Convolution + LSTM Hybrid Example - Bigdl Typing CST Test

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Convolution + LSTM Hybrid Example — Bigdl Code

Combining convolutional and LSTM layers for spatio-temporal data in BigDL.

from bigdl.nn.layer import Sequential, Conv2D, ReLU, MaxPooling2D, Recurrent, LSTM, Linear, SoftMax
from bigdl.optim.optimizer import SGD

# Define hybrid model
model = Sequential()
model.add(Conv2D(1, 16, kernel_size=3, stride=1, padding=1)).add(ReLU())
model.add(MaxPooling2D(pool_size=2, stride=2))
model.add(Recurrent().add(LSTM(16*14*14, 50)))
model.add(Linear(50, 10)).add(SoftMax())

# Optimizer
optimizer = SGD(model=model, learningrate=0.01)
# optimizer.train(spatio_temporal_data)

print('Hybrid Conv+LSTM model ready.')

Bigdl Language Guide

BigDL is an open-source distributed deep learning library for Apache Spark, enabling users to build, train, and deploy deep learning models at scale on big data clusters using standard Spark or Hadoop environments.

Primary Use Cases

▸Distributed training of deep learning models on Spark/Hadoop clusters
▸Large-scale image, text, and time-series analysis
▸Recommendation engines and predictive analytics on big datasets
▸Integrating deep learning with existing big data pipelines
▸Deploying AI models directly on big data infrastructure for inference

Notable Features

▸High-performance distributed deep learning on Apache Spark
▸Supports CNNs, RNNs, LSTMs, and standard neural networks
▸Keras-style high-level APIs for model development
▸Zero-copy data transformation between Spark RDDs/DataFrames and tensors
▸Integration with TensorFlow, PyTorch, and ONNX for model import/export

Origin & Creator

BigDL was developed by Intel in 2016 to bring deep learning capabilities to Apache Spark clusters efficiently, aiming to leverage big data infrastructure for AI workloads.

Industrial Note

BigDL is primarily used in industries requiring large-scale AI training on big data, such as finance, telecom, healthcare, and recommendation systems, where in-place model training and low-latency inference on Spark clusters are crucial.

More Bigdl Typing Exercises

BigDL Simple Neural Network Example BigDL Convolutional Neural Network Example BigDL RNN Example BigDL Autoencoder Example BigDL LSTM Sequence Forecasting BigDL Transfer Learning Example BigDL Distributed Training Example BigDL Custom Loss Example BigDL AutoML Pipeline Example

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Convolution + LSTM Hybrid Example — Bigdl Code

Combining convolutional and LSTM layers for spatio-temporal data in BigDL.

from bigdl.nn.layer import Sequential, Conv2D, ReLU, MaxPooling2D, Recurrent, LSTM, Linear, SoftMax from bigdl.optim.optimizer import SGD # Define hybrid model model = Sequential() model.add(Conv2D(1, 16, kernel_size=3, stride=1, padding=1)).add(ReLU()) model.add(MaxPooling2D(pool_size=2, stride=2)) model.add(Recurrent().add(LSTM(16*14*14, 50))) model.add(Linear(50, 10)).add(SoftMax()) # Optimizer optimizer = SGD(model=model, learningrate=0.01) # optimizer.train(spatio_temporal_data) print('Hybrid Conv+LSTM model ready.')

Bigdl Language Guide

Primary Use Cases

▸Distributed training of deep learning models on Spark/Hadoop clusters
▸Large-scale image, text, and time-series analysis
▸Recommendation engines and predictive analytics on big datasets
▸Integrating deep learning with existing big data pipelines
▸Deploying AI models directly on big data infrastructure for inference

Notable Features

▸High-performance distributed deep learning on Apache Spark
▸Supports CNNs, RNNs, LSTMs, and standard neural networks
▸Keras-style high-level APIs for model development
▸Zero-copy data transformation between Spark RDDs/DataFrames and tensors
▸Integration with TensorFlow, PyTorch, and ONNX for model import/export

Origin & Creator

BigDL was developed by Intel in 2016 to bring deep learning capabilities to Apache Spark clusters efficiently, aiming to leverage big data infrastructure for AI workloads.

Industrial Note