def train_model():
net = make_net1()
result = net.train(x_train,
t_train,
x_test,
t_test,
batch_size=64,
iters_num=300,
print_epoch=1,
evaluate_limit=130,
is_use_progress_bar=True,
save_model_each_epoch=1,
save_model_path="./idol_result",
optimizer=Optimizer.Adam(lr=0.0001))
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("weight_idol_f.npz")
print("============================================")
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("============================================")
net.add_layer(
Layer.Dense(10,
initializer=Initializer.He(),
activation=Layer.SoftmaxWithLoss()))
result = net.train(x_train,
t_train,
x_test,
t_test,
batch_size=300,
iters_num=1000,
print_epoch=1,
is_use_progress_bar=True,
save_model_each_epoch=1,
save_model_path="./dog_cat_result",
optimizer=Optimizer.Adam(lr=0.01))
print("done!")
print(net.params)
# print(net.layers)
# net.save_model()
# net.load_model("weight.npz")
print("============================================")
# net2 = MultiLayerNet()
# net2.load_model("weight.npz")
#