Training the ML model with TF
The model designed for forecasting the snow is a binary classifier, and it is illustrated in the following diagram:
Figure 3.5 – Neural network model for forecasting the snow
The network consists of the following layers:
- 1 x fully connected layers with 12 neurons and followed by a ReLU activation function
- 1 x dropout layer with a 20% rate (0.2) to prevent overfitting
- 1 x fully connected layer with one output neuron and followed by a sigmoid activation function
In this recipe, we will train the preceding model with TF.
The following Colab file (see the Training the ML model with TF section in the following repository) contains the code referred to in this recipe:
preparing_model.ipynb:
Getting ready
The model designed in this recipe has one input and output node. The input...
