RNN - RuntimeError: input must have 3 dimensions, got 2

Question

我收到以下错误：

RuntimeError: input must have 3 dimensions, got 2

我有一个特征列，我正在尝试将其输入 GRU 神经网络。

下面是我的数据加载器和神经网络。当我检索一批数据时，我还包括了我的数据加载器的输出。

我做错了什么？？？

def batch_data(feature1, sequence_length, batch_size):
“”"
Batch the neural network data using DataLoader
:param feature1: the single feature column
:param sequence_length: The sequence length of each batch
:param batch_size: The size of each batch; the number of sequences in a batch
:return: DataLoader with batched data
“”"
    # total number of batches we can make
    n_batches = len(feature1)//batch_size

    # Keep only enough characters to make full batches
    feature1= feature1[:n_batches * batch_size]

    y_len = len(feature1) - sequence_length

    x, y = [], []
    for idx in range(0, y_len):
        idx_end = sequence_length + idx
        x_batch = feature1[idx:idx_end]
        x.append(x_batch)
        # only making predictions after the last item in the batch
        batch_y = feature1[idx_end]    
        y.append(batch_y)    

    # create tensor datasets
    data = TensorDataset(torch.from_numpy(np.asarray(x)), torch.from_numpy(np.asarray(y)))

    data_loader = DataLoader(data, shuffle=False, batch_size=batch_size)

    # return a dataloader
    return data_loader



# test dataloader on subset of actual data

test_text = data_subset_b
t_loader = batch_data(test_text, sequence_length=5, batch_size=10)
 
data_iter = iter(t_loader)
sample_x, sample_y = data_iter.next()
 
print(sample_x.shape)
print(sample_x)
print()
print(sample_y.shape)
print(sample_y)

当我传入数据时，生成了以下批次…

torch.Size([10, 5])
tensor([[ 0.0045, 0.0040, -0.0008, 0.0005, -0.0012],
[ 0.0040, -0.0008, 0.0005, -0.0012, 0.0000],
[-0.0008, 0.0005, -0.0012, 0.0000, -0.0015],
[ 0.0005, -0.0012, 0.0000, -0.0015, 0.0008],
[-0.0012, 0.0000, -0.0015, 0.0008, 0.0000],
[ 0.0000, -0.0015, 0.0008, 0.0000, 0.0000],
[-0.0015, 0.0008, 0.0000, 0.0000, -0.0008],
[ 0.0008, 0.0000, 0.0000, -0.0008, -0.0039],
[ 0.0000, 0.0000, -0.0008, -0.0039, -0.0026],
[ 0.0000, -0.0008, -0.0039, -0.0026, -0.0082]], dtype=torch.float64)

torch.Size([10])
tensor([ 0.0000, -0.0015, 0.0008, 0.0000, 0.0000, -0.0008, -0.0039, -0.0026,
-0.0082, 0.0078], dtype=torch.float64)

Answer 1

根据您得到的错误提示，GRU 预期的输入张量形状是三维的，形状为 (batch_size, seq_len, input_size)1

但是你正在输入一个形状为 (10, 5) 的张量。你说你的输入有一个特征值，所以你应该为 input_size 添加一个大小为 1 的维度。这可以像这样完成

sample_x.unsqueeze(-1)

Answer 2

实际上错误本身会告诉您问题所在。 RNN class 是 GRU 的超级 class，期望输入形状为：

 (#batch,#number_of_timesteps,#number_of_features)

所以对于你的案例，你有 1 个特征，5 个时间步长。在您的数据加载器中，您需要将 X 扩展为 (#batch,5,1)。

RNN - RuntimeError: input must have 3 dimensions, got 2

RNN - RuntimeError: input must have 3 dimensions, got 2

python

deep-learning

recurrent-neural-network

gated-recurrent-unit

pytorch