**Eco-Friendly AI Code Snippet: Resource-aware Neural Networ

Eco-Friendly AI Code Snippet: Resource-aware Neural Network

import numpy as np
from tensorflow.keras.models import Model

def sustainable_model(x_train, x_val):
    input_layer = keras.Input(shape=(x_train.shape[1],))
    model = Sequential([Dense(x_train.shape[1]//4, activation='relu', input_shape=(x_train.shape[1],))])
    model.compile(loss='mean_squared_error', optimizer='adam')
    return Model(inputs=input_layer, outputs=model)

This code snippet defines a simple neural network model that incorporates sustainability into its design.

Here's what it does:

• It takes into consideration computational resource usage, a major concern for energy-intensive AI models.
• The input layer size is reduced using a technique called dimensionality reduction, using x_train.shape[1]//4 instead of the full size. This reduces the computational load while preserving the model's performance to some extent.
• Optimizer 'adam' is used to ensure fast and efficient model convergence.
• It compiles the model with a focus on resource efficiency during training.
• The model can then be trained and validated on smaller input datasets.

This snippet showcases a key concept in sustainable AI: designing models that are energy-efficient and computationally light.

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