def main():
(x_train, t_train), (x_test, t_test) = load_mnist(flatten=False)
print(x_train.shape, t_train.shape)
print(t_train[0])
net = MultiLayerNet(is_use_dropout=False)
net.add_layer(Layer.Conv2D(16, (3, 3), pad=1, input_size=(1, 28, 28)), initializer=Initializer.He(),
activation=Layer.Relu())
net.add_layer(Layer.BatchNormalization())
net.add_layer(Layer.Relu())
net.add_layer(Layer.Pooling(pool_h=2, pool_w=2, stride=2))
net.add_layer(Layer.Conv2D(16, (3, 3), pad=1, initializer=Initializer.He()))
net.add_layer(Layer.BatchNormalization())
net.add_layer(Layer.Relu())
net.add_layer(Layer.Pooling(pool_h=2, pool_w=2, stride=2))
net.add_layer(Layer.Dense(20, initializer=Initializer.He(), activation=Layer.Relu()))
net.add_layer(Layer.Dropout(0.5))
net.add_layer(Layer.Dense(10))
net.add_layer(Layer.Dropout(0.5))
net.add_layer(Layer.SoftmaxWithLoss())
if gpu_enable:
net.to_gpu()
for k, v in net.params.items():
print(k, v.shape)
result = net.train(
x_train, t_train, x_test, t_test, batch_size=200, iters_num=100, print_epoch=1, evaluate_limit=500,
is_use_progress_bar=True,
optimizer=Optimizer.Adam(lr=0.001))
import pickle
import datetime
## Save pickle
with open(f"train_data_{str(datetime.datetime.now())[:-7].replace(':', '')}.pickle", "wb") as fw:
pickle.dump(result, fw)
# net.save_model()
print("============================================")
def make_net2():
net = MultiLayerNet(is_use_dropout=False)
net.add_layer(Layer.Conv2D(32, (3, 3), pad=1, input_size=(1, 64, 64)),
initializer=Initializer.He(),
activation=Layer.Relu())
net.add_layer(Layer.Pooling(pool_h=2, pool_w=2, stride=2))
net.add_layer(
Layer.Dense(128, initializer=Initializer.He(),
activation=Layer.Relu()))
net.add_layer(Layer.Dense(2, initializer=Initializer.He()))
net.add_layer(Layer.SoftmaxWithLoss())
return net
def make_net1():
net = MultiLayerNet(is_use_dropout=False)
net.add_layer(Layer.Conv2D(32, (3, 3), pad=1, input_size=(1, 128, 128)),
initializer=Initializer.He())
net.add_layer(Layer.BatchNormalization())
net.add_layer(Layer.Relu())
net.add_layer(Layer.Pooling(pool_h=2, pool_w=2, stride=2))
# net.add_layer(Layer.Conv2D(64, (3, 3), pad=1, initializer=Initializer.He()))
# net.add_layer(Layer.BatchNormalization())
# net.add_layer(Layer.Relu())
# net.add_layer(Layer.Pooling(pool_h=2, pool_w=2, stride=2))
net.add_layer(Layer.Conv2D(32, (3, 3), pad=1,
initializer=Initializer.He()))
net.add_layer(Layer.BatchNormalization())
net.add_layer(Layer.Relu())
net.add_layer(Layer.Pooling(pool_h=2, pool_w=2, stride=2))
net.add_layer(
Layer.Dense(30, initializer=Initializer.He(), activation=Layer.Relu()))
net.add_layer(Layer.Dropout(0.5))
net.add_layer(Layer.Dense(3))
net.add_layer(Layer.Dropout(0.5))
net.add_layer(Layer.SoftmaxWithLoss())
return net
def save_model(self, path=None):
save_data = OrderedDict()
params = self.params
save_data["NetConfig"] = {
"weight_decay_lambda": self.weight_decay_lambda,
"is_use_dropout": self.is_use_dropout,
"dropout_ratio": self.dropout_ratio
}
layer_info = []
for layer in self.added_layer_list:
t = layer
if isinstance(t, Layer.SoftmaxWithLoss):
t = Layer.SoftmaxWithLoss()
elif isinstance(t, Layer.IdentityWithLoss):
t = Layer.IdentityWithLoss()
elif isinstance(t, Layer.BatchNormalization):
t = Layer.BatchNormalization(running_mean=t.running_mean, running_var=t.running_var)
elif isinstance(t, Layer.Pooling):
t = Layer.Pooling(t.pool_h, t.pool_w, t.stride, t.pad)
s = jsp.encode(t)
layer_info.append(s)
save_data["LayerInfo"] = layer_info
# for layer_idx, layer in enumerate(self.layers.values()):
# if isinstance(layer, Layer.BatchNormalization):
# params[f"BN_m{layer_idx}"] = layer.running_mean
# params[f"BN_v{layer_idx}"] = layer.running_var
save_data["Params"] = params
if path is None:
path = f"train_weight_{str(datetime.datetime.now())[:-7].replace(':', '')}.npz"
# print(save_data)
np.savez_compressed(path, **save_data)
print(f"Weight was saved at {path}")