Problem Statement
The dataset consists of 2-Dimensional spectrograms of radio signals from space collected at the SETI Institute by the Allen Telescope Array. The objective is to classify the radio signals from outer space into one of four classes.
Dataset Description
SETI Dataset
Training Data:
train_images: Normalized values of Pixels
train_labels: Stored as One-Hot Encoded data
Validation Data:
val_images: Normalized values of Pixels
val_labels: Stored as One-Hot Encoded data
Classes: “squiggle”, “narrowband”, “narrowbanddrd”, and “noise”
.ipynb file
As a part of this test, you will be performing the following tasks:
Prepare a detailed python notebook using CNN for classifying the radio signals from deep space using Keras from the SETI Dataset
Import Required Libraries
Load and Pre-process the dataset
Visualize the dataset
Create Training and Validation Data Generators
Design a Convolutional Neural Network (CNN) Model
Compile the Model
Train the Model
Evaluate the Mode
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