Prepare python notebook(recommended-use Google Colab) to build, train and evaluate model…

Prepare python notebook(recommended-use Google Colab) to build, train and evaluate model (tensorflow or tensorflow.keras library recommended). Read the instructions carefully.

Question:Image Captioning : Image Captioning is the process of generating textual description of an image. It uses both Natural LanguageProcessing and Computer Vision to generate the captions. The dataset will be in the form [image ? captions].The dataset consists of input images and their corresponding output captions.

Prepare python notebook(recommended-use Google Colab) to build, train and evaluate model (tensorflow...

Encoder The Convolutional Neural Network(CNN) can be thought of as an encoder. The input image is given to CNN to extract the features. The last hidden state of the CNN is connected to the Decoder. Decoder The Decoder is a Recurrent Neural Network(RNN) which does language modelling up to the word level. The first time step receives the encoded output from the encoder and also the vector.

1. Import Libraries/Dataset (0 mark)

a. Import the required libraries

b. Check the GPU available (recommended- use free GPU provided by Google Colab).

2. Model Building

a. Use Pretrained Resnet-50 model trained on ImageNet dataset (available publicly on google) for image feature extraction.

b. Create 5 layered LSTM layer model and other relevant layers for image caption generation.

c. Add L1 regularization to all the LSTM layers.

d. Add one layer of dropout at the appropriate position and give reasons.

e. Choose the appropriate activation function for all the layers.

f. Print the model summary.

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