# Variational AutoEncoders

Variational autoencoder was proposed in 2013 by Knigma and Welling at Google and Qualcomm. A variational autoencoder (VAE) provides a probabilistic manner for describing an observation in latent space. Thus, rather than building an encoder that outputs a single value to describe each latent state attribute, we’ll formulate our encoder to describe a probability distribution for each latent attribute.

It has many applications such as data compression, synthetic data creation etc.

Architecture:

Autoencoders are a type of neural network that learns the data encodings from the dataset in an unsupervised way. It basically contains two parts: the first one is an encoder which is similar to the convolution neural network except for the last layer. The aim of the encoder to learn efficient data encoding from the dataset and pass it into a bottleneck architecture. The other part of the autoencoder is a decoder that uses latent space in the bottleneck layer to regenerate the images similar to the dataset. These results backpropagate from the neural network in the form of the loss function.

Variational autoencoder is different from autoencoder in a way such that it provides a statistic manner for describing the samples of the dataset in latent space. Therefore, in variational autoencoder, the encoder outputs a probability distribution in the bottleneck layer instead of a single output value.

Mathematics behind variational autoencoder:

Variational autoencoder uses KL-divergence as its loss function, the goal of this is to minimize the difference between a supposed distribution and original distribution of dataset.

Suppose we have a distribution z and we want to generate the observation x from it.  In other words, we want to calculate

We can do it by following way:

But, the calculation of p(x) can be quite difficult

This usually makes it an intractable distribution. Hence, we need to approximate p(z|x) to q(z|x) to make it a tractable distribution. To better approximate p(z|x) to q(z|x), we will minimize the KL-divergence loss which calculates how similar two distributions are:

By simplifying, the above minimization problem is equivalent to the following maximization problem :

The first term represents the reconstruction likelihood and the other term ensures that our learned distribution q is similar to the true prior distribution p.

Thus our total loss consists of two terms, one is reconstruction error and other is KL-divergence loss:

Implementation:

In this implementation, we will be using the Fashion-MNIST dataset, this dataset is already available in keras.datasets API, so we don’t need to add or upload manually.

• First, we need to import the necessary packages to our python environment. we will be using Keras package with tensorflow as a backend.

Code:

## python3

 # code import numpy as np import tensorflow as tf from tensorflow import keras from tensorflow.keras import Input, Model from tensorflow.keras.layers import Layer, Conv2D, Flatten, Dense, Reshape, Conv2DTranspose import matplotlib.pyplot as plt

• For variational autoencoders, we need to define the architecture of two parts encoder and decoder but first, we will define the bottleneck layer of architecture, the sampling layer.

Code:

## python3

 # this sampling layer is the bottleneck layer of variational autoencoder, # it uses the output from two dense layers z_mean and z_log_var as input,  # convert them into normal distribution and pass them to the decoder layer class Sampling(Layer):       def call(self, inputs):         z_mean, z_log_var = inputs         batch = tf.shape(z_mean)[0]         dim = tf.shape(z_mean)[1]         epsilon = tf.keras.backend.random_normal(shape =(batch, dim))         return z_mean + tf.exp(0.5 * z_log_var) * epsilon

• Now, we define the architecture of encoder part of our autoencoder, this part takes images as input and encodes their representation in the Sampling layer.

Code:

## python3

 # Define Encoder Model latent_dim = 2   encoder_inputs = Input(shape =(28, 28, 1)) x = Conv2D(32, 3, activation ="relu", strides = 2, padding ="same")(encoder_inputs) x = Conv2D(64, 3, activation ="relu", strides = 2, padding ="same")(x) x = Flatten()(x) x = Dense(16, activation ="relu")(x) z_mean = Dense(latent_dim, name ="z_mean")(x) z_log_var = Dense(latent_dim, name ="z_log_var")(x) z = Sampling()([z_mean, z_log_var]) encoder = Model(encoder_inputs, [z_mean, z_log_var, z], name ="encoder") encoder.summary()

Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to
==================================================================================================
input_3 (InputLayer)            [(None, 28, 28, 1)]  0
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 14, 14, 32)   320         input_3[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 7, 7, 64)     18496       conv2d_2[0][0]
__________________________________________________________________________________________________
flatten_1 (Flatten)             (None, 3136)         0           conv2d_3[0][0]
__________________________________________________________________________________________________
dense_2 (Dense)                 (None, 16)           50192       flatten_1[0][0]
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 2)            34          dense_2[0][0]
__________________________________________________________________________________________________
z_log_var (Dense)               (None, 2)            34          dense_2[0][0]
__________________________________________________________________________________________________
sampling_1 (Sampling)           (None, 2)            0           z_mean[0][0]
z_log_var[0][0]
==================================================================================================
Total params: 69, 076
Trainable params: 69, 076
Non-trainable params: 0
__________________________________________________________________________________________________
• Now, we define the architecture of decoder part of our autoencoder, this part takes the output of the sampling layer as input and output an image of size (28, 28, 1) .

Code:

## python3

 # Define Decoder Architecture latent_inputs = keras.Input(shape =(latent_dim, )) x = Dense(7 * 7 * 64, activation ="relu")(latent_inputs) x = Reshape((7, 7, 64))(x) x = Conv2DTranspose(64, 3, activation ="relu", strides = 2, padding ="same")(x) x = Conv2DTranspose(32, 3, activation ="relu", strides = 2, padding ="same")(x) decoder_outputs = Conv2DTranspose(1, 3, activation ="sigmoid", padding ="same")(x) decoder = Model(latent_inputs, decoder_outputs, name ="decoder") decoder.summary()

Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
input_4 (InputLayer)         [(None, 2)]               0
_________________________________________________________________
dense_3 (Dense)              (None, 3136)              9408
_________________________________________________________________
reshape_1 (Reshape)          (None, 7, 7, 64)          0
_________________________________________________________________
conv2d_transpose_3 (Conv2DTr (None, 14, 14, 64)        36928
_________________________________________________________________
conv2d_transpose_4 (Conv2DTr (None, 28, 28, 32)        18464
_________________________________________________________________
conv2d_transpose_5 (Conv2DTr (None, 28, 28, 1)         289
=================================================================
Total params: 65, 089
Trainable params: 65, 089
Non-trainable params: 0
_________________________________________________________________
• In this step, we combine the model and define the training procedure with loss functions.

Code:

## python3

 # this class takes encoder and decoder models and # define the complete variational autoencoder architecture class VAE(keras.Model):     def __init__(self, encoder, decoder, **kwargs):         super(VAE, self).__init__(**kwargs)         self.encoder = encoder         self.decoder = decoder       def train_step(self, data):         if isinstance(data, tuple):             data = data[0]         with tf.GradientTape() as tape:             z_mean, z_log_var, z = encoder(data)             reconstruction = decoder(z)             reconstruction_loss = tf.reduce_mean(                 keras.losses.binary_crossentropy(data, reconstruction)             )             reconstruction_loss *= 28 * 28             kl_loss = 1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)             kl_loss = tf.reduce_mean(kl_loss)             kl_loss *= -0.5             total_loss = reconstruction_loss + kl_loss         grads = tape.gradient(total_loss, self.trainable_weights)         self.optimizer.apply_gradients(zip(grads, self.trainable_weights))         return {             "loss": total_loss,             "reconstruction_loss": reconstruction_loss,             "kl_loss": kl_loss,         }

• Now it’s the right time to train our variational autoencoder model, we will train it for 100 epochs.  But first we need to import the fashion MNIST dataset.

Code:

## python3

 # load fashion mnist dataset  from  keras.dataset API (x_train, _), (x_test, _) = keras.datasets.fashion_mnist.load_data() fmnist_images = np.concatenate([x_train, x_test], axis = 0) # expand dimension to add  a color map dimension fmnist_images = np.expand_dims(fmnist_images, -1).astype("float32") / 255   # compile and train the model vae = VAE(encoder, decoder) vae.compile(optimizer ='rmsprop') vae.fit(fmnist_images, epochs = 100, batch_size = 64)

Epoch 1/100
1094/1094 [==============================] - 7s 6ms/step - loss: 301.9441 - reconstruction_loss: 298.3138 - kl_loss: 3.6303
Epoch 2/100
1094/1094 [==============================] - 7s 6ms/step - loss: 273.5940 - reconstruction_loss: 270.0484 - kl_loss: 3.5456
Epoch 3/100
1094/1094 [==============================] - 7s 6ms/step - loss: 269.3337 - reconstruction_loss: 265.9077 - kl_loss: 3.4260
Epoch 4/100
1094/1094 [==============================] - 7s 6ms/step - loss: 266.8168 - reconstruction_loss: 263.4100 - kl_loss: 3.4068
Epoch 5/100
1094/1094 [==============================] - 7s 6ms/step - loss: 264.9917 - reconstruction_loss: 261.5603 - kl_loss: 3.4314
Epoch 6/100
1094/1094 [==============================] - 7s 6ms/step - loss: 263.5237 - reconstruction_loss: 260.0712 - kl_loss: 3.4525
Epoch 7/100
1094/1094 [==============================] - 7s 6ms/step - loss: 262.3414 - reconstruction_loss: 258.8548 - kl_loss: 3.4865
Epoch 8/100
1094/1094 [==============================] - 7s 6ms/step - loss: 261.4241 - reconstruction_loss: 257.9104 - kl_loss: 3.5137
Epoch 9/100
1094/1094 [==============================] - 7s 6ms/step - loss: 260.6090 - reconstruction_loss: 257.0662 - kl_loss: 3.5428
Epoch 10/100
1094/1094 [==============================] - 7s 6ms/step - loss: 259.9735 - reconstruction_loss: 256.4075 - kl_loss: 3.5660
Epoch 11/100
1094/1094 [==============================] - 7s 6ms/step - loss: 259.4184 - reconstruction_loss: 255.8348 - kl_loss: 3.5836
Epoch 12/100
1094/1094 [==============================] - 7s 6ms/step - loss: 258.9688 - reconstruction_loss: 255.3724 - kl_loss: 3.5964
Epoch 13/100
1094/1094 [==============================] - 7s 6ms/step - loss: 258.5413 - reconstruction_loss: 254.9356 - kl_loss: 3.6057
Epoch 14/100
1094/1094 [==============================] - 7s 6ms/step - loss: 258.2400 - reconstruction_loss: 254.6236 - kl_loss: 3.6163
Epoch 15/100
1094/1094 [==============================] - 7s 6ms/step - loss: 257.9335 - reconstruction_loss: 254.3038 - kl_loss: 3.6298
Epoch 16/100
1094/1094 [==============================] - 7s 6ms/step - loss: 257.6331 - reconstruction_loss: 253.9993 - kl_loss: 3.6339
Epoch 17/100
1094/1094 [==============================] - 7s 6ms/step - loss: 257.4199 - reconstruction_loss: 253.7707 - kl_loss: 3.6492
Epoch 18/100
1094/1094 [==============================] - 6s 6ms/step - loss: 257.1951 - reconstruction_loss: 253.5309 - kl_loss: 3.6643
Epoch 19/100
1094/1094 [==============================] - 7s 6ms/step - loss: 256.9326 - reconstruction_loss: 253.2723 - kl_loss: 3.6604
Epoch 20/100
1094/1094 [==============================] - 7s 6ms/step - loss: 256.7551 - reconstruction_loss: 253.0836 - kl_loss: 3.6715
Epoch 21/100
1094/1094 [==============================] - 7s 6ms/step - loss: 256.5663 - reconstruction_loss: 252.8877 - kl_loss: 3.6786
Epoch 22/100
1094/1094 [==============================] - 7s 6ms/step - loss: 256.4068 - reconstruction_loss: 252.7112 - kl_loss: 3.6956
Epoch 23/100
1094/1094 [==============================] - 7s 6ms/step - loss: 256.2588 - reconstruction_loss: 252.5588 - kl_loss: 3.7000
Epoch 24/100
1094/1094 [==============================] - 7s 6ms/step - loss: 256.0853 - reconstruction_loss: 252.3794 - kl_loss: 3.7059
Epoch 25/100
1094/1094 [==============================] - 7s 6ms/step - loss: 255.9321 - reconstruction_loss: 252.2201 - kl_loss: 3.7120
Epoch 26/100
1094/1094 [==============================] - 7s 6ms/step - loss: 255.7962 - reconstruction_loss: 252.0814 - kl_loss: 3.7148
Epoch 27/100
1094/1094 [==============================] - 7s 6ms/step - loss: 255.6953 - reconstruction_loss: 251.9673 - kl_loss: 3.7280
Epoch 28/100
1094/1094 [==============================] - 7s 6ms/step - loss: 255.5534 - reconstruction_loss: 251.8248 - kl_loss: 3.7287
Epoch 29/100
1094/1094 [==============================] - 7s 6ms/step - loss: 255.4437 - reconstruction_loss: 251.7134 - kl_loss: 3.7303
Epoch 30/100
1094/1094 [==============================] - 7s 6ms/step - loss: 255.3439 - reconstruction_loss: 251.6064 - kl_loss: 3.7375
Epoch 31/100
1094/1094 [==============================] - 7s 6ms/step - loss: 255.2326 - reconstruction_loss: 251.5018 - kl_loss: 3.7308
Epoch 32/100
1094/1094 [==============================] - 7s 6ms/step - loss: 255.1356 - reconstruction_loss: 251.3933 - kl_loss: 3.7423
Epoch 33/100
1094/1094 [==============================] - 7s 6ms/step - loss: 255.0660 - reconstruction_loss: 251.3224 - kl_loss: 3.7436
Epoch 34/100
1094/1094 [==============================] - 7s 6ms/step - loss: 254.9977 - reconstruction_loss: 251.2449 - kl_loss: 3.7528
Epoch 35/100
1094/1094 [==============================] - 7s 6ms/step - loss: 254.8857 - reconstruction_loss: 251.1363 - kl_loss: 3.7494
Epoch 36/100
1094/1094 [==============================] - 7s 6ms/step - loss: 254.7980 - reconstruction_loss: 251.0481 - kl_loss: 3.7499
Epoch 37/100
1094/1094 [==============================] - 7s 6ms/step - loss: 254.7485 - reconstruction_loss: 250.9851 - kl_loss: 3.7634
Epoch 38/100
1094/1094 [==============================] - 7s 6ms/step - loss: 254.6701 - reconstruction_loss: 250.9049 - kl_loss: 3.7652
Epoch 39/100
1094/1094 [==============================] - 6s 6ms/step - loss: 254.6105 - reconstruction_loss: 250.8389 - kl_loss: 3.7716
Epoch 40/100
1094/1094 [==============================] - 7s 6ms/step - loss: 254.4979 - reconstruction_loss: 250.7333 - kl_loss: 3.7646
Epoch 41/100
1094/1094 [==============================] - 7s 6ms/step - loss: 254.4734 - reconstruction_loss: 250.7037 - kl_loss: 3.7697
Epoch 42/100
1094/1094 [==============================] - 7s 6ms/step - loss: 254.4408 - reconstruction_loss: 250.6576 - kl_loss: 3.7831
Epoch 43/100
1094/1094 [==============================] - 7s 6ms/step - loss: 254.3272 - reconstruction_loss: 250.5562 - kl_loss: 3.7711
Epoch 44/100
1094/1094 [==============================] - 7s 6ms/step - loss: 254.3110 - reconstruction_loss: 250.5354 - kl_loss: 3.7755
Epoch 45/100
1094/1094 [==============================] - 7s 6ms/step - loss: 254.1982 - reconstruction_loss: 250.4256 - kl_loss: 3.7726
Epoch 46/100
1094/1094 [==============================] - 7s 6ms/step - loss: 254.1655 - reconstruction_loss: 250.3795 - kl_loss: 3.7860
Epoch 47/100
1094/1094 [==============================] - 7s 6ms/step - loss: 254.0979 - reconstruction_loss: 250.3105 - kl_loss: 3.7875
Epoch 48/100
1094/1094 [==============================] - 7s 6ms/step - loss: 254.0801 - reconstruction_loss: 250.2973 - kl_loss: 3.7828
Epoch 49/100
1094/1094 [==============================] - 7s 6ms/step - loss: 254.0101 - reconstruction_loss: 250.2270 - kl_loss: 3.7831
Epoch 50/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.9512 - reconstruction_loss: 250.1681 - kl_loss: 3.7831
Epoch 51/100
1094/1094 [==============================] - 7s 7ms/step - loss: 253.9307 - reconstruction_loss: 250.1408 - kl_loss: 3.7899
Epoch 52/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.8858 - reconstruction_loss: 250.1059 - kl_loss: 3.7800
Epoch 53/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.8118 - reconstruction_loss: 250.0236 - kl_loss: 3.7882
Epoch 54/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.8171 - reconstruction_loss: 250.0325 - kl_loss: 3.7845
Epoch 55/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.7622 - reconstruction_loss: 249.9735 - kl_loss: 3.7887
Epoch 56/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.7338 - reconstruction_loss: 249.9380 - kl_loss: 3.7959
Epoch 57/100
1094/1094 [==============================] - 6s 6ms/step - loss: 253.6761 - reconstruction_loss: 249.8792 - kl_loss: 3.7969
Epoch 58/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.6236 - reconstruction_loss: 249.8283 - kl_loss: 3.7954
Epoch 59/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.6181 - reconstruction_loss: 249.8236 - kl_loss: 3.7945
Epoch 60/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.5509 - reconstruction_loss: 249.7587 - kl_loss: 3.7921
Epoch 61/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.5124 - reconstruction_loss: 249.7126 - kl_loss: 3.7998
Epoch 62/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.4739 - reconstruction_loss: 249.6683 - kl_loss: 3.8056
Epoch 63/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.4609 - reconstruction_loss: 249.6567 - kl_loss: 3.8042
Epoch 64/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.4066 - reconstruction_loss: 249.6020 - kl_loss: 3.8045
Epoch 65/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.3578 - reconstruction_loss: 249.5580 - kl_loss: 3.7998
Epoch 66/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.3728 - reconstruction_loss: 249.5609 - kl_loss: 3.8118
Epoch 67/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.3523 - reconstruction_loss: 249.5351 - kl_loss: 3.8171
Epoch 68/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.2646 - reconstruction_loss: 249.4452 - kl_loss: 3.8194
Epoch 69/100
1094/1094 [==============================] - 6s 6ms/step - loss: 253.2642 - reconstruction_loss: 249.4603 - kl_loss: 3.8040
Epoch 70/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.2227 - reconstruction_loss: 249.4159 - kl_loss: 3.8068
Epoch 71/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.1848 - reconstruction_loss: 249.3755 - kl_loss: 3.8094
Epoch 72/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.1812 - reconstruction_loss: 249.3737 - kl_loss: 3.8074
Epoch 73/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.1803 - reconstruction_loss: 249.3743 - kl_loss: 3.8059
Epoch 74/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.1295 - reconstruction_loss: 249.3114 - kl_loss: 3.8181
Epoch 75/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.0516 - reconstruction_loss: 249.2391 - kl_loss: 3.8125
Epoch 76/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.0736 - reconstruction_loss: 249.2582 - kl_loss: 3.8154
Epoch 77/100
1094/1094 [==============================] - 6s 6ms/step - loss: 253.0331 - reconstruction_loss: 249.2200 - kl_loss: 3.8131
Epoch 78/100
1094/1094 [==============================] - 7s 6ms/step - loss: 253.0479 - reconstruction_loss: 249.2272 - kl_loss: 3.8207
Epoch 79/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.9317 - reconstruction_loss: 249.1137 - kl_loss: 3.8179
Epoch 80/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.9578 - reconstruction_loss: 249.1483 - kl_loss: 3.8095
Epoch 81/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.9072 - reconstruction_loss: 249.0963 - kl_loss: 3.8109
Epoch 82/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.8793 - reconstruction_loss: 249.0646 - kl_loss: 3.8147
Epoch 83/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.8914 - reconstruction_loss: 249.0676 - kl_loss: 3.8238
Epoch 84/100
1094/1094 [==============================] - 6s 6ms/step - loss: 252.8365 - reconstruction_loss: 249.0121 - kl_loss: 3.8244
Epoch 85/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.8063 - reconstruction_loss: 248.9844 - kl_loss: 3.8218
Epoch 86/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.7960 - reconstruction_loss: 248.9777 - kl_loss: 3.8183
Epoch 87/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.7733 - reconstruction_loss: 248.9529 - kl_loss: 3.8204
Epoch 88/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.7303 - reconstruction_loss: 248.9055 - kl_loss: 3.8248
Epoch 89/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.7225 - reconstruction_loss: 248.8902 - kl_loss: 3.8323
Epoch 90/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.6822 - reconstruction_loss: 248.8549 - kl_loss: 3.8273
Epoch 91/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.6540 - reconstruction_loss: 248.8314 - kl_loss: 3.8227
Epoch 92/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.6540 - reconstruction_loss: 248.8239 - kl_loss: 3.8300
Epoch 93/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.6213 - reconstruction_loss: 248.7778 - kl_loss: 3.8435
Epoch 94/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.5990 - reconstruction_loss: 248.7594 - kl_loss: 3.8397
Epoch 95/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.5786 - reconstruction_loss: 248.7413 - kl_loss: 3.8373
Epoch 96/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.5839 - reconstruction_loss: 248.7411 - kl_loss: 3.8427
Epoch 97/100
1094/1094 [==============================] - 7s 7ms/step - loss: 252.5364 - reconstruction_loss: 248.6960 - kl_loss: 3.8404
Epoch 98/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.5347 - reconstruction_loss: 248.6915 - kl_loss: 3.8431
Epoch 99/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.4996 - reconstruction_loss: 248.6569 - kl_loss: 3.8428
Epoch 100/100
1094/1094 [==============================] - 7s 6ms/step - loss: 252.4938 - reconstruction_loss: 248.6405 - kl_loss: 3.8533
<tensorflow.python.keras.callbacks.History at 0x7f5467c56be0>
• In this step, we display training results, we will be displaying these results according to their values in latent space vectors.

Code:

## python3

 def plot_latent(encoder, decoder):     # display a n * n 2D manifold of images     n = 10     img_dim = 28     scale = 2.0     figsize = 15     figure = np.zeros((img_dim * n, img_dim * n))     # linearly spaced coordinates corresponding to the 2D plot     # of images classes in the latent space     grid_x = np.linspace(-scale, scale, n)     grid_y = np.linspace(-scale, scale, n)[::-1]       for i, yi in enumerate(grid_y):         for j, xi in enumerate(grid_x):             z_sample = np.array([[xi, yi]])             x_decoded = decoder.predict(z_sample)             images = x_decoded[0].reshape(img_dim, img_dim)             figure[                 i * img_dim : (i + 1) * img_dim,                 j * img_dim : (j + 1) * img_dim,             ] = images       plt.figure(figsize =(figsize, figsize))     start_range = img_dim // 2     end_range = n * img_dim + start_range + 1     pixel_range = np.arange(start_range, end_range, img_dim)     sample_range_x = np.round(grid_x, 1)     sample_range_y = np.round(grid_y, 1)     plt.xticks(pixel_range, sample_range_x)     plt.yticks(pixel_range, sample_range_y)     plt.xlabel("z[0]")     plt.ylabel("z[1]")     plt.imshow(figure, cmap ="Greys_r")     plt.show()     plot_latent(encoder, decoder)

• To get a more clear view of our representational latent vectors values, we will be plotting the scatter plot of training data on the basis of their values of corresponding latent dimensions generated from the encoder .

Code:

## python3

 def plot_label_clusters(encoder, decoder, data, test_lab):     z_mean, _, _ = encoder.predict(data)     plt.figure(figsize =(12, 10))     sc = plt.scatter(z_mean[:, 0], z_mean[:, 1], c = test_lab)     cbar = plt.colorbar(sc, ticks = range(10))     cbar.ax.set_yticklabels([labels.get(i) for i in range(10)])     plt.xlabel("z[0]")     plt.ylabel("z[1]")     plt.show()     labels = {0    :"T-shirt / top", 1:    "Trouser", 2:    "Pullover", 3:    "Dress", 4:    "Coat", 5:    "Sandal", 6:    "Shirt", 7:    "Sneaker", 8:    "Bag", 9:    "Ankle boot"}   (x_train, y_train), _ = keras.datasets.fashion_mnist.load_data() x_train = np.expand_dims(x_train, -1).astype("float32") / 255 plot_label_clusters(encoder, decoder, x_train, y_train)

References:

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