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  • RNN 循环神经网络 回归 地平线上的背影关注 RNN网络较少运用于回归任务,但是并不代表其不可运用于回归任务。本文通过简单回归任务的RNN进一步加深对RNN的理解 1. 准备数据和超参数 import torch from torch ...

    RNN 循环神经网络 回归

    地平线上的背影关注

    RNN网络较少运用于回归任务,但是并不代表其不可运用于回归任务。本文通过简单回归任务的RNN进一步加深对RNN的理解

    1. 准备数据和超参数

    import torch
    from torch import nn
    import numpy as np
    import matplotlib.pyplot as plt
    
    # torch.manual_seed(1)    # reproducible
    
    # Hyper Parameters
    TIME_STEP = 10      # rnn time step
    INPUT_SIZE = 1      # rnn input size
    LR = 0.02           # learning rate
    
    # show data
    steps = np.linspace(0, np.pi*2, 100, dtype=np.float32)  # float32 for converting torch FloatTensor
    x_np = np.sin(steps)
    y_np = np.cos(steps)
    plt.plot(steps, y_np, 'r-', label='target (cos)')
    plt.plot(steps, x_np, 'b-', label='input (sin)')
    plt.legend(loc='best')
    plt.show()
    

    2. 构建RNN神经网络

    class RNN(nn.Module):
        def __init__(self):
            super(RNN, self).__init__()
    
            self.rnn = nn.RNN(
                input_size=INPUT_SIZE,
                hidden_size=32,     # rnn hidden unit
                num_layers=1,       # number of rnn layer
                batch_first=True,   
    # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)
            )
            self.out = nn.Linear(32, 1)
    
        def forward(self, x, h_state):
            # x (batch, time_step, input_size)
            # h_state (n_layers, batch, hidden_size)
            # r_out (batch, time_step, hidden_size)
            r_out, h_state = self.rnn(x, h_state)
    
            outs = []    # save all predictions
            for time_step in range(r_out.size(1)):    # calculate output for each time step
                outs.append(self.out(r_out[:, time_step, :]))
            return torch.stack(outs, dim=1), h_state
    
            # instead, for simplicity, you can replace above codes by follows
            # r_out = r_out.view(-1, 32)
            # outs = self.out(r_out)
            # outs = outs.view(-1, TIME_STEP, 1)
            # return outs, h_state
            
            # or even simpler, since nn.Linear can accept inputs of any dimension 
            # and returns outputs with same dimension except for the last
            # outs = self.out(r_out)
            # return outs
    
    rnn = RNN()
    print(rnn)
    

    3. 选择优化器和损失函数

    optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # optimize all cnn parameters
    loss_func = nn.MSELoss()
    

    注:

    1. adam():我们常使用该函数,因为其在各种情况下均有较好的表现
    2. MSELoss():均方误差损失函数,常用于预测模型中

    4. 初始化数据和plt图像

    h_state = None      # for initial hidden state
    
    plt.figure(1, figsize=(12, 5))
    plt.ion()           # continuously plot
    

    5. 训练和优化

    for step in range(100):
        start, end = step * np.pi, (step+1)*np.pi   # time range
        # use sin predicts cos
        steps = np.linspace(start, end, TIME_STEP, dtype=np.float32, endpoint=False)  
    # float32 for converting torch FloatTensor
        x_np = np.sin(steps)
        y_np = np.cos(steps)
    
        x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis])    
    # shape (batch, time_step, input_size)
        y = torch.from_numpy(y_np[np.newaxis, :, np.newaxis])
    
        prediction, h_state = rnn(x, h_state)   # rnn output
        # !! next step is important !!
        h_state = h_state.data        
    # repack the hidden state, break the connection from last iteration
    
        loss = loss_func(prediction, y)         # calculate loss
        optimizer.zero_grad()                   # clear gradients for this training step
        loss.backward()                         # backpropagation, compute gradients
        optimizer.step()                        # apply gradients
    
        # plotting
        plt.plot(steps, y_np.flatten(), 'r-')
        plt.plot(steps, prediction.data.numpy().flatten(), 'b-')
        plt.draw(); plt.pause(0.05)
    
    plt.ioff()
    plt.show()
    展开全文
  • 本文主要介绍使用RNN来实现回归,对曲线进行预测。 示例代码: import numpy as np from keras.datasets import mnist from keras.utils import np_utils from keras.models import Sequential from keras.layers...

    本文主要介绍使用RNN来实现回归,对曲线进行预测。

    示例代码:

    import numpy as np
    from keras.datasets import mnist
    from keras.utils import np_utils
    from keras.models import Sequential
    from keras.layers import Dense, TimeDistributed, LSTM
    from keras.optimizers import Adam
    import matplotlib.pyplot as plt
    
    # 使多次生成的随机数相同
    np.random.seed(1337)
    
    # 超参数
    BATCH_START = 0
    TIME_STEPS = 20
    BATCH_SIZE = 50
    INPUT_SIZE = 1
    OUTPUT_SIZE = 1
    CELL_SIZE = 20
    LR = 0.006
    
    
    # 生成数据
    def get_batch():
        global BATCH_START, TIME_STEPS
        # xs shape (50batch, 20steps)
        xs = np.arange(BATCH_START, BATCH_START+TIME_STEPS*BATCH_SIZE).reshape((BATCH_SIZE, TIME_STEPS)) / (10*np.pi)
        seq = np.sin(xs)
        res = np.cos(xs)
        BATCH_START += TIME_STEPS
        # plt.plot(xs[0, :], res[0, :], 'r', xs[0, :], seq[0, :], 'b--')
        # plt.show()
        return [seq[:, :, np.newaxis], res[:, :, np.newaxis], xs]
    
    # 查看数据
    # get_batch()
    # exit()
    # 搭建网络
    model = Sequential()
    
    # 添加LSTM层
    model.add(LSTM(
        batch_input_shape=(BATCH_SIZE, TIME_STEPS, INPUT_SIZE),
        output_dim=CELL_SIZE,
        return_sequences=True,  # 对于每一个时间点需不需要输出对应的output, True每个时刻都输出, False最后的输出output
        stateful=True,  # batch与batch之间是否有联系,需不需要将状态进行传递
    ))
    # add output layer
    model.add(TimeDistributed(Dense(OUTPUT_SIZE)))  # TimeDistributed:对每一个output进行全连接的计算
    
    # 优化器
    adam = Adam()
    model.compile(
        optimizer=adam,
        loss='mse',
    )
    
    # 训练
    print('Training ------------')
    for step in range(501):
        # data shape = (batch_num, steps, inputs/outputs)
        X_batch, Y_batch, xs = get_batch()
        cost = model.train_on_batch(X_batch, Y_batch)
        pred = model.predict(X_batch, BATCH_SIZE)
        plt.plot(xs[0, :], Y_batch[0].flatten(), 'r', xs[0, :], pred.flatten()[:TIME_STEPS], 'b--')
        plt.ylim((-1.2, 1.2))
        plt.draw()
        plt.pause(0.1)
        if step % 10 == 0:
            print('train cost: ', cost)

    数据示例:

    def get_batch():
        global BATCH_START, TIME_STEPS
        # xs shape (50batch, 20steps)
        xs = np.arange(BATCH_START, BATCH_START+TIME_STEPS*BATCH_SIZE).reshape((BATCH_SIZE, TIME_STEPS)) / (1*np.pi)
        seq = np.sin(xs)
        res = np.cos(xs)
        BATCH_START += TIME_STEPS
        plt.plot(xs[0, :], res[0, :], 'r', xs[0, :], seq[0, :], 'b--')
        plt.show()
        return [seq[:, :, np.newaxis], res[:, :, np.newaxis], xs]
    
    # 查看数据
    get_batch()
    exit()

    结果:

     

    train cost:  0.50940645
    train cost:  0.4966624
    train cost:  0.48060146
    train cost:  0.45672885
    train cost:  0.4108651
    train cost:  0.31347314
    train cost:  0.12554297
    train cost:  0.07388962
    train cost:  0.10137392
    train cost:  0.046597198
    train cost:  0.05946522
    train cost:  0.040294208
    train cost:  0.053411756
    train cost:  0.15622795
    train cost:  0.17914045
    train cost:  0.16356382
    train cost:  0.21077277
    train cost:  0.20014948
    train cost:  0.18070495
    train cost:  0.16142645
    train cost:  0.19912449
    train cost:  0.16934186
    train cost:  0.16477375
    train cost:  0.17521137
    train cost:  0.20553884
    train cost:  0.15104571
    train cost:  0.16296455
    train cost:  0.16819069
    train cost:  0.11465822
    train cost:  0.14150377
    train cost:  0.13508156
    train cost:  0.13755415
    train cost:  0.13000277
    train cost:  0.11969448
    train cost:  0.09293661
    train cost:  0.0819223
    train cost:  0.06903682
    train cost:  0.07125411
    train cost:  0.08032415
    train cost:  0.07321488
    train cost:  0.096763514
    train cost:  0.078285255
    train cost:  0.07236056
    train cost:  0.065320924
    train cost:  0.057717755
    train cost:  0.063192114
    train cost:  0.047402352
    train cost:  0.05537389
    train cost:  0.051893406
    train cost:  0.052938405
    train cost:  0.05649735

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    展开全文
  • 循环神经网络让神经网络有了记忆, 对于序列话的数据,循环神经网络能达到更好的效果.上次我们提到了用 RNN 的最后一个时间点输出来判断之前看到的图片属于哪一类, 这次我们来真的了, 用 RNN 来及时预测时间序列. ...

    目录

    1.写在前面

    2.训练数据

    3.RNN模型

    4.训练

    5.完整代码演示


    1.写在前面

            循环神经网络让神经网络有了记忆, 对于序列话的数据,循环神经网络能达到更好的效果.上次我们提到了用 RNN 的最后一个时间点输出来判断之前看到的图片属于哪一类, 这次我们来真的了, 用 RNN 来及时预测时间序列.

    2.训练数据

            我们要用到的数据就是这样的一些数据, 我们想要用 sin 的曲线预测出 cos 的曲线.

    import torch
    from torch import nn
    import numpy as np
    import matplotlib.pyplot as plt
    
    torch.manual_seed(1)    # reproducible
    
    # Hyper Parameters
    TIME_STEP = 10      # rnn time step / image height
    INPUT_SIZE = 1      # rnn input size / image width
    LR = 0.02           # learning rate
    DOWNLOAD_MNIST = False  # set to True if haven't download the data

    3.RNN模型

            这一次的 RNN, 我们对每一个 r_out 都得放到 Linear 中去计算出预测的 output, 所以我们能用一个 for loop 来循环计算. 这点是 Tensorflow 望尘莫及的! 看看我们的 PyTorch 和 Tensorflow 到底哪家强.

    class RNN(nn.Module):
        def __init__(self):
            super(RNN, self).__init__()
    
            self.rnn = nn.RNN(  # 这回一个普通的 RNN 就能胜任
                input_size=1,
                hidden_size=32,     # rnn hidden unit
                num_layers=1,       # 有几层 RNN layers
                batch_first=True,   # input & output 会是以 batch size 为第一维度的特征集 e.g. (batch, time_step, input_size)
            )
            self.out = nn.Linear(32, 1)
    
        def forward(self, x, h_state):  # 因为 hidden state 是连续的, 所以我们要一直传递这一个 state
            # x (batch, time_step, input_size)
            # h_state (n_layers, batch, hidden_size)
            # r_out (batch, time_step, output_size)
            r_out, h_state = self.rnn(x, h_state)   # h_state 也要作为 RNN 的一个输入
    
            outs = []    # 保存所有时间点的预测值
            for time_step in range(r_out.size(1)):    # 对每一个时间点计算 output
                outs.append(self.out(r_out[:, time_step, :]))
            return torch.stack(outs, dim=1), h_state
    
    
    rnn = RNN()
    print(rnn)
    """
    RNN (
      (rnn): RNN(1, 32, batch_first=True)
      (out): Linear (32 -> 1)
    )
    """

            其实熟悉 RNN 的朋友应该知道, forward 过程中的对每个时间点求输出还有一招使得计算量比较小的. 不过上面的内容主要是为了呈现 PyTorch 在动态构图上的优势, 所以我用了一个 for loop 来搭建那套输出系统. 下面介绍一个替换方式. 使用 reshape 的方式整批计算.

    def forward(self, x, h_state):
        r_out, h_state = self.rnn(x, h_state)
        r_out = r_out.view(-1, 32)
        outs = self.out(r_out)
        return outs.view(-1, 32, TIME_STEP), h_state

    4.训练

            下面的代码就能实现动图的效果啦~开心, 可以看出, 我们使用 x 作为输入的 sin 值, 然后 y 作为想要拟合的输出, cos 值. 因为他们两条曲线是存在某种关系的, 所以我们就能用 sin 来预测 cosrnn 会理解他们的关系, 并用里面的参数分析出来这个时刻 sin 曲线上的点如何对应上 cos 曲线上的点.

    optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # optimize all rnn parameters
    loss_func = nn.MSELoss()
    
    h_state = None   # 要使用初始 hidden state, 可以设成 None
    
    for step in range(100):
        start, end = step * np.pi, (step+1)*np.pi   # time steps
        # sin 预测 cos
        steps = np.linspace(start, end, 10, dtype=np.float32)
        x_np = np.sin(steps)    # float32 for converting torch FloatTensor
        y_np = np.cos(steps)
    
        x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis])    # shape (batch, time_step, input_size)
        y = torch.from_numpy(y_np[np.newaxis, :, np.newaxis])
    
        prediction, h_state = rnn(x, h_state)   # rnn 对于每个 step 的 prediction, 还有最后一个 step 的 h_state
        # !!  下一步十分重要 !!
        h_state = h_state.data  # 要把 h_state 重新包装一下才能放入下一个 iteration, 不然会报错
    
        loss = loss_func(prediction, y)     # cross entropy loss
        optimizer.zero_grad()               # clear gradients for this training step
        loss.backward()                     # backpropagation, compute gradients
        optimizer.step()                    # apply gradients

    5.完整代码演示

    import torch
    from torch import nn
    import numpy as np
    import matplotlib.pyplot as plt
    
    # torch.manual_seed(1)    # reproducible
    
    # Hyper Parameters
    TIME_STEP = 10      # rnn time step
    INPUT_SIZE = 1      # rnn input size
    LR = 0.02           # learning rate
    
    # show data
    steps = np.linspace(0, np.pi*2, 100, dtype=np.float32)  # float32 for converting torch FloatTensor
    x_np = np.sin(steps)
    y_np = np.cos(steps)
    plt.plot(steps, y_np, 'r-', label='target (cos)')
    plt.plot(steps, x_np, 'b-', label='input (sin)')
    plt.legend(loc='best')
    plt.show()
    
    
    class RNN(nn.Module):
        def __init__(self):
            super(RNN, self).__init__()
    
            self.rnn = nn.RNN(
                input_size=INPUT_SIZE,
                hidden_size=32,     # rnn hidden unit
                num_layers=1,       # number of rnn layer
                batch_first=True,   # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)
            )
            self.out = nn.Linear(32, 1)
    
        def forward(self, x, h_state):
            # x (batch, time_step, input_size)
            # h_state (n_layers, batch, hidden_size)
            # r_out (batch, time_step, hidden_size)
            r_out, h_state = self.rnn(x, h_state)
    
            outs = []    # save all predictions
            for time_step in range(r_out.size(1)):    # calculate output for each time step
                outs.append(self.out(r_out[:, time_step, :]))
            return torch.stack(outs, dim=1), h_state
    
            # instead, for simplicity, you can replace above codes by follows
            # r_out = r_out.view(-1, 32)
            # outs = self.out(r_out)
            # outs = outs.view(-1, TIME_STEP, 1)
            # return outs, h_state
            
            # or even simpler, since nn.Linear can accept inputs of any dimension 
            # and returns outputs with same dimension except for the last
            # outs = self.out(r_out)
            # return outs
    
    rnn = RNN()
    print(rnn)
    
    optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # optimize all cnn parameters
    loss_func = nn.MSELoss()
    
    h_state = None      # for initial hidden state
    
    plt.figure(1, figsize=(12, 5))
    plt.ion()           # continuously plot
    
    for step in range(100):
        start, end = step * np.pi, (step+1)*np.pi   # time range
        # use sin predicts cos
        steps = np.linspace(start, end, TIME_STEP, dtype=np.float32, endpoint=False)  # float32 for converting torch FloatTensor
        x_np = np.sin(steps)
        y_np = np.cos(steps)
    
        x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis])    # shape (batch, time_step, input_size)
        y = torch.from_numpy(y_np[np.newaxis, :, np.newaxis])
    
        prediction, h_state = rnn(x, h_state)   # rnn output
        # !! next step is important !!
        h_state = h_state.data        # repack the hidden state, break the connection from last iteration
    
        loss = loss_func(prediction, y)         # calculate loss
        optimizer.zero_grad()                   # clear gradients for this training step
        loss.backward()                         # backpropagation, compute gradients
        optimizer.step()                        # apply gradients
    
        # plotting
        plt.plot(steps, y_np.flatten(), 'r-')
        plt.plot(steps, prediction.data.numpy().flatten(), 'b-')
        plt.draw(); plt.pause(0.05)
    
    plt.ioff()
    plt.show()

     

    展开全文
  • RNN 循环神经网络 (回归)

    千次阅读 2019-02-18 15:24:23
    循环神经网络让神经网络有了记忆, 对于序列话的数据,循环神经网络能达到更好的效果. 如果你对循环神经网络还没有特别了解, 请观看几分钟的短动画, RNN 动画简介 和 LSTM 动画简介 能让你生...

    学习资料:

    要点 

    循环神经网络让神经网络有了记忆, 对于序列话的数据,循环神经网络能达到更好的效果. 如果你对循环神经网络还没有特别了解, 请观看几分钟的短动画, RNN 动画简介 和 LSTM 动画简介 能让你生动理解 RNN. 上次我们提到了用 RNN 的最后一个时间点输出来判断之前看到的图片属于哪一类, 这次我们来真的了, 用 RNN 来及时预测时间序列.

    RNN 循环神经网络 (回归)

    训练数据 

    我们要用到的数据就是这样的一些数据, 我们想要用 sin 的曲线预测出 cos 的曲线.

    RNN 循环神经网络 (回归)

    import torch
    from torch import nn
    import numpy as np
    import matplotlib.pyplot as plt
    from torch.autograd import Variable
    
    torch.manual_seed(1)    # reproducible
    
    # Hyper Parameters
    TIME_STEP = 10      # rnn time step / image height
    INPUT_SIZE = 1      # rnn input size / image width
    LR = 0.02           # learning rate
    DOWNLOAD_MNIST = False  # set to True if haven't download the data
    

    RNN模型 

    这一次的 RNN, 我们对每一个 r_out 都得放到 Linear 中去计算出预测的 output, 所以我们能用一个 for loop 来循环计算. 这点是 Tensorflow 望尘莫及的! 除了这点, 还有一些动态的过程都可以在这个教程中查看, 看看我们的 PyTorch 和 Tensorflow 到底哪家强.

    class RNN(nn.Module):
        def __init__(self):
            super(RNN, self).__init__()
    
            self.rnn = nn.RNN(  # 这回一个普通的 RNN 就能胜任
                input_size=1,
                hidden_size=32,     # rnn hidden unit
                num_layers=1,       # 有几层 RNN layers
                batch_first=True,   # input & output 会是以 batch size 为第一维度的特征集 e.g. (batch, time_step, input_size)
            )
            self.out = nn.Linear(32, 1)
    
        def forward(self, x, h_state):  # 因为 hidden state 是连续的, 所以我们要一直传递这一个 state
            # x (batch, time_step, input_size)
            # h_state (n_layers, batch, hidden_size)
            # r_out (batch, time_step, output_size)
            r_out, h_state = self.rnn(x, h_state)   # h_state 也要作为 RNN 的一个输入
    
            outs = []    # 保存所有时间点的预测值
            for time_step in range(r_out.size(1)):    # 对每一个时间点计算 output
                outs.append(self.out(r_out[:, time_step, :]))
            return torch.stack(outs, dim=1), h_state
    
    
    rnn = RNN()
    print(rnn)
    """
    RNN (
      (rnn): RNN(1, 32, batch_first=True)
      (out): Linear (32 -> 1)
    )
    """
    

    其实熟悉 RNN 的朋友应该知道, forward 过程中的对每个时间点求输出还有一招使得计算量比较小的. 不过上面的内容主要是为了呈现 PyTorch 在动态构图上的优势, 所以我用了一个 for loop 来搭建那套输出系统. 下面介绍一个替换方式. 使用 reshape 的方式整批计算.

    def forward(self, x, h_state):
        r_out, h_state = self.rnn(x, h_state)
        r_out = r_out.view(-1, 32)
        outs = self.out(r_out)
        return outs.view(-1, 32, TIME_STEP), h_state
    

    训练 

    下面的代码就能实现动图的效果啦~开心, 可以看出, 我们使用 x 作为输入的 sin 值, 然后 y作为想要拟合的输出, cos 值. 因为他们两条曲线是存在某种关系的, 所以我们就能用 sin 来预测 cosrnn 会理解他们的关系, 并用里面的参数分析出来这个时刻 sin 曲线上的点如何对应上 cos 曲线上的点.

    RNN 循环神经网络 (回归)

    optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # optimize all rnn parameters
    loss_func = nn.MSELoss()
    
    h_state = None   # 要使用初始 hidden state, 可以设成 None
    
    for step in range(100):
        start, end = step * np.pi, (step+1)*np.pi   # time steps
        # sin 预测 cos
        steps = np.linspace(start, end, 10, dtype=np.float32)
        x_np = np.sin(steps)    # float32 for converting torch FloatTensor
        y_np = np.cos(steps)
    
        x = Variable(torch.from_numpy(x_np[np.newaxis, :, np.newaxis]))    # shape (batch, time_step, input_size)
        y = Variable(torch.from_numpy(y_np[np.newaxis, :, np.newaxis]))
    
        prediction, h_state = rnn(x, h_state)   # rnn 对于每个 step 的 prediction, 还有最后一个 step 的 h_state
        # !!  下一步十分重要 !!
        h_state = h_state.data  # 要把 h_state 重新包装一下才能放入下一个 iteration, 不然会报错
    
        loss = loss_func(prediction, y)     # cross entropy loss
        optimizer.zero_grad()               # clear gradients for this training step
        loss.backward()                     # backpropagation, compute gradients
        optimizer.step()                    # apply gradients
    

    RNN 循环神经网络 (回归)

    所以这也就是在我 github 代码 中的每一步的意义啦.

     

    展开全文
  • 重要:网络中的初始状态赋值为零,在下一次的时候一定要将上一次生成的隐层状态包装为variable """ import torch from torch import nn from torch.autograd import Variable import numpy as np import ...
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  • 一、RNN ...对循环神经网络的研究始于二十世纪80-90年代,并在二十一世纪初发展为深度学习(deep learning)算法之一 [2] ,其中双向循环神经网络(Bidirectional RNN, Bi-RNN)和长短期记忆网络
  • RNN回归可以用来及时预测时间序列 训练数据 我们要用sin的曲线预测出cos的曲线。 import torch from torch import nn import numpy as np import matplotlib.pyplot as plt torch.manual_seed(1) TIME...
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  • 实验结果: 这次用RNN_LSTM实现回归任务 ...可参考pytorch搭建CNN卷积神经网络详解 ???? 搭建RNN(该任务使用RNN足矣) class RNN(nn.Module): def __init__(self): super(RNN, self)._...
  • RNN_lstm 循环神经网络 - 回归任务

    千次阅读 2018-04-30 19:02:35
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  • “微信公众号” 一、回归预测要实现的问题 这次我们会使用RNN来进行回归(Regression)的训练,使用自己创建的sin曲线预测一条cos曲线。...经过RNN的回归训练,我们的网络预测结果和真实结果的一个比对图...
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