sgodfrey66

  history=model.fit(x=ds.train_ds_modeling,
                    y=None,
                    batch_size=None,
                    verbose=1,
                    validation_split=0.0,
                    epochs=epochs,
                    validation_data=ds.test_ds_modeling,
                    callbacks=callbacks)

sgodfrey66

  # Create a callback for TensorBoard and put it in a list
  callbacks=[tf.keras.callbacks.TensorBoard(log_dir=model_log,
                                              histogram_freq=1,
                                              write_graph=True,
                                              write_images=False,
                                              update_freq='epoch')]

  # Create the TensorBoard writer for the confusion matrix writer
  cm_file_writer=tf.summary.create_file_writer(logdir=cm_log)

sgodfrey66

  # Define the confusion matrix logging function
  def cm_logger(epoch, logs):

    # Create a name for this image
    i_name='Confusion Matrix: {}; {}'.format(model_name, ds.dataset_name)

    # Create the per-epoch callback for the confusion matrix
    cm_image=image_funcs.plot_to_image(metrics_funcs.generate_heatmap(
                                       dataset=ds,
                                       model=model).figure)