CODEWITHSUNDEEP
pythonpytorch
GeverAL
GeverAL

Reputation: 21

This torch project keep telling me "Expected 2 or more dimensions (got 1)"

I was trying to make my own neural network using PyTorch. I do not understand why my code is not working properly.

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optimizers
import numpy as np
from tqdm import tqdm
import os
import hashlib

# Only for the first time
MAKE_DATA = False


class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(2, 100)
        self.fc2 = nn.Linear(100, 200)
        self.fc3 = nn.Linear(200, 200)
        self.fc4 = nn.Linear(200, 1)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        x = self.fc4(x)
        return F.relu(x)


def make_numirical(data):
    data = str(data)
    data = data.encode()
    data = hashlib.md5(data).hexdigest()
    str1 = ''
    for c in data:
        if not (c >= '0' and c <= '9'):
            c = ord(c)
            if c > 10:
                c /= 10
        str1 += str(int(c))
    return int(str1[:20])


def make_train_data():
    HITS = 'Songs_DB/Hits'
    NOHITS = 'Songs_DB/NoHits'
    hits_count = 0
    no_hits_count = 0
    LABELS = {HITS: 0, NOHITS: 0}
    training_data = []
    i = 0
    for label in LABELS:
        for f in tqdm(os.listdir(label)):
            try:
                path = os.path.join(label, f)
                with open(path, 'rb') as file:
                    data = file.read()
                    file.close()
                data = make_numirical(data)
                data = int(data)
                training_data.append([np.array([data]), np.eye(2)[i]])

                if label == HITS:
                    hits_count += 1
                else:
                    no_hits_count += 1
            except:
                pass
        i += 1

        np.random.shuffle(training_data)
        np.save('training_data.npy', training_data)
        print(hits_count)
        print(no_hits_count)


if MAKE_DATA:
    make_train_data()

model = Model()

# 1 = brown, 0 = not brown, 1 = cat, 0 = dog.

Xs = torch.Tensor([[0, 1], [1, 1], [1, 0], [1, 1], [1, 1], [0, 1], [1, 1], [0, 0], [1, 0]])
ys = torch.Tensor([[1], [0], [0], [1], [0], [1], [0], [1], [1]])

i = 0
for x in Xs:
    output = model(x)
    print(output)
    loss = F.nll_loss(output, ys[i])

print(loss)

The program keeps giving me this error:

Expected 2 or more dimensions (got 1)

Can anyone explain what is wrong with my code?

Upvotes: 0

Views: 641

Answers (1)

Ivan
Ivan

Reputation: 40668

The tensor you use as the dataset, Xs is shaped (n, 2). So when looping over it each element x ends up as a 1D tensor shaped (2,). However, your module expects a batched tensor as input, i.e. here a 2D tensor shaped (n, 2), just like Xs. You have two possible options, either use a data loader and divide your dataset into batches, or unsqueeze your input x to make it two dimensional shaped (1, 2).

  • Using a TensorDataset and wrapping it with a DataLoader:

    >>> dataset = TensorDataset(Xs, ys)
>>> dataloader = Dataloader(dataset, batch_size=4)

    Then iterating over dataloader will return batches of fours (inputs and corresponding labels):

    >>> for x, y in dataloader:
...     output = model(x)
...     loss = F.nll_loss(output, y)

    TensorDataset and Dataloader are both imported from torch.utils.data.

  • Or use torch.Tensor.unsqueeze on x to add one extra dimension:

    >>> for x, y in zip(Xs, ys):
...     output = model(x.unsqueeze())
...     loss = F.nll_loss(output, y)

    Alternatively, you can do x[None] which has the same effect.

Upvotes: 1

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