mode='min', verbose=0, save_best_only=True, save_weights_only=True) csv_logger = tf.keras.callbacks.CSVLogger( f'./Logs/{NOW}_{kernel_initializer[ki]}_{Optimizers[opt]}.csv') my_callbacks = [ model_checkpoint, csv_logger, # tf.keras.callbacks.EarlyStopping(patience=2), # tf.keras.callbacks.TensorBoard(log_dir=f'./Logs/{NOW}_{kernel_initializer[ki]}_{Optimizers[opt]}'), ] # ============================================================================= strategy = tf.distribute.MirroredStrategy() with strategy.scope(): MODEL = CreateModel(KI=kernel_initializer[ki], nfs=nfs) MODEL.compile(loss=MeanSquaredError(), optimizer=optimizer, metrics=[myAcc, maxSAD, avgSAD]) if os.path.isfile(HDF5): print(f"restore from {HDF5}") MODEL.load_weights(HDF5) MODEL.evaluate(X, Y, batch_size=256**3) MODEL.fit(X, Y, epochs=SUB_EPOCH, batch_size=256**2, callbacks=my_callbacks) MODEL.evaluate(X, Y, batch_size=256**3) MODEL.load_weights(HDF5) MODEL.evaluate(X, Y, batch_size=256**3)
save_best_only=False), CSVLogger(f"./experiments/{conf['name']}/log.csv"), TensorBoard(f"./experiments/{conf['name']}/Logs"), # tf.keras.callbacks.EarlyStopping(patience=2), ] # ============================================================================= strategy = tf.distribute.MirroredStrategy() with strategy.scope(): MODEL = CreateModel(KI=conf['parameter']['kernel_initializer'], nfs=conf['nfs']) MODEL.compile(loss=MeanSquaredError(), optimizer=optimizer, metrics=[myAcc, maxSAD, avgSAD]) if conf['restore_from']: print(f"restore from conf['restore_from']") MODEL.load_weights(conf['restore_from']) npzfile = np.load(conf['dataset']['XY_npz']) X = npzfile['arr_0'] Y = npzfile['arr_1'] del npzfile MODEL.evaluate(X, Y, batch_size=256**3) MODEL.fit(X, Y, epochs=conf['parameter']['epoch'], batch_size=256**2, callbacks=callbacks) MODEL.evaluate(X, Y, batch_size=256**3)