import numpy as np
import plotly
import plotly.graph_objs as go
from HypeNet.Networks.CNN_Simple import CNN_Simple
from HypeNet.Core.loadData import loadMnist
from HypeNet.Core.Trainer import Trainer
from HypeNet.Core.utils import *
import os
np.random.seed(0)
DIR = os.path.dirname(os.path.abspath(__file__)) + '/SavedNetwork/MnistCNN/'
X_train, Y_train, X_val, Y_val, Y_train_label, Y_val_label = loadMnist(
flatten=False)
num_epoch = 1
minibatch_size = 50
save_network = True
learning_rate = 0.01
optimizer_type = 'nesterov'
print('network created')
network = CNN_Simple()
print('network setting finished')
trainer = Trainer(network,
X_train,
Y_train,
X_val,
Y_val,
num_epoch,
minibatch_size,
import numpy as np
import matplotlib.pyplot as plt
from HypeNet.Networks.FCNN_MSE import FCNN_MSE
from HypeNet.Core.loadData import loadMnist
from HypeNet.Core.Trainer import Trainer
from HypeNet.Core.utils import *
X_train, Y_train, X_val, Y_val, Y_train_label, Y_val_label = loadMnist()
num_epoch = 20
minibatch_size = 256
network = FCNN_MSE(784, [1000, 500, 100, 500, 1000],
784, ['Relu', 'Relu', 'Relu', 'Relu', 'Relu', 'Sigmoid'],
weight_init_std='he',
use_dropout=False,
keep_probs=[0.9, 0.9, 0.9, 0.9, 0.9],
use_batchnorm=False)
trainer = Trainer(network,
X_train,
X_train,
X_val,
X_val,
num_epoch,
minibatch_size,
'adam', {'lr': 0.0004},
verbose=True,
LossAccInterval=10000,
lr_scheduler_type='exp_decay',
lr_scheduler_params={'k': 0.00001})
train_loss_list, val_loss_list, train_acc_list, val_acc_list, x, lrs = trainer.train(
)