Python Dense Examples

Example #1

File: train.py Project: woduq1414/deep-learning-without-framework

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

Example #2

File: cupy_test.py Project: woduq1414/deep-learning-without-framework

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("============================================")

Example #3

File: train.py Project: woduq1414/deep-learning-without-framework

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

Example #4

File: regression_test.py Project: woduq1414/deep-learning-without-framework

    y = np.dot(x, np.array([2, 1])) + 3
    #     y =  y + (4 * np.random.random_sample((1,y.shape[0])) - 2).flatten()
    t = np.reshape(y, (y.shape[0], 1))

    # print(x)

    return x, t


x_data, t_data = make_sample_data_set_regression3()

print(x_data[:3])
print(t_data[:3])

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,

Example #5

File: classification_test.py Project: woduq1414/deep-learning-without-framework

    #     y =  y + (4 * np.random.random_sample((1,y.shape[0])) - 2).flatten()
    t = np.reshape(y, (y.shape[0], 1))

    return x, t


(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True,
                                                  one_hot_label=True)

print(x_train.shape, t_train.shape, x_test.shape, t_test.shape)

x_data = np.append(x_train, x_test, axis=0)
t_data = np.append(t_train, t_test, axis=0)

net = MultiLayerNet(is_use_dropout=True, dropout_ratio=0.2)
net.add_layer(Layer.Dense(30, input_size=784, initializer=Initializer.He()))
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(64), initializer=Initializer.He())
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(64), initializer=Initializer.He())
net.add_layer(Layer.BatchNormalization())
net.add_layer(Layer.Relu())
net.add_layer(