📑 TensorFlow Workout 02. TFHub Models

tensorflow_02.html

📑 TensorFlow Workout 02

Code Modules

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!python3 -m pip install tensorflow_hub \

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--user --quiet --no-warn-script-location

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#!python3 -m pip install tensorflow==2.6.0 \

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#--user --quiet --no-warn-script-location

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path='/home/sc_work/.sage/local/lib/python3.9/site-packages'

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import sys,warnings; sys.path.append(path)

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warnings.filterwarnings('ignore')

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import tensorflow_hub as th,tensorflow as tf

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import numpy as np,pandas as pd,pylab as pl,h5py,urllib

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Functions

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def interpolate_hypersphere(v1,v2,steps=int(60)):

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    v1norm=tf.norm(v1); v2norm=tf.norm(v2); vectors=[]

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    v2normalized=v2*(v1norm/v2norm)

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    for step in range(steps):

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        interpolated=v1+(v2normalized-v1)*step/(steps-int(1))

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        interpolated_norm=tf.norm(interpolated)

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        interpolated_normalized=\

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        interpolated*(v1norm/interpolated_norm)

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        vectors.append(interpolated_normalized)

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    return tf.stack(vectors).numpy()

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def load_img(img_file,max_dim=int(512)):

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    img=tf.io.read_file(img_file)

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    img=tf.image.decode_image(img,channels=int(3))

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    img=tf.image.convert_image_dtype(img,tf.float32)

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    shape=tf.cast(tf.shape(img)[:-int(1)],tf.float32)

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    scale=max_dim/max(shape)

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    new_shape=tf.cast(shape*scale,tf.int32)

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    img=tf.image.resize(img,new_shape)

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    return img[tf.newaxis,:]

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def tensor_to_image(tensor):

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    if np.ndim(tensor)>int(3):

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        assert tensor.shape[int(0)]==int(1)

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        tensor=tensor[int(0)]

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    return matrix_plot(tensor,frame=False).show()

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Image Data & Interpolation

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path='https://raw.githubusercontent.com/'+\

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     'OlgaBelitskaya/data_kitchen/main/'

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zf='HorseBreeds160.h5'

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input_file=urllib.request.urlopen(path+zf)

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output_file=open(zf,'wb'); output_file.write(input_file.read())

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output_file.close(); input_file.close()

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with h5py.File(zf,'r') as f:

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    keys=list(f.keys())

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    pretty_print(html(

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        '<style>p:hover {color:darkorange}</style>'+\

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        '<p>file keys: '+', '.join(keys)+'</p>'))

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    images=np.array(f[keys[0]]); labels=np.array(f[keys[1]])

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    names=[el.decode('utf-8')for el in f[keys[2]]]

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    f.close()

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N=labels.shape[0]; n=int(.1*N); shuffle_ids=np.arange(N)

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np.random.RandomState(123).shuffle(shuffle_ids)

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images=images[shuffle_ids]; labels=labels[shuffle_ids]

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x_test,x_valid,x_train=images[:n],images[n:2*n],images[2*n:]

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y_test,y_valid,y_train=labels[:n],labels[n:2*n],labels[2*n:]

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df=pd.DataFrame(

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    [[x_train.shape,x_valid.shape,x_test.shape],

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     [x_train.dtype,x_valid.dtype,x_test.dtype],

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     [y_train.shape,y_valid.shape,y_test.shape],

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     [y_train.dtype,y_valid.dtype,y_test.dtype]],

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    columns=['train','valid','test'],

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    index=['image shape','image type','label shape','label type'])

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display(df)

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imgs=np.vstack([interpolate_hypersphere(x_train[0],x_train[1]),

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                interpolate_hypersphere(x_train[1],x_train[0])])

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animate([matrix_plot(imgs[i],figsize=(3,3),frame=False) 

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         for i in range(120)])

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Image Data & Styling

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hub_path='https://tfhub.dev/google/magenta/'+\

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         'arbitrary-image-stylization-v1-256/1'

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img_path='https://raw.githubusercontent.com/'+\

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         'OlgaBelitskaya/data/main/'

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folder_path=['flowers/','patterns/','urban/']

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img_file=['12_001.png','00_00_001.png','01_00_001.png']

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for i in range(3):

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    input_file=urllib.request.urlopen(

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        img_path+folder_path[i]+img_file[i])

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    output_file=open(img_file[i],'wb')

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    output_file.write(input_file.read())

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    output_file.close(); input_file.close()

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hub_module=th.load(hub_path)  

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original_img=load_img(img_file[0])

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style_img=load_img(img_file[1])

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stylized_img=hub_module(

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    tf.constant(original_img),tf.constant(style_img))[int(0)]

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tensor_to_image(stylized_img)

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Image Data & Object Recognition

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from tensorflow.keras.applications import vgg19,VGG19

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content_img=load_img(img_file[2])

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x=vgg19.preprocess_input(content_img*(int(255)))

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x=tf.image.resize(x,(int(224),int(224)))

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mvgg19=VGG19(include_top=True,weights='imagenet')

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prediction_probabilities=mvgg19(x)

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predicted_top5=vgg19.decode_predictions(

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    prediction_probabilities.numpy())[int(0)]

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tensor_to_image(content_img)

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table([[class_name,prob] 

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       for (number,class_name,prob) in predicted_top5])

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Artificial Data & Interpolation

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def randi(nmin,nmax): return np.random.randint(nmin,nmax)

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def randch(a): return np.random.choice(a,1)[0]

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def randcoef():

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    a=(.5+.1**6*randi(1,999999))*randch([-1,1])

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    b=randi(3,12)

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    c=.001*randi(1,99)*randch([-1,1])

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    return a,b,c

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def randcoord(a,b,c):

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    t=np.arange(0,36*np.pi,1/3600)

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    fx=np.sin(t/6)+a*np.sin(b*t)*np.cos(t)-c*np.sin(16*b*t)

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    fy=np.cos(t/6)+a*np.sin(b*t)*np.sin(t)-c*np.cos(16*b*t)

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    return fx,fy

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def randcol(): return [np.random.random(3)]

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def randplot(idx,fig_size):

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    a,b,c=randcoef(); fx,fy=randcoord(a,b,c)

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    pl.figure(figsize=(fig_size,fig_size))

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    pl.scatter(fx,fy,s=.1**4,c=randcol())

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    pl.axis('off'); pl.tight_layout(pad=.05)

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    fn='%03d'%idx+'.png'

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    pl.savefig(fn); pl.close();

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    img=tf.io.read_file(fn)

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    img=tf.image.decode_image(img,channels=int(3))

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    img=tf.image.convert_image_dtype(img,tf.float32)

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    return img

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img1=randplot(1,4); img2=randplot(2,4)

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imgs=np.vstack([interpolate_hypersphere(img1,img2),

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                interpolate_hypersphere(img2,img1)])

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animate([matrix_plot(imgs[i],figsize=(3,3),frame=False) 

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         for i in range(120)])

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