import tensorflow as tf

            self.batch_size = 1000

            # LSTM structure
            self.n_inputs = len(X_train[0][0])  # Features count is of 9: three 3D sensors features over time
            self.n_hidden = N_HIDDEN_CONFIG  # nb of neurons inside the neural network
            self.n_classes = 6  # Final output classes
            self.W = {
                'hidden': tf.Variable(tf.random_normal([self.n_inputs, self.n_hidden])),  # [9, 32]
                'output': tf.Variable(tf.random_normal([self.n_hidden, self.n_classes]))  # [32, 6]
            }
            self.biases = {
                'hidden': tf.Variable(tf.random_normal([self.n_hidden], mean=1.0)),  # [32]
                'output': tf.Variable(tf.random_normal([self.n_classes]))  # [6]
            }
