PyTorch LSTM not learning in training
I have the following simple LSTM network:
class LSTMModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, layer_dim, output_dim):
        super().__init__()
        self.hidden_dim = hidden_dim
        self.layer_dim = layer_dim
        self.rnn = nn.LSTM(input_dim, hidden_dim, layer_dim, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_dim)
        self.batch_size = None
        self.hidden = None
    
    def forward(self, x):
        h0, c0 = self.init_hidden(x)
        out, (hn, cn) = self.rnn(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out
    
    def init_hidden(self, x):
        h0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim)
        c0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim)
        return [t for t in (h0, c0)]

I am initialising this model as&quot;
model = LSTMClassifier(28, 10, 6, 1)

i.e. each input instance has 6 time steps and the dimension of each time step is 28, and the hidden dimension is 10. The inputs are being mapped to an output dim of 1.
The training data is being prepared in batches of size 16, meaning that the data passed in the training loop has the shape:
torch.Size([16, 6, 28])

With labels of shape:
batches[1][0].size()

An example of the input is:
tensor([[-0.3674,  0.0347, -0.2169, -0.0821, -0.3673, -0.1773,  1.1840, -0.2669,
         -0.4202, -0.1473, -0.1132, -0.4756, -0.3565,  0.5010,  0.1274, -0.1147,
          0.2783,  0.0836, -1.3251, -0.8067, -0.6447, -0.7396, -0.3241,  1.3329,
          1.3801,  0.8198,  0.6098,  0.0697],
        [-0.2710,  0.1596, -0.2524, -0.0821, -0.3673, -0.1773,  0.0302, -0.2099,
         -0.4550,  0.1451, -0.4561, -0.5207, -0.5657, -0.5287, -0.2690, -0.1147,
         -0.0346, -0.1043, -0.7515, -0.8392, -0.4745, -0.7396, -0.3924,  0.8122,
         -0.1624, -1.2198,  0.0326, -0.9306],
        [-0.1746,  0.0972, -0.2702, -0.0821, -0.3673, -0.1773, -0.0468, -1.1225,
         -0.4480, -0.4397,  0.4011, -1.1073, -1.0536, -0.1855, -0.7502, -0.1147,
         -0.0146, -0.1545, -0.1919, -0.1674,  0.0930, -0.7396,  0.8106,  1.1594,
          0.4546, -1.2198, -0.5446, -1.2640],
        [-0.2710,  0.0660, -0.2524, -0.0821, -0.4210, -0.1773,  1.8251, -0.5236,
         -0.4410, -0.7321,  0.4011, -0.6110, -0.2171,  1.1875, -0.2973, -0.1147,
         -0.1278,  0.7728, -0.9334, -0.5141, -2.1202,  1.3521, -0.9393,  0.5085,
         -0.4709,  0.8198, -1.1218,  0.0697],
        [-0.3674, -0.0277, -0.2347, -0.0821, -0.0448, -0.1773,  0.2866, -0.1386,
         -0.4271,  0.4375, -0.2847, -0.1146, -0.4262, -0.3571, -0.0425, -0.1147,
         -0.4207, -0.4552, -0.5277, -0.9584, -0.4177, -0.7396, -0.2967,  0.5085,
          0.4546, -1.2198, -0.3522, -1.2640],
        [-0.3674, -0.1447, -0.1991, -0.0821,  0.1701, -0.1773,  0.0430,  0.1324,
         -0.4271,  0.7299, -0.4561,  0.2915, -0.5657, -0.1855, -0.2123, -0.1147,
         -0.0413, -0.8311, -0.6396, -1.0451, -0.4177, -0.7396, -0.2967, -0.4028,
          0.7631, -1.2198, -0.3522, -1.2640]])

When I train the model as:
Epochs = 10
batch_size = 32
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=5e-4)
for epoch in range(Epochs):
    print(f&quot;Epoch {epoch + 1}&quot;)
    for n, (X, y) in enumerate(batches):
        model.train()
        optimizer.zero_grad()
        y_pred = model(X)
        loss = criterion(y_pred, y)
        loss.backward()
        optimizer.step()


    model.eval()
    accurate = 0
    for X_instance, y_instance in zip(test_X, test_y):
        if y_instance == round(model(X_instance.view(-1, 6, 28)).detach().item()):
            accurate += 1
    print(f&quot;Accuracy test set: {accurate/len(test_X)}&quot;)

The accuracy does not converge:
Epoch 1
Accuracy test set: 0.23169107856191745
Sample params: 
tensor([-0.3356, -0.0105, -0.3405, -0.0049,  0.0037,  0.1707,  0.2685, -0.3893,
        -0.4707, -0.2872, -0.1544, -0.1455,  0.0393,  0.0774, -0.4194,  0.0780,
        -0.2177, -0.3829, -0.4679,  0.0370, -0.0794,  0.0455, -0.1331, -0.0169,
        -0.1551, -0.0348,  0.1746, -0.5163], grad_fn=&lt;SelectBackward&gt;)
tensor([ 0.2137, -0.2558,  0.1509, -0.0975,  0.5591,  0.0907, -0.1249,  0.3095,
         0.2112,  0.3134, -0.1581, -0.3051, -0.3559, -0.0177,  0.1485,  0.4397,
        -0.1441,  0.1705,  0.3230, -0.3236,  0.0692,  0.0920, -0.2691, -0.3695,
        -0.0692,  0.3747,  0.0149,  0.5216], grad_fn=&lt;SelectBackward&gt;)
Epoch 2
Accuracy test set: 0.23049267643142476
Sample params: 
tensor([-0.3483, -0.0144, -0.3512,  0.0213, -0.0081,  0.1777,  0.2674, -0.4031,
        -0.4628, -0.3041, -0.1651, -0.1511,  0.0216,  0.0513, -0.4320,  0.0839,
        -0.2602, -0.3629, -0.4541,  0.0398, -0.0768,  0.0432, -0.1150, -0.0160,
        -0.1346, -0.0727,  0.1801, -0.5253], grad_fn=&lt;SelectBackward&gt;)
tensor([ 0.1879, -0.2534,  0.1461, -0.1141,  0.5735,  0.0872, -0.1286,  0.3273,
         0.2084,  0.3037, -0.1535, -0.2934, -0.3870, -0.0252,  0.1492,  0.4752,
        -0.1709,  0.1776,  0.3390, -0.3318,  0.0734,  0.1077, -0.2790, -0.3777,
        -0.0518,  0.3726,  0.0228,  0.5404], grad_fn=&lt;SelectBackward&gt;)
Epoch 3
Accuracy test set: 0.22982689747003995
Sample params: 
tensor([-0.3725, -0.0069, -0.3623,  0.0393, -0.0167,  0.1748,  0.2577, -0.4183,
        -0.4681, -0.3196, -0.1657, -0.1613,  0.0122,  0.0268, -0.4361,  0.0838,
        -0.2962, -0.3566, -0.4344,  0.0366, -0.0822,  0.0486, -0.1150, -0.0295,
        -0.1080, -0.1094,  0.1841, -0.5336], grad_fn=&lt;SelectBackward&gt;)
tensor([ 0.1664, -0.2456,  0.1477, -0.1332,  0.5820,  0.0819, -0.1228,  0.3426,
         0.2066,  0.2985, -0.1464, -0.2824, -0.4199, -0.0323,  0.1530,  0.5057,
        -0.1991,  0.1856,  0.3407, -0.3347,  0.0800,  0.1203, -0.2791, -0.3863,
        -0.0426,  0.3760,  0.0327,  0.5641], grad_fn=&lt;SelectBackward&gt;)
Epoch 4
Accuracy test set: 0.23249001331557922
Sample params: 
tensor([-0.3945,  0.0032, -0.3765,  0.0600, -0.0248,  0.1713,  0.2442, -0.4297,
        -0.4741, -0.3311, -0.1653, -0.1667,  0.0029,  0.0066, -0.4373,  0.0738,
        -0.3320, -0.3530, -0.4136,  0.0390, -0.0731,  0.0552, -0.1117, -0.0517,
        -0.0871, -0.1455,  0.1841, -0.5359], grad_fn=&lt;SelectBackward&gt;)
tensor([ 0.1495, -0.2292,  0.1524, -0.1473,  0.5938,  0.0661, -0.1157,  0.3626,
         0.2013,  0.2927, -0.1350, -0.2661, -0.4558, -0.0411,  0.1562,  0.5381,
        -0.2279,  0.1927,  0.3319, -0.3431,  0.0852,  0.1402, -0.2747, -0.4026,
        -0.0297,  0.3757,  0.0396,  0.5856], grad_fn=&lt;SelectBackward&gt;)

Have I made a mistake in the model definition?

So normally 6 layers in your LSTM are way to much. The input dimension is 28 (are you training MNIST, or are the inputs letters?) so 10 as hidden dimension is acutally way to small. Try the following parameters:
hidden_dim = 128 to 512
layer_dim = 2 to max. 4

I see your output-shape is 1 and you dont use an activation function. Are you trying to predict intergers (like 1 for class &quot;dog&quot;, 2 for class &quot;cat&quot;)? If so you should switch to one-hot encoding, so that your output shape is equal to the classes you want to predict. And then use softmax as activation for your last layer.
