Hello,
i habe an autoencoder model which i want to visualize but the internals of the model architecture are not visual.
latent_dim = 64
class Autoencoder(Model):
def init(self, latent_dim):
super(Autoencoder, self).init()
self.latent_dim = latent_dim
self.encoder = tf.keras.Sequential([
layers.Flatten(),
layers.Dense(latent_dim, activation=‘relu’),
])
self.decoder = tf.keras.Sequential([
layers.Dense(784, activation=‘sigmoid’),
layers.Reshape((28, 28))
])
def call(self, x):
encoded = self.encoder(x)
decoded = self.decoder(encoded)
return decoded
autoencoder = Autoencoder(latent_dim)
plot_model(autoencoder)
Why is the model architecture not visual?
Thanks
Christoph
Hello innat, thanks for your fast response, but i dont understand correct, but this makes the problem a little bit clearer for me.
innat
#5
Here is the full workaround. (I think this query is more fit on S0).
from tensorflow.keras import Model, Input
import tensorflow as tf
from tensorflow.keras import layers
latent_dim = 64
class Autoencoder(Model):
def __init__(self, latent_dim):
super(Autoencoder, self).__init__()
self.latent_dim = latent_dim
self.encoder = tf.keras.Sequential([
layers.Flatten(),
layers.Dense(latent_dim, activation='relu'),
])
self.decoder = tf.keras.Sequential([
layers.Dense(784, activation='sigmoid'),
layers.Reshape((28, 28))
])
def call(self, x):
encoded = self.encoder(x)
decoded = self.decoder(encoded)
return decoded
def build_graph(self):
x = Input(shape=(28,28))
return Model(inputs=[x], outputs=self.call(x))
autoencoder = Autoencoder(latent_dim)
Model summary
autoencoder.build_graph().summary()
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_5 (InputLayer) [(None, 28, 28)] 0
_________________________________________________________________
sequential (Sequential) (None, 64) 50240
_________________________________________________________________
sequential_1 (Sequential) (None, 28, 28) 50960
=================================================================
Total params: 101,200
Trainable params: 101,200
Non-trainable params: 0
Plotting
tf.keras.utils.plot_model(autoencoder.build_graph(),
expand_nested=True, show_shapes=True)
4 Likes
Hello innat,
you make my day (night in Germany), things are now clear for me 
Here are my results → https://github.com/Christoph-Lauer/Tensorflow-Autoencoder-Tutorial/blob/main/autoencoder.ipynb
2 Likes
