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 = MultiLayerNet() net.add_layer(Layer.Dense(1, input_size=2, activation=Layer.IdentityWithLoss())) # net.add_layer(Layer.Dense(5, input_size = 2, activation=Layer.Relu() )) # net.add_layer(Layer.Dense(1)) x_train, t_train, x_test, t_test = shuffle_split_data(x_data, t_data, 0.2) print(net.params) # scaler = Scaler.StandardScaler() # x_train = scaler.fit_transform(x_train) # x_test = scaler.transform(x_test) result = net.train(x_train, t_train, x_test, t_test, batch_size=100, iters_num=1000, print_epoch=30, optimizer=Optimizer.SGD(lr=0.001)) print("done!") print(net.params) print(net.layers["Affine0"].__dict__) net.save_model() # net.load_model("weight.npz")
net.add_layer(Layer.BatchNormalization()) net.add_layer(Layer.Relu()) net.add_layer(Layer.Dense(64), initializer=Initializer.He()) net.add_layer(Layer.BatchNormalization()) net.add_layer(Layer.Relu()) 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")