TENSOR FLOW MODULE

A TensorFlow module 

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A TensorFlow model is a computational graph that defines how data flows through a series of layers to produce an output. Here’s a short explanation of the main components used in your module:

1. Module Structure:
   • Sequential Model: This is a linear stack of layers where each layer has one input and one output. In your case, you use a Sequential model because you're building a simple, linear model with just one layer.
2. Layers:
   • Dense Layer: This is a fully connected layer, meaning each neuron in the layer is connected to every neuron in the previous layer. In your model, you're using one Dense layer with a single unit (neuron) to make predictions. The number of units represents the output dimension of the layer (here, it's just 1 for the predicted price).
3. Activation Function:
   • By default, the Dense layer does not use an activation function in your model, meaning it applies a linear transformation (i.e., y = wx + b), which is suitable for regression tasks like predicting the price of a house.
4. Optimizer:
   • RMSprop: This is an optimizer used to minimize the loss function by adjusting the model’s parameters (weights and biases) during training. The learning rate controls how large the changes to the parameters are in each step of the optimization process.
5. Loss Function:
   • Mean Squared Error (MSE): This is the loss function used in regression problems, which calculates the average of the squared differences between the predicted and actual values. The model aims to minimize this loss to improve its accuracy.
6. Training:
   • Fit: The fit() function is used to train the model on the data. You provide the training data (X_train_1d and y_train), the number of epochs (how many times the model sees the entire dataset), and the batch size (how many samples are used in one step of training).
7. Prediction:
   • Once trained, you can use the model to make predictions by calling model.predict(), where you input the number of rooms, and it outputs the predicted house price.

So, in your model, you're trying to predict house prices based on the average number of rooms, with the goal of minimizing the error (MSE) during training.