Category: Misc

I published a tutorial where I explain how to save an
AutoEncoder with Python + Keras. In particular, in this video
you’ll learn how to save/load the Autoencoder class parameters
with pickle and the model weights with methods native to the Keras
API.

This video is part of a series called “Generating Sound with
Neural Networks”. In this series, you’ll learn how to generate
sound from audio files and spectrograms 🎧 🎧 using Variational
Autoencoders 🤖 🤖

Here’s the video:

https://www.youtube.com/watch?v=UIC0Irq-Eok&list=PL-wATfeyAMNpEyENTc-tVH5tfLGKtSWPp&index=7

Version control is used to keep track of modifications made in a
software code. Similarly, when building machine learning (ML)
systems, it is essential to track things, such as the datasets used
to train the model, the hyperparameters and pipeline used, the
version of tensorflow used to create the model, and many more.

ML artifacts’ history and lineage are very complicated than a
simple, linear log. Git can be used to track the code to one
extent, but we need something to track your models, datasets, and
more. The complexity of ML code and artifacts like models,
datasets, and much more requires a similar approach.

Article:
https://www.marktechpost.com/2021/01/12/machine-learning-metadata-mlmd-a-library-to-track-full-lineage-of-machine-learning-workflow/

Github: https://github.com/google/ml-metadata

def load_data(dir_list, image_size):

X = []

Y = []

image_width, image_height = image_size

for directory in dir_list:

for filename in listdir(directory):

image = cv2.imread(directory + ‘//’ + filename)

imgres = resize(image, (240,240,3))

img_resized = cv2.resize(imgres, dsize = (image_width,
image_height),interpolation=cv2.INTER_CUBIC)

X.append(image)

if directory[-3:] == ‘yes’:

Y.append([1])

else:

Y.append([0])

X = np.array(X)

Y = np.array(Y)

X, Y = shuffle(X, Y)

print(f’Number of examples is: {len(X)}’)

print(f’X shape is: {X.shape}’)

print(f’y shape is: {Y.shape}’)

return X, Y

———————————————–

yes = ‘yes’

no = ‘no’

​

IMG_WIDTH, IMG_HEIGHT = (240, 240)

​

X, Y = load_data([yes, no], (IMG_WIDTH, IMG_HEIGHT))

——————————————————–

​

OUTPUT:

Number of examples is: 253

X shape is: (253,)

Y shape is: (253, 1)

​

——————————————————-

​

The X-Shape should be (253,240,240,3), however, I do not know
why it is missing the other numbers. Thank you for helping.

Quick question, so, i’ve got some model (an autoencoder to be
exact) written in a sequential manner and let’s say it looks
something like this

input = keras.Input() conv = layers.Conv2D()(input) flatten = layers.Flatten()(conv) dense = layers.Dense()(flatten) out = layers.Dense()(dense) model = keras.Model(inputs=input, outputs=out) model.compile() 

and let’s say i’ve trained that model. If i then use some layers
of that model to make another one like this

new_model = keras.Model(inputs=input, outputs=conv) 

will that new model be already trained? I guess it poses more
global question, does keras.Model() create separate object which
uses those layers vairables you’ve written just to know the
structure, like a Class description or does it actually acts upon
those variables during actions like .fit()?