def test():
layers = [
base_layer.DirectLayer((28, 28)),
NeuronLayer(50, tf_neurons.Relu()),
NeuronLayer(10, tf_neurons.SoftMax())
]
# layers = [base_layer.DirectLayer((28, 28)), Conv2DPoolingLayer(32, (5, 5), (2, 2)), NeuronLayer(50, tf_neurons.Relu()),
# NeuronLayer(10, tf_neurons.SoftMax())]
net = BaseNet(layers)
# net._get_b()
# net.save_params()
train(net, 200, 64)
print('after training:')
net._get_b()
net.load_params()
net._get_b()
print(net.test(mn.tf_valid_set()))
# print('test before reset:{0}'.format(net.test_model()))
# net.reset_params()
#
# print('test after reset:{0}'.format(net.test_model()))
# net._get_b()
# # train(net, 1000, 600, learning_rate=0.01)
# net.load_params()
# print('after loading:')
# net._get_b()
# print('load best test:{0}'.format(net.test_model()))
print('done')
def test():
layers = [base_layer.DirectLayer((28, 28)), NeuronLayer(50, tf_neurons.Relu()),
NeuronLayer(10, tf_neurons.SoftMax())]
# layers = [base_layer.DirectLayer((28, 28)), Conv2DPoolingLayer(32, (5, 5), (2, 2)), NeuronLayer(50, tf_neurons.Relu()),
# NeuronLayer(10, tf_neurons.SoftMax())]
net = BaseNet(layers)
# net._get_b()
# net.save_params()
train(net, 200, 64)
print('after training:')
net._get_b()
net.load_params()
net._get_b()
print(net.test(mn.tf_valid_set()))
# print('test before reset:{0}'.format(net.test_model()))
# net.reset_params()
#
# print('test after reset:{0}'.format(net.test_model()))
# net._get_b()
# # train(net, 1000, 600, learning_rate=0.01)
# net.load_params()
# print('after loading:')
# net._get_b()
# print('load best test:{0}'.format(net.test_model()))
print('done')
def train(net: BaseNet, n_epochs, batch_size=64, learning_rate=1e-3, auto_load_mnist=True, **kwargs):
print('layers:{!r}'.format(net.layers))
print('loading data sets...')
if auto_load_mnist:
train_set = mn.tf_train_set()
valid_set = mn.tf_valid_set()
test_set = mn.tf_test_set()
# train_set = (mnist1.train.images, mnist1.train.labels)
# valid_set = (mnist1.validation.images, mnist1.validation.labels)
# test_set = (mnist1.test.images, mnist1.test.labels)
else:
train_set = kwargs['train_set']
valid_set = kwargs['valid_set']
test_set = kwargs['test_set']
data_sets = (train_set, valid_set, test_set)
# select custom cost/loss function
net.train(data_sets, batch_size, n_epochs, learning_rate)
def train(net: BaseNet,
n_epochs,
batch_size=64,
learning_rate=1e-3,
auto_load_mnist=True,
**kwargs):
print('layers:{!r}'.format(net.layers))
print('loading data sets...')
if auto_load_mnist:
train_set = mn.tf_train_set()
valid_set = mn.tf_valid_set()
test_set = mn.tf_test_set()
# train_set = (mnist1.train.images, mnist1.train.labels)
# valid_set = (mnist1.validation.images, mnist1.validation.labels)
# test_set = (mnist1.test.images, mnist1.test.labels)
else:
train_set = kwargs['train_set']
valid_set = kwargs['valid_set']
test_set = kwargs['test_set']
data_sets = (train_set, valid_set, test_set)
# select custom cost/loss function
net.train(data_sets, batch_size, n_epochs, learning_rate)
