checkpoint2 = ModelCheckpoint(MODEL_PATH +
'model_loss{}.h5'.format(exp_suffix),
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min',
save_weights_only=True)
tensorboard = TensorBoard(MODEL_PATH + 'logs{}'.format(fold_id) +
'{}'.format(exp_suffix) + '/')
# reduceLROnPlat = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3,
# verbose=1, mode='auto', epsilon=0.0001)
# early = EarlyStopping(monitor="val_loss",
# mode="min",
# patience=6)
f1_metric = F1Metric(validation_generator2,
2 * len(valid_indexes) // batch_size, batch_size,
28) #2 times val because of val_aug
nb_epochs = epochs[1]
nb_cycles = 15
init_lr = 0.001
def _cosine_anneal_schedule(t):
cos_inner = np.pi * (t % (nb_epochs // nb_cycles))
cos_inner /= nb_epochs // nb_cycles
cos_out = np.cos(cos_inner) + 1
return float(init_lr / 2 * cos_out)
oversample_factor=0)
checkpoint = ModelCheckpoint(MODEL_PATH + 'model_{}.h5'.format(exp_suffix),
monitor='val_f1_all',
verbose=1,
save_best_only=True,
mode='max',
save_weights_only=True)
tensorboard = TensorBoard(MODEL_PATH + 'logs{}_'.format(fold_id) +
'{}'.format(exp_suffix) + '/')
# reduceLROnPlat = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3,
# verbose=1, mode='auto', epsilon=0.0001)
# early = EarlyStopping(monitor="val_loss",
# mode="min",
# patience=6)
f1_metric = F1Metric(validation_generator, len(valid_indexes) // 1, 1, 28)
callbacks_list = [f1_metric, checkpoint, tensorboard]
# warm up model
model = create_model(input_shape=(SIZE, SIZE, 3), n_out=28)
for layer in model.layers:
layer.trainable = False
#model.layers[2].trainable = True
model.layers[-1].trainable = True
model.layers[-2].trainable = True
model.layers[-3].trainable = True
model.layers[-4].trainable = True
model.layers[-5].trainable = True
model.layers[-6].trainable = True
augument=False)
checkpoint = ModelCheckpoint(MODEL_PATH + 'model_{}.h5'.format(exp_suffix),
monitor='val_f1_all',
verbose=1,
save_best_only=True,
mode='max',
save_weights_only=True)
tensorboard = TensorBoard(MODEL_PATH + 'logs{}_'.format(fold_id) +
'{}'.format(exp_suffix) + '/')
# reduceLROnPlat = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3,
# verbose=1, mode='auto', epsilon=0.0001)
# early = EarlyStopping(monitor="val_loss",
# mode="min",
# patience=6)
f1_metric = F1Metric(validation_generator,
len(valid_indexes) // batch_size, batch_size, 28)
callbacks_list = [f1_metric, checkpoint, tensorboard]
# warm up model
model = create_model(input_shape=(SIZE, SIZE, 3), n_out=28)
for layer in model.layers:
layer.trainable = False
#model.layers[2].trainable = True
model.layers[-1].trainable = True
model.layers[-2].trainable = True
model.layers[-3].trainable = True
model.layers[-4].trainable = True
model.layers[-5].trainable = True
model.layers[-6].trainable = True
#ax1.bar(n_keys, [train_sum_vec[k] for k in n_keys])
#ax1.set_title('Training Distribution')
#ax2.bar(n_keys, [valid_sum_vec[k] for k in n_keys])
#ax2.set_title('Validation Distribution')
# create train and valid datagens
aug = False
if fold_id == 2:
aug = True
train_generator = data_generator.create_train(train_dataset_info[train_indexes],
batch_size, (SIZE, SIZE, 3), augument=aug)
validation_generator = data_generator.create_train(train_dataset_info[valid_indexes],
batch_size, (SIZE, SIZE, 3), augument=False)
f1_macro = F1Metric(validation_generator,steps=len(valid_indexes)//batch_size,batch_size=batch_size,num_classes=28)
# f1_macro resuls in an additional metric "val_f1_all" in history which can be used to checkpoint
checkpoint = ModelCheckpoint(MODEL_PATH + 'model_{}.h5'.format(fold_id), monitor='val_f1_all', verbose=1,
save_best_only=True, mode='max', save_weights_only=True)
tensorboard = TensorBoard(MODEL_PATH + 'logs{}/'.format(fold_id))
callbacks_list = [f1_macro,checkpoint, tensorboard]
# warm up model
model = create_model(
input_shape=(SIZE, SIZE, 4),
n_out=28)
for layer in model.layers:
layer.trainable = False
#model.layers[2].trainable = True
model.layers[-1].trainable = True