How to visualize the encoded state of an autoencoder with Keras?

How to visualize the encoded state of an autoencoder with Keras?

Autoencoders are special types of neural networks which learn to convert inputs into lower-dimensional form, after which they convert it back into the original or some related output. A variety of interesting applications has emerged for them: denoising, dimensionality reduction, input reconstruction, and – with a particular type of autoencoder called Variational Autoencoder – even generative actions.

This lower-dimensional form is also called the encoded state. But what does it look like? Can we visualize the encoded state of an autoencoder? And how do we do that for a Keras model? Those are questions that I’ll answer in this blog post. Firstly, we’ll dive into the concept of an autoencoder, to recap – or to inject – your high-level understanding of them. Next, we’ll take a look at what we’re going to do today, i.e., generate visualizations of the encoded state. Subsequently, we provide an example for encoded state visualization with both the Keras Functional API and the Keras Sequential API.

Are you ready? Let’s go! ๐Ÿ˜Ž


Recap: what are autoencoders and what are they used for?

We talk about encoded states and autoencoders – but what are they?

Likely, you already know what is meant with these concepts. In that case, I’d suggest that you skip this section and start at the next one.

However, it’s also possible that you don’t know yet what they are, or that your familiarity with these concepts is growing – but that you can’t dream them yet ๐Ÿ˜ด

In that case, read on ๐Ÿ™‚

At a high level, this is an autoencoder:

It’s a combination of building blocks which allows you to feed it some input, which is encoded, decoded and then returned to the user. Depending on how you configure it (in terms of input and desired outputs), they can be used for:

Besides the input image and the ‘reconstructed image’ (or denoised image), there are more building blocks: an encoder, a decoder and an encoded state.

What are they?

Let’s take at the encoder first. When you feed an autoencoder input, the input has multiple dimensions. For example, a 28 x 28 pixels input image has 28 x 28 = 784 dimensions, which all take a real value (i.e., a number with some decimals that can be positive and negative). The encoder reduces the dimensionality of your input to the extent that it can be overseen by compressing information and discarding useless information (e.g. sampling noise), to e.g. 25 dimensions. This is called the encoded state, which you can feed to the decoder. The decoder, in turn, attempts to build up a new image, such as a reconstruction of the input. How good the autoencoder works is determined by the loss function with which it is trained, and is determined by how similar the output is to the input.

An important word in the previous paragraph is trained. Indeed, the encoder and decoder segments of autoencoders must be trained. Usually, neural networks are employed for this purpose, as they are universal function approximators and can by consequence learn the mapping from input to encoded state, and from encoded state to reconstruction. This is really important to remember when you’re talking about autoencoders. While they are usually associated with neural networks, they’re not the same. Rather, they are implemented with neural nets.


Visualizing the encoded state: what we want to achieve

You may now wonder: what’s the goal of this blog post?

It’s to visualize the encoded state, when a sample is fed to the autoencoder.

This can be useful in situations when you use autoencoders for dimensionality reduction, and you consider the encoded states to be features for e.g. Support Vector Machines. In those cases, you may wish to look at your encoded states in order to find whether dimensions must be added or dropped, and so on. In other words, you wish to generate images like this one:

Fortunately, this is possible! ๐ŸŽ‰

Keract (link to their GitHub) is a nice toolkit with which you can “get the activations (outputs) and gradients for each layer of your Keras model” (Rรฉmy, 2019). We already covered Keract before, in a blog post illustrating how to use it for visualizing the hidden layers in your neural net, but we’re going to use it again today.

This time, we’ll be using it to visualize the encoded state – which, in terms of the neural network implementation of your autoecoder, is nothing else than a visualization of the output of the encoder segment, i.e. the final layer of the neural network segment that represents your autoencoder’s encoder.

Let’s find out how we can do that, with both the Keras Functional API and the Keras Sequential API! ๐Ÿ˜€

What you’ll need to run the model

Today, we create two variants of one autoencoder: one with the Keras Functional API, and one with the Keras Sequential API. This allows you to choose what fits best for you, and use code for both scenarios in your machine learning projects.

Regardless of the API that we will use, you need to install a set of software dependencies if you wish to run the autoencoders successfully in your local system:

  • Keras, which is the deep learning framework we will use today.
  • Python, which is the language in which Keras models are created. Preferably, use Python 3.6 or newer.
  • One of the backends supported by Keras, and since the deep integration with TensorFlow (since v2.0) I’d say that Tensorflow is the best choice.
  • Keract, for visualizing the encoded state: pip install keract.
  • Matplotlib, for plotting the visualizations on screen.

Are you ready? Lets go! ๐Ÿ˜Ž


Visualizing encoded state with a Keras Functional API autoencoder

We’ll first take a look at how encoded states can be visualized when building autoencoders with the Keras Functional API. While it’s a bit harder to structure your models, it allows easier access to intermediate layers compared to the Keras Sequential API.

And access to these intermediate layers is what we need. Remember that autoencoders contain an encoder segment, as well as a decoder segment, which are trained together but have separate tasks. Additionally, the autoencoder must be considered as a whole. Separating between layers and/or segments is thus necessary when creating autoencoders. While this is certainly possible with the Sequential API (as we will show later in this blog post), you’ll make your life easier when you use the Functional API.

In order to start, open up your Explorer/Finder, navigate to some folder, and create a Python file, e.g. autoencoder_encodedstate_functional.py. Next, open this file in your code editor. Now, we can start!

Model imports

The first thing we must do is import the dependencies:

'''
  Visualizing the encoded state of a simple autoencoder created with the Keras Functional API
  with Keract.
'''
import keras
from keras.layers import Input, Dense
from keras.datasets import mnist
from keras.models import Model
from keract import get_activations, display_activations
import matplotlib.pyplot as plt

We’ll need the Input and Dense layers today: Input for serving as the input layer of the neural network implementing our autoencoder, Dense as the hidden layer that generates the encoding. With the mnist dataset, we’ll train our autoencoder, and use the Model instance to instantiate our models later on. Additionally, we’ll need some functions from keract, and need to import the Matplotlib PyPlot API as plt.

Model configuration

Next up: model configuration. As usual, we’ll define the width and height of our images, and multiply them in order to define our initial dimension (which is 28 x 28 = 784 pixels). Batch size is set to 128, which is default, and the number of epochs is kept low deliberately (as the model seems to converge quite quickly). 20% of the training data is used for validation purposes, verbosity mode is set to True (i.e., all output is shown on screen), and our encoder reduces the dimensionality to 50 (from 784).

# Model configuration
img_width, img_height = 28, 28
initial_dimension = img_width * img_height
batch_size = 128
no_epochs = 10
validation_split = 0.2
verbosity = 1
encoded_dim = 50

Data loading and preprocessing

Now, it’s time to load data and preprocess it.

First, we use the built-in Keras functionality for loading the MNIST dataset: mnist.load_data(). This automatically downloads the data from some S3 endpoint and puts it into local cache, which allows you to use it without any difficulty.

Secondly, we’ll reshape the data into our initial_dimension (i.e., from (28, 28, 1) format – 28 width, 28 height and 1 channel – into 784 (everything merged together). We finally represent our new shape in input_shape.

Subsequently, we parse the numbers in our data as floats – specifically, float32 – which presumably speeds up the training process.

Finally, we normalize the data, so that it’s in the [-1, +1] range. This is appreciated by the neural network during training.

# Load MNIST dataset
(input_train, target_train), (input_test, target_test) = mnist.load_data()

# Reshape data
input_train = input_train.reshape(input_train.shape[0], initial_dimension)
input_test = input_test.reshape(input_test.shape[0], initial_dimension)
input_shape = (initial_dimension, )

# Parse numbers as floats
input_train = input_train.astype('float32')
input_test = input_test.astype('float32')

# Normalize data
input_train = input_train / 255
input_test = input_test / 255

Defining the layers of the autoencoder

Next, we can define the layers that we will use in the autoencoder.

The inputs layer does what you think it does: it serves to “take in” the input following the input_shape determined before.

Recognizable by the (inputs) code in the next layer, we can tell that it’s fed to the encoding_layer, which is a densely-connected layer with encoded_dim (= 50, in our case) neurons, ReLU activation, and by consequence He init.

The decoding layer, which takes the input from the encoding layer, is once again densely-connected. Its output equals the initial_dimension, which results in the same shape as we fed it in the first place. It activates with a Sigmoi activation function, so that we can use binary crossentropy for computing loss (as we will see next).

# Define the layers
inputs = Input(shape=input_shape)
encoding_layer = Dense(encoded_dim, activation='relu', kernel_initializer='he_normal')(inputs)
decoding_layer = Dense(initial_dimension, activation='sigmoid')(encoding_layer)

Intermezzo: definitions versus instantiations

Above, we defined the layers of our autoencoder:

  • The input layer;
  • The encoding layer;
  • The decoding layer.

We also told Keras in our code how information should flow with these layers: from the input layer, through the encoding layer, through the decoding layer, to become output again.

However, we don’t have a model yet. It’s important to understand that even though we defined the architecture of the model, we haven’t actually got a model instance yet. We’ll create one now ๐Ÿ™‚

Instantiating the full autoencoder and encoder/decoder segments

Now that we have defined out layers, we can instantiate them, i.e. convert them to true Model instances.

Full autoencoder

First, for the full autoencoder, we essentially tell the Model that we’ll wish to create one with inputs (the inputs layer) as the starting layer, and decoding_layer (the output of the decoder) as the full layer.

# Instantiate the autoencoder
autoencoder = Model(inputs, decoding_layer, name='full_autoencoder')

Encoder segment

For the encoder, we only want the flow to go from inputs to encoding_layer:

# Instantiate the encoder
encoder = Model(inputs, encoding_layer, name='encoder')

Decoder segment

For the decoder, well have to take a slightly different approach. Contrary to the decoder, which is not dependent on anything, the decoder is dependent on the learnt encoder. Hence, we’ll need to include it in our code: simply adding the decoding_layer here would suggest that we split encoder and decoder.

Hence, we’ll first define a new pseudo-Input layer, which takes inputs with shape (encoded_dim, ). Next, we retrieve the final (i.e., the decoding) layer from our instantiated autoencoder. This layer is used in the decoder model, which has the encoded_input as its input (which makes sense) and final_ae_layer(encoded_input) as output. Although being slightly more difficult, this makes sense as well: it’s just the encoded input fed to the decoder layer we already trained before.

# Instantiate the decoder
encoded_input = Input(shape=(encoded_dim, ))
final_ae_layer = autoencoder.layers[-1]
decoder = Model(encoded_input, final_ae_layer(encoded_input), name='decoder')

Compile autoencoder & encoder segment

Next, we can compile both the autoencoder and the encoder segment. We’ll need to compile them both, as we’ll use them later to generate an image of input and reconstructed output (hence we need autoencoder) and visualize the encoded state (hence we need encoder). If you only need one of them, it’s of course fine to drop any of them.

We’ll use the Adam optimizer and binary crossentropy, pretty much the standard settings for today’s networks with Sigmoid activation functions.

# Compile the autoencoder
encoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')

It’s now time to get some insights about the structure of our models as well.

To achieve this, we generate summaries:

# Give us some insights
autoencoder.summary()
encoder.summary()
decoder.summary()

Fitting data

Finally, we’ll fit the data – our input_train serves both as features and as targets, specify the number of epochs and batch size as configured before, and do the same for the validation split.

# Fit data
autoencoder.fit(input_train, input_train, epochs=no_epochs, batch_size=batch_size, validation_split=validation_split)

Almost ready!

Visualizing a sample and reconstruction

The only thing left is generating some visualizations, which is why you’re here in the first place.

The first thing to add is taking a sample from the testing dataset, which you’ll feed to the autoencoder (i.e. the encoder and decoder segments) in order to retrieve a reconstruction.

# =============================================
# Take a sample for visualization purposes
# =============================================
input_sample = input_test[:1]
reconstruction = autoencoder.predict([input_sample])

The next thing we do is plot this reconstruction together with the input_sample. This allows us to see how well the autoencoder performs:

# =============================================
# Visualize input-->reconstruction
# =============================================
fig, axes = plt.subplots(1, 2)
fig.set_size_inches(6, 3.5)
input_sample_reshaped = input_sample.reshape((img_width, img_height))
reconsstruction_reshaped = reconstruction.reshape((img_width, img_height))
axes[0].imshow(input_sample_reshaped) 
axes[0].set_title('Original image')
axes[1].imshow(reconsstruction_reshaped)
axes[1].set_title('Reconstruction')
plt.show()

Visualizing the encoded state

Next, we can visualize the encoded state. As said, we’ll use Keract for this, which allows you to visualize activations in neural network layers made with Keras. Since it abstracts much of the coding away, it’s actually really easy to generate these visualizations:

# =============================================
# Visualize encoded state with Keract
# =============================================
activations = get_activations(encoder, input_sample)
display_activations(activations, cmap="gray", save=False)

The steps are simple:

  • Get the activations for the encoder model (we use the encoder because we want to visualize the encoded state!) from the input_sample we took before.
  • Next, display these activations with display_activations, using the gray colormap and without saving them.

That’s it!

Full model code

Should you wish to copy the full model code at once, here you go ๐Ÿ˜€

'''
  Visualizing the encoded state of a simple autoencoder created with the Keras Functional API
  with Keract.
'''
import keras
from keras.layers import Input, Dense
from keras.datasets import mnist
from keras.models import Model
from keract import get_activations, display_activations
import matplotlib.pyplot as plt

# Model configuration
img_width, img_height = 28, 28
initial_dimension = img_width * img_height
batch_size = 128
no_epochs = 10
validation_split = 0.2
verbosity = 1
encoded_dim = 50

# Load MNIST dataset
(input_train, target_train), (input_test, target_test) = mnist.load_data()

# Reshape data
input_train = input_train.reshape(input_train.shape[0], initial_dimension)
input_test = input_test.reshape(input_test.shape[0], initial_dimension)
input_shape = (initial_dimension, )

# Parse numbers as floats
input_train = input_train.astype('float32')
input_test = input_test.astype('float32')

# Normalize data
input_train = input_train / 255
input_test = input_test / 255

# Define the layers
inputs = Input(shape=input_shape)
encoding_layer = Dense(encoded_dim, activation='relu', kernel_initializer='he_normal')(inputs)
decoding_layer = Dense(initial_dimension, activation='sigmoid')(encoding_layer)

# Instantiate the autoencoder
autoencoder = Model(inputs, decoding_layer, name='full_autoencoder')

# Instantiate the encoder
encoder = Model(inputs, encoding_layer, name='encoder')

# Instantiate the decoder
encoded_input = Input(shape=(encoded_dim, ))
final_ae_layer = autoencoder.layers[-1]
decoder = Model(encoded_input, final_ae_layer(encoded_input), name='decoder')

# Compile the autoencoder
encoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')

# Give us some insights
autoencoder.summary()
encoder.summary()
decoder.summary()

# Fit data
autoencoder.fit(input_train, input_train, epochs=no_epochs, batch_size=batch_size, validation_split=validation_split)

# =============================================
# Take a sample for visualization purposes
# =============================================
input_sample = input_test[:1]
reconstruction = autoencoder.predict([input_sample])

# =============================================
# Visualize input-->reconstruction
# =============================================
fig, axes = plt.subplots(1, 2)
fig.set_size_inches(6, 3.5)
input_sample_reshaped = input_sample.reshape((img_width, img_height))
reconsstruction_reshaped = reconstruction.reshape((img_width, img_height))
axes[0].imshow(input_sample_reshaped) 
axes[0].set_title('Original image')
axes[1].imshow(reconsstruction_reshaped)
axes[1].set_title('Reconstruction')
plt.show()

# =============================================
# Visualize encoded state with Keract
# =============================================
activations = get_activations(encoder, input_sample)
display_activations(activations, cmap="gray", save=False)

Results

Now, it’s time to run the model.

Open up a terminal, cd to the folder where your .py file is located, and run it: e.g. python autoencoder_encodedstate_functional.py. You should see the training process begin, and once it finishes, some visualizations should start popping up.

The results are pretty awesome.

This is the first visualization we’ll get:

As you can see, the MNIST dataset is so discriminative that even with a simple structure (Dense layers only!), the autoencoder is capable of generating accurate reconstructions ๐Ÿ˜Š

Next, the visualization of the encoded state – which is, once again, why you’re here. Here you go:

Looks good to me! ๐Ÿ˜Ž


Visualizing encoded state with a Keras Sequential API autoencoder

Visualizing the encoded state of an autoencoder created with the Keras Sequential API is a bit harder, because you don’t have as much control over the individual layers as you’d like to have.

However, it’s possible nevertheless ๐Ÿ™‚

Open up your Explorer/Finder again, navigate to some folder, and create another Python file: autoencoder_encodedstate_sequential.py. Now open this file in your code editor. We can start coding again ๐Ÿ™‚

Model imports

First, we’ll add the imports:

'''
  Visualizing the encoded state of a simple autoencoder created with the Keras Sequential API
  with Keract.
'''
import keras
from keras.layers import Dense
from keras.datasets import mnist
from keras.models import Sequential
from keract import get_activations, display_activations
import matplotlib.pyplot as plt
from keras import backend as K

We use Keras for creating the deep learning model. From Keras, we’ll import the densely-connected layer, the MNIST dataset – just as we did before. However, this time, instead of the Model container, we import the Sequential API. This allows us to stack the layers easily.

Next, we import some calls from Keract which we can use to generate the visualizations. This is followed by the Matplotlib PyPlot API, which we’ll use to plot the visualizations generated with Keract. Finally, we import the Keras backend – which we’ll use to get the intermediate layer (from the encoder) later on.

Model configuration

Next, we specify the configuration of our model:

# Model configuration
img_width, img_height = 28, 28
initial_dimension = img_width * img_height
batch_size = 128
no_epochs = 10
validation_split = 0.2
verbosity = 1
encoded_dim = 50

It’s equal to the model configuration for the model created with the Functional API.

Import & process dataset

The same is valid for importing and preprocessing the data: it’s equal to using the Functional API. First, we import the data, reshape it into the correct format, parse the data as floats (to speed up the training process) and normalize it to the [-1, +1] range.

# Load MNIST dataset
(input_train, target_train), (input_test, target_test) = mnist.load_data()

# Reshape data
input_train = input_train.reshape(input_train.shape[0], initial_dimension)
input_test = input_test.reshape(input_test.shape[0], initial_dimension)
input_shape = (initial_dimension, )

# Parse numbers as floats
input_train = input_train.astype('float32')
input_test = input_test.astype('float32')

# Normalize data
input_train = input_train / 255
input_test = input_test / 255

Defining the full autoencoder

We next define the autoencoder in full. We do so by assigning a new instance of the Sequential model type to the autoencoder variable. Subsequently, we’ll add a densely-connected layer that has encoded_dim outputs i.e. neurons – and thus learns the encoded state.

It makes use of ReLU activation and by consequence He initialization. The input_shape is defined as well as with the Sequential API, the input layer is defined under the hood.

Next, we add another densely-connected layer, which converts the representation of encoded_dim dimensionality back into the initial_dimension, and thus serves as the decoder segment. It makes use of Sigmoid activation in order to allow us to use binary crossentropy loss.

# Define the 'autoencoder' full model
autoencoder = Sequential()
autoencoder.add(Dense(encoded_dim, activation='relu', kernel_initializer='he_normal', input_shape=input_shape))
autoencoder.add(Dense(initial_dimension, activation='sigmoid'))

Compilation & fitting data

Next up: compiling the model (using the default Adam optimizer and binary crossentropy loss), outputting a summary, and fitting the data. Note that, as usual with an autoencoder, we feed input_train both as features and as targets. We train the model as specified in the configuration in terms of number of epochs, batch size and validation split.

# Compile the autoencoder
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')

# Give us some insights
autoencoder.summary()

# Fit data
autoencoder.fit(input_train, input_train, epochs=no_epochs, batch_size=batch_size, validation_split=validation_split)

Visualizing a sample

We next take a sample, generate a reconstruction using the trained model and visualize it using Matplotlib. This code is no different compared to the one used with the Functional API.

# =============================================
# Take a sample for visualization purposes
# =============================================
input_sample = input_test[:1]
reconstruction = autoencoder.predict([input_sample])

# =============================================
# Visualize input-->reconstruction
# =============================================
fig, axes = plt.subplots(1, 2)
fig.set_size_inches(6, 3.5)
input_sample_reshaped = input_sample.reshape((img_width, img_height))
reconsstruction_reshaped = reconstruction.reshape((img_width, img_height))
axes[0].imshow(input_sample_reshaped) 
axes[0].set_title('Original image')
axes[1].imshow(reconsstruction_reshaped)
axes[1].set_title('Reconstruction')
plt.show()

Visualizing the encoded state

Next, we visualize the encoded state … and here is one difference compared to the Functional API.

Since Keras does not allow us to consider layers and their outputs easily (and to make it compatible with add-ons like Keract), we’ll have to feed Keract the entire autoencoder instance. This means that you will get visualizations for both the encoder and the decoder segment (the latter of which is simply the output that is visualized later, but has to be reshaped yet into 28 x 28 pixels format). It’s unfortunate, but it’s how we’ll have to do it. The silver lining: there’s not much to visualize here, so it won’t take you a lot of extra time ๐Ÿ™‚

# =============================================
# Visualize encoded state with Keract
# =============================================
activations = get_activations(autoencoder, input_sample)
display_activations(activations, cmap="gray", save=False)

Now, we’re ready! Time to start up your terminal again, cd into the folder where your Sequential API model is stored, and run python autoencoder_encodedstate_sequential.py. Training should begin and visualizations should pop up once it finishes. Let’s go! ๐Ÿ˜Ž

Full model code

Should you wish to obtain the full code for the Sequential version at once, here you go:

'''
  Visualizing the encoded state of a simple autoencoder created with the Keras Sequential API
  with Keract.
'''
import keras
from keras.layers import Dense
from keras.datasets import mnist
from keras.models import Sequential
from keract import get_activations, display_activations
import matplotlib.pyplot as plt
from keras import backend as K

# Model configuration
img_width, img_height = 28, 28
initial_dimension = img_width * img_height
batch_size = 128
no_epochs = 10
validation_split = 0.2
verbosity = 1
encoded_dim = 50

# Load MNIST dataset
(input_train, target_train), (input_test, target_test) = mnist.load_data()

# Reshape data
input_train = input_train.reshape(input_train.shape[0], initial_dimension)
input_test = input_test.reshape(input_test.shape[0], initial_dimension)
input_shape = (initial_dimension, )

# Parse numbers as floats
input_train = input_train.astype('float32')
input_test = input_test.astype('float32')

# Normalize data
input_train = input_train / 255
input_test = input_test / 255

# Define the 'autoencoder' full model
autoencoder = Sequential()
autoencoder.add(Dense(encoded_dim, activation='relu', kernel_initializer='he_normal', input_shape=input_shape))
autoencoder.add(Dense(initial_dimension, activation='sigmoid'))

# Compile the autoencoder
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')

# Give us some insights
autoencoder.summary()

# Fit data
autoencoder.fit(input_train, input_train, epochs=no_epochs, batch_size=batch_size, validation_split=validation_split)

# =============================================
# Take a sample for visualization purposes
# =============================================
input_sample = input_test[:1]
reconstruction = autoencoder.predict([input_sample])

# =============================================
# Visualize input-->reconstruction
# =============================================
fig, axes = plt.subplots(1, 2)
fig.set_size_inches(6, 3.5)
input_sample_reshaped = input_sample.reshape((img_width, img_height))
reconsstruction_reshaped = reconstruction.reshape((img_width, img_height))
axes[0].imshow(input_sample_reshaped) 
axes[0].set_title('Original image')
axes[1].imshow(reconsstruction_reshaped)
axes[1].set_title('Reconstruction')
plt.show()

# =============================================
# Visualize encoded state with Keract
# =============================================
activations = get_activations(autoencoder, input_sample)
display_activations(activations, cmap="gray", save=False)

Results

Time for some results ๐Ÿ™‚

As with the Functional API version, the Sequential API based autoencoder learns to reconstruct the inputs pretty accurately. Additionally, you’ll also get a visualization of the encoded state:

This time, as indicated before, you’ll also get an extra visualization – being what is output by the decoder before it’s reshaped into 28 x 28 pixels format. It’s simply the way Keract works, and given the relative inflexibility of the Sequential API there’s not much we can do about it.

Mission achieved! ๐ŸŽ‰


Summary

In this blog post, we’ve seen how the encoded state of an autoencoder created with Keras can be visualized. We first looked at the concepts behind autoencoders, and how they can be implemented by using neural networks. This included an analysis of the concept ‘encoded state’ and how autoencoders learn it.

In order to demonstrate how it works, we created an example with Keras which contains two densely-connected (Dense) autoencoders being trained on the MNIST dataset. The two, one of which is created with the Functional API and the other with the Sequential API, learnt to reconstruct the MNIST digits pretty accurately. With Keract, we finally visualized the encoded state with only a few lines of code.

I hope that you’ve learnt something new today ๐Ÿ˜€ Please let me know what you think by dropping a comment in the comments section below ๐Ÿ‘ Thank you for reading MachineCurve today and happy engineering! ๐Ÿ˜Ž


References

Rรฉmy, P. (2019, November 28). Keract. Retrieved from https://github.com/philipperemy/keract

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