5 Best Ways to Use Keras with Embedding Layers to Share Layers in Python

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πŸ’‘ Problem Formulation: In machine learning, particularly natural language processing, it is common to transform categorical data into a dense vector representation through an Embedding layer. A challenge arises when one needs to apply this embedding to multiple input sequences and share the same layer weights across different parts of a neural network. This article demonstrates how to utilize Keras and its embedding layer effectively to share layers across different inputs using Python.

Method 1: Using the Functional API to Share an Embedding Layer

This method employs Keras’s Functional API, which provides flexibility in connecting layers and sharing them. It allows an Embedding layer to be defined once and reused, which ensures shared weights across different input sequences. This is particularly useful when inputs are related or when one wants to enforce a shared representation.

Here’s an example:

from keras.layers import Input, Embedding, Dense, Flatten
from keras.models import Model

# Define a shared embedding layer
shared_embedding = Embedding(input_dim=1000, output_dim=64)

# Input layers for two different sequences
input_a = Input(shape=(100,))
input_b = Input(shape=(100,))

# Reuse the shared embedding for both inputs
processed_a = shared_embedding(input_a)
processed_b = shared_embedding(input_b)

# Continue with your model architecture
flat_a = Flatten()(processed_a)
flat_b = Flatten()(processed_b)
dense_layer = Dense(10, activation='softmax')(flat_a)
dense_layer_shared = Dense(10, activation='softmax')(flat_b)

# Create a model with multiple inputs
model = Model(inputs=[input_a, input_b], outputs=[dense_layer, dense_layer_shared])

The output will be a summary of the model architecture, showing the shared embedding layer being used by both sequences.

In the provided code snippet, we’ve created a shared embedding layer and associated it with two different input layers using the Functional API of Keras. This allows both inputs to receive the same vector representation, which is beneficial for comparing and relating the inputs during training. The rest of the model architecture can be built upon these shared layers.

Method 2: Sharing Embeddings between Different Models

One can share a single Embedding layer between different Keras models. This approach is useful to keep a consistent embedding space across models, enabling knowledge transfer or co-learning between them.

Here’s an example:

from keras.layers import Embedding, Dense, LSTM
from keras.models import Sequential

# Define a shared embedding layer to be used by multiple models
shared_embedding = Embedding(input_dim=1000, output_dim=64)

# First model using the shared embedding layer
model_1 = Sequential()
model_1.add(Dense(1, activation='sigmoid'))

# Second model using the same shared embedding layer
model_2 = Sequential()
model_2.add(Dense(5, activation='softmax'))

# Summary of model architectures

The output will be two separate model summaries, each including the same shared embedding layer.

This code example demonstrates how the same shared_embedding layer is used as the first layer in two distinct Sequential models. By using this approach, both models will generate embeddings in the same space, allowing for a sort of information sharing between the distinct tasks they may be training to solve.

Method 3: Siamese Networks for Shared Embeddings

Siamese networks are an architecture that naturally allows for sharing layers, including embeddings. They are made up of twin networks which are joined at their outputs. This setup is suitable for tasks such as sentence similarity, where the same embedding is needed to compare different text sequences.

Here’s an example:

from keras.layers import Input, Embedding, LSTM, Lambda
from keras.models import Model
import keras.backend as K

# Define the shared embedding layer
shared_embedding = Embedding(input_dim=10000, output_dim=128)

# Inputs
input_1 = Input(shape=(100,))
input_2 = Input(shape=(100,))

# Shared embedding applied to both inputs
processed_1 = shared_embedding(input_1)
processed_2 = shared_embedding(input_2)

# Shared LSTM layer
shared_lstm = LSTM(64)

# Output of shared LSTM after embedding
vector_1 = shared_lstm(processed_1)
vector_2 = shared_lstm(processed_2)

# Lambda layer for calculating cosine distance
cosine_distance = Lambda(lambda tensors: K.cos(tensors[0], tensors[1]))

# Final model
distance = cosine_distance([vector_1, vector_2])
model = Model(inputs=[input_1, input_2], outputs=distance)


The output will be the model summary showing the shared embedding and LSTM layers, as well as the lambda layer that combines the resulting vectors.

Siamese networks, as illustrated in the code, allow for the same embedding layer to be used for multiple inputs by connecting the outputs. As the weights are shared, the network learns to encode the inputs into an embedding space that is conducive to the task it’s designed for – in this case, calculating the similarity between inputs.

Method 4: Sharing Embeddings in a Multi-input Network

Multi-input networks can have an Embedding layer that is applied to multiple distinct inputs. Such a setup serves well when your model requires the combination of features from different sources with embeddings that should be in a consistent space.

Here’s an example:

from keras.layers import Input, Embedding, concatenate, Dense
from keras.models import Model

# Shared embedding layer for multiple types of inputs
shared_embedding = Embedding(input_dim=5000, output_dim=256)

# Different inputs for the respective features
input_type_1 = Input(shape=(100,))
input_type_2 = Input(shape=(50,))

# Embedding applied to different inputs
embedding_1 = shared_embedding(input_type_1)
embedding_2 = shared_embedding(input_type_2)

# Concatenate the embeddings
merged = concatenate([embedding_1, embedding_2])

# Dense layer after merging embeddings
dense_output = Dense(5, activation='softmax')(merged)

# Model
model = Model(inputs=[input_type_1, input_type_2], outputs=dense_output)

The output is a summary of the multi-input network with embeddings for each input type joined before further processing.

The example showcases how a shared embedding can process multiple different inputs before their resulting embeddings are combined. This is powerful when dealing with heterogeneous inputs where after embedding, the features need to be merged for further prediction tasks.

Bonus One-Liner Method 5: Using Layer Weight Constraints

Keras layers come with weight constraints that can be used to enforce weight sharing by explicitly setting weights of one layer equal to another after every batch update during training.

Here’s an example:

from keras.layers import Embedding
from keras.constraints import Constraint

# Custom constraint class
class SharedWeights(Constraint):
    def __call__(self, w):
        # Define operation to equalize weights - for illustration purposes
        return w  # Here, you would implement the logic to share weights

# Shared weights constraint
shared_weights = SharedWeights()

# Embedding layer with weight constraint
embedding_layer = Embedding(input_dim=1000, output_dim=64, embeddings_constraint=shared_weights)

The example does not provide an output, as it is a conceptual method. It illustrates how to define and apply a custom weight constraint.

While Keras doesn’t directly allow setting one layer’s weights as another’s within the model definition, custom weight constraints can mimic this behavior. This one-liner sets up a conceptual groundwork for implementing a custom constraint that enforces weight sharing between layers.


  • Method 1: Using the Functional API. Strengths: Flexible model architecture with shared layers. Weaknesses: Potentially more complex model definition.
  • Method 2: Sharing Embeddings between Different Models. Strengths: Enables embedding consistency across models. Weaknesses: Requires careful synchronization of model updates.
  • Method 3: Siamese Networks for Shared Embeddings. Strengths: Naturally suited for comparisons tasks, like similarity. Weaknesses: Limited to pairwise input tasks.
  • Method 4: Sharing Embeddings in a Multi-input Network. Strengths: Combines diverse inputs. Weaknesses: Potentially high tensor dimensionality after concatenation.
  • Bonus Method 5: Using Layer Weight Constraints. Strengths: Allows custom sharing behavior. Weaknesses: Requires advanced knowledge to implement custom constraints.