model_tf-checkpoint.py

def run_model():
    # reset underlying graph data
    tf.reset_default_graph()
    # Build neural network
    net = tflearn.input_data(shape=[None, len(train_x[0])])
    net = tflearn.fully_connected(net, 8)
    net = tflearn.fully_connected(net, 8)
    net = tflearn.fully_connected(net, len(train_y[0]), activation='softmax')
    net = tflearn.regression(net)

    # Define model and setup tensorboard
    model = tflearn.DNN(net, tensorboard_dir='tflearn_logs')
    # Start training (apply gradient descent algorithm)
    model.fit(train_x, train_y, n_epoch=1000, batch_size=8, show_metric=True)
    model.save('model.tflearn')