index
600000 items loaded to the index
700000 items loaded to the index
800000 items loaded to the index
900000 items loaded to the index
1000000 items loaded to the index
1100000 items loaded to the index
A total of 1103664 items added to the index
Building the index with 100 trees...
Index is successfully built.
Saving index to disk...
Index is saved to disk.
Index file size: 1.61 GB
Saving mapping to disk...
Mapping is saved to disk.
Mapping file size: 50.61 MB
CPU times: user 9min 54s, sys: 53.9 s, total: 10min 48s
Wall time: 5min 5s
```
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