minibatch_size = 64
#create network & trainer
network = FCNN_SoftmaxCE(2, [100, 100],
2, ['Relu', 'Relu'],
weight_init_std='he')
trainer = Trainer(network,
X_train,
Y_train,
X_val,
Y_val,
num_epoch,
minibatch_size,
'adam', {'lr': 0.01},
verbose=True,
LossAccInterval=10)
#start training network
train_loss_list, val_loss_list, train_acc_list, val_acc_list, x, lrs = trainer.train(
)
#network training finished
#feed the trained final network, whole moon data in order to visualize the result
draw_border(network,
X_train,
Y_train,
X_val,
Y_val,
0.01,
c_map='viridis',
c_edge='black')
minibatch_size,
'sgd', {'lr': 0.2901},
verbose=True,
LossAccInterval=10)
trainer10 = Trainer(network10,
X_train,
Y_train,
X_val,
Y_val,
num_epoch,
minibatch_size,
'sgd', {'lr': 0.35},
verbose=True,
LossAccInterval=10)
train_loss_list_1, val_loss_list_1, train_acc_list_1, val_acc_list_1, x, lrs_1 = trainer1.train(
)
train_loss_list_2, val_loss_list_2, train_acc_list_2, val_acc_list_2, x, lrs_2 = trainer2.train(
)
train_loss_list_3, val_loss_list_3, train_acc_list_3, val_acc_list_3, x, lrs_3 = trainer3.train(
)
train_loss_list_4, val_loss_list_4, train_acc_list_4, val_acc_list_4, x, lrs_4 = trainer4.train(
)
train_loss_list_5, val_loss_list_5, train_acc_list_5, val_acc_list_5, x, lrs_5 = trainer5.train(
)
train_loss_list_6, val_loss_list_6, train_acc_list_6, val_acc_list_6, x, lrs_6 = trainer6.train(
)
train_loss_list_7, val_loss_list_7, train_acc_list_7, val_acc_list_7, x, lrs_7 = trainer7.train(
)
train_loss_list_8, val_loss_list_8, train_acc_list_8, val_acc_list_8, x, lrs_8 = trainer8.train(
)
train_loss_list_9, val_loss_list_9, train_acc_list_9, val_acc_list_9, x, lrs_9 = trainer9.train(
minibatch_size,
'adam', {'lr': 0.001},
verbose=True,
LossAccInterval=100)
trainer_increase = Trainer(network_increase,
X_train_extended,
Y_train_extended,
X_val,
Y_val,
num_epoch,
minibatch_size,
'adam', {'lr': 0.001},
verbose=True,
LossAccInterval=100)
train_loss_list_augment, val_loss_list_augment, train_acc_list_augment, val_acc_list_augment, x, lrs = trainer_augment.train(
)
train_loss_list_increase, val_loss_list_increase, train_acc_list_increase, val_acc_list_increase, x, lrs = trainer_increase.train(
)
trainLoss_aug = go.Scatter(x=x,
y=train_loss_list_augment,
mode='lines',
name='augment',
line=dict(color=('rgb(232, 85, 85)')))
valLoss_aug = go.Scatter(x=x,
y=val_loss_list_augment,
mode='lines',
name='augment',
line=dict(color=('rgb(232, 85, 85)'), dash='dash'))
trainAcc_aug = go.Scatter(x=x,
y=train_acc_list_augment,
weight_init_std='he',
use_dropout=True,
use_batchnorm=True,
keep_probs=[0.9, 0.9],
batchnorm_prev=True)
trainer_prev = Trainer(network_prev,
X_train,
Y_train,
X_val,
Y_val,
num_epoch,
minibatch_size,
optimizer_type, {'lr': learning_rate},
verbose=True,
LossAccInterval=5)
train_loss_list_prev, val_loss_list_prev, train_acc_list_prev, val_acc_list_prev, x_axis, lrs = trainer_prev.train(
)
network_after = FCNN_SoftmaxCE(784, [256, 256],
10, ['Relu', 'Relu'],
weight_init_std='he',
use_dropout=True,
use_batchnorm=True,
keep_probs=[0.9, 0.9],
batchnorm_prev=False)
trainer_after = Trainer(network_after,
X_train,
Y_train,
X_val,
Y_val,
num_epoch,
minibatch_size,
10, ['Relu', 'Relu', 'Relu', 'Relu', 'Relu'],
weight_init_std='he',
use_dropout=True,
use_batchnorm=True,
keep_probs=[0.9, 0.9, 0.9, 0.9, 0.9])
trainer1 = Trainer(network1,
X_train,
Y_train,
X_val,
Y_val,
num_epoch,
minibatch_size,
optimizer_type1, {'lr': learning_rate},
verbose=True,
LossAccInterval=20)
train_loss_list1, val_loss_list1, train_acc_list1, val_acc_list1, x_axis, lrs = trainer1.train(
)
network2 = FCNN_SoftmaxCE(784, [256, 256, 256, 256, 256],
10, ['Relu', 'Relu', 'Relu', 'Relu', 'Relu'],
weight_init_std='he',
use_dropout=True,
use_batchnorm=True,
keep_probs=[0.9, 0.9, 0.9, 0.9, 0.9])
trainer2 = Trainer(network2,
X_train,
Y_train,
X_val,
Y_val,
num_epoch,
minibatch_size,
optimizer_type2, {'lr': learning_rate},