Model training

Model training is implemented in the fit(..) method. It takes the following parameters:

• train_X: array_like, shape (n_samples, n_features), Training data
• train_Y: array_like, shape (n_samples, n_classes), Training labels
• val_X: array_like, shape (N, n_features) optional, (default = None), Validation data
• val_Y: array_like, shape (N, n_classes) optional, (default = None), Validation labels
• graph: tf.Graph, optional (default = None), TensorFlow Graph object

Next, we look at the implementation of fit(...) function where the model is trained and saved in the model path specified by model_path.

def fit(self, train_X, train_Y, val_X=None, val_Y=None, graph=None):

    if len(train_Y.shape) != 1:
        num_classes = train_Y.shape[1]
    else:
        raise Exception("Please convert the labels with one-hot encoding.")

    g = graph if graph is not None else self.tf_graph

    with g.as_default():
        # Build model
        self.build_model(train_X.shape[1], num_classes)
        with...