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)
