input batch_size と match target batch_size に関するエラー

Question

機械学習のコードを作成しており，入力xはshape=[1,64],yはshape=[1,6]です．for文内ではそれぞれxはshape[64],
yはshape=[6]になります．LogisticRegression(nn.Module)をモデルとして，作成したら損失の計算時にエラーが出ました．
サイズの大きさがあわないとのエラーなのでview関数を用いたのですが，直りませんでした．どなたか，ご教示の程，よろしくお願い致します．

###エラー文

  Traceback (most recent call last):
  File "C:/Users/name/Desktop/myo-python-1.0.4/bindsnet-master/bindsnet/nextrsnn.py", line 101, in <module>
    loss = criterion(output.unsqueeze(0),  y.unsqueeze(-1).long())
  File "C:\Python36\lib\site-packages\torch\nn\modules\module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "C:\Python36\lib\site-packages\torch\nn\modules\loss.py", line 948, in forward
    ignore_index=self.ignore_index, reduction=self.reduction)
  File "C:\Python36\lib\site-packages\torch\nn\functional.py", line 2422, in cross_entropy
    return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
  File "C:\Python36\lib\site-packages\torch\nn\functional.py", line 2216, in nll_loss
    .format(input.size(0), target.size(0)))
ValueError: Expected input batch_size (1) to match target batch_size (6).
Train progress: (500 / 500)


Process finished with exit code 1

試したこと　view(-1,6)

        optimizer.zero_grad(); output = model(s.float()).view(-1,6)
        loss = criterion(output.unsqueeze(0),  y.unsqueeze(-1).long())
        loss.backward(); optimizer.step()

###コード

import torch.nn as nn
import numpy as np
from bindsnet.network import Network
from bindsnet.network.nodes import Input, LIFNodes
from bindsnet.network.topology import Connection
from bindsnet.network.monitors import Monitor

# モデルの定義
class LogisticRegression(nn.Module):
    def __init__(self, input_size, num_classes):
        super(LogisticRegression, self).__init__()
        self.linear = nn.Linear(input_size, num_classes)
        self.dropout = nn.Dropout(0.5)

    def forward(self, x):
        x = x.view(-1, 64)
        return self.linear(x)

# ネットワーク作成
input_size = 64
num_classes = 6
time = 64      
network = Network(dt=1.0)
_BATCH_SIZE = 300
# ニューロンのレイヤー 5層作成
inpt = Input(n=64, sum_input=True)
middle = LIFNodes(n=40, trace=True, sum_input=True)
center = LIFNodes(n=40, trace=True, sum_input=True)
final = LIFNodes(n=40, trace=True, sum_input=True)
out = LIFNodes(n=6, sum_input=True)

# レイヤー同士の接続
inpt_middle = Connection(source=inpt, target=middle, wmin=0, wmax=1e-1)
middle_center = Connection(source=middle, target=center, wmin=0, wmax=1e-1)
center_final = Connection(source=center, target=final, wmin=0, wmax=1e-1)
final_out = Connection(source=final, target=out, wmin=0, wmax=1e-1)

# 全てのレイヤー5層をネットワークに接続
network.add_layer(inpt, name='A')
network.add_layer(middle, name='B')
network.add_layer(center, name='C')
network.add_layer(final,  name='D')
network.add_layer(out, name='E')

forward_connection = Connection(source=inpt, target=middle, w=0.05 + 0.1*torch.randn(inpt.n, middle.n))
network.add_connection(connection=forward_connection, source="A", target="B")
forward_connection = Connection(source=middle, target=center, w=0.05 + 0.1*torch.randn(middle.n, center.n))
network.add_connection(connection=forward_connection, source="B", target="C")
forward_connection = Connection(source=center, target=final, w=0.05 + 0.1*torch.randn(center.n, final.n))
network.add_connection(connection=forward_connection, source="C", target="D")
forward_connection = Connection(source=final, target=out, w=0.05 + 0.1*torch.randn(final.n, out.n))
network.add_connection(connection=forward_connection, source="D", target="E")
recurrent_connection = Connection(source=out, target=out, w=0.025*(torch.eye(out.n)-1),)
network.add_connection(connection=recurrent_connection, source="E", target="E")

# 入力と出力層だけMonitorを作成(電圧とスパイクを記録)
inpt_monitor = Monitor(obj=inpt, state_vars=("s", "v"), time=500,)
middle_monitor = Monitor(obj=inpt, state_vars=("s", "v"), time=500,)
center_monitor = Monitor(obj=inpt, state_vars=("s", "v"), time=500,)
final_monitor = Monitor(obj=inpt, state_vars=("s", "v"), time=500,)
out_monitor = Monitor(obj=inpt, state_vars=("s", "v"), time=500,)

# Monitorをネットワークに接続
network.add_monitor(monitor=inpt_monitor, name="A")
network.add_monitor(monitor=middle_monitor, name="B")
network.add_monitor(monitor=center_monitor, name="C")
network.add_monitor(monitor=final_monitor, name="D")
network.add_monitor(monitor=out_monitor, name="E")

for l in network.layers:
    m = Monitor(network.layers[l], state_vars=['s'], time=time)
    network.add_monitor(m, name=l)

# トレーニングデータをロード 　x = shape[1,64] y = [1,6]
npzfile = np.load("C:/Users/name/Desktop/myo-python-1.0.4/myo-armband-nn-master/data/train_set.npz")
x = npzfile['x']
y = npzfile['y']

# tensor型に変換
x = torch.from_numpy(x).float()
y = torch.from_numpy(y).float()

training_pairs = []
for i, (x, y) in enumerate(zip(x.view(-1, 64), y)):
    inputs = {'A': x.repeat(time, 1), 'E_b': torch.ones(time, 1)}
    network.run(inputs=inputs, time=time)
    training_pairs.append([network.monitors['E'].get('s').sum(-1), y])
    network.reset_state_variables()

    if (i + 1) % 50 == 0: print('Train progress: (%d / 500)' % (i + 1))
    if (i + 1) == 500: print(); break

model = LogisticRegression(input_size, num_classes); criterion = nn.CrossEntropyLoss()   # m2に相当
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
# スパイクとラベルのトレーニング
for epoch in range(6):
    for i, (s, y) in enumerate(training_pairs):
        optimizer.zero_grad(); output = model(s.float()).view(-1,6)
        loss = criterion(output.unsqueeze(0),  y.unsqueeze(-1).long()) **☚ここがエラー箇所です．**
        loss.backward(); optimizer.step()

test_pairs = []
for i, (x, y) in enumerate(zip(x.view(-1, 64), y)):
    inputs = {'A': x.repeat(time, 1), 'E_b': torch.ones(time, 1)}
    network.run(inputs=inputs, time=time)
    test_pairs.append([network.monitors['E'].get('s').sum(-1), y])
    network.reset_state_variables()

    if (i + 1) % 50 == 0: print('Test progress: (%d / 500)' % (i + 1))
    if (i + 1) == 500: print(); break

correct, total = 0, 0
for s, y in test_pairs:
    output = model(s); _, predicted = torch.max(output.data.unsueeze(0), 1)
    total += 1; correct += int(predicted == y.long())

print('Accuracy of logistic regression on 500 test examples: %2f %%\n ' % (100 * correct / total))
torch.save({
            'epoch': epoch,
            'model_state_dict': model.state_dict(),
            'optimizer_state_dict': optimizer.state_dict(),
            'loss': loss,
            }, "C:/Users/name/Desktop/myo-python-1.0.4/bindsnet-master/bindsnet/pytorchsession")