Where exactly are the tensors model.input and model.output declared ?

Hello Nidhi,

In machine learning and deep learning, tensors are typically used to represent data and computations within a neural network. Tensors can be thought of as multi-dimensional arrays. When you're serving a machine learning model, you often need to declare the input and output tensors that will be used during inference (prediction) or serving.

The specific location where tensors are declared can vary depending on the deep learning framework you are using and the context of your code. However, I can provide a general idea of where tensors might be declared during the creation of a model in a deep learning framework like TensorFlow or PyTorch.

TensorFlow: In TensorFlow, you typically declare tensors as part of building the computational graph. For example, when creating a neural network model, you would define placeholders or input tensors for the model's input data and tensors for the output predictions. Here's a simplified example for a feedforward neural network:

python code: import tensorflow as tf

Define input tensor

input_tensor = tf.placeholder(tf.float32, shape=(None, input_size), name="input")

Define model architecture

hidden_layer = tf.layers.dense(input_tensor, units=64, activation=tf.nn.relu) output_tensor = tf.layers.dense(hidden_layer, units=output_size, activation=tf.nn.softmax, name="output")
PyTorch: In PyTorch, tensors are often declared when defining the forward pass of a model using the torch.nn.Module class. Here's a simplified example for a simple neural network:

python code: import torch import torch.nn as nn

class MyModel(nn.Module): def init(self, input_size, output_size): super(MyModel, self).init() self.fc1 = nn.Linear(input_size, 64) self.fc2 = nn.Linear(64, output_size)

def forward(self, x): x = torch.relu(self.fc1(x)) x = torch.softmax(self.fc2(x), dim=1) return x

Create an instance of the model

model = MyModel(input_size, output_size)

Regarding your request for the entire code for a sequence-to-sequence (seq2seq) model, it's a complex model that can vary greatly in its implementation depending on the specific task and dataset. If you have a specific seq2seq model in mind or if you need assistance with a particular aspect of seq2seq modeling, please provide more details, and I had be happy to help further.

Regards Aryan

5agado / recurrent-neural-networks-intro

Where exactly are the tensors model.input and model.output declared ? #3

Define input tensor

Define model architecture

Create an instance of the model