## Issue

I have created and trained one very simple network in pytorch as shown below:

```
self.task_layers[task][task_layer_key]; TaskLayerManager(
(taskLayers): ModuleList(
(0): lc_hidden(
(dropout_layer): Dropout(p=0.0, inplace=False)
(layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(1): cnn(
(cnn_layer): CNN_Text(
(dropout): Dropout(p=0.1, inplace=False)
(fc1): Linear(in_features=300, out_features=2, bias=True)
(convs1): ModuleList(
(0): Conv2d(1, 300, kernel_size=(5, 768), stride=(1, 1), padding=(4, 0))
)
)
)
)
)
Layer descriptions:
taskLayers.0.linear_weights torch.Size([13])
taskLayers.0.layer_norm.weight torch.Size([768])
taskLayers.0.layer_norm.bias torch.Size([768])
taskLayers.1.cnn_layer.fc1.weight torch.Size([2, 300])
taskLayers.1.cnn_layer.fc1.bias torch.Size([2])
taskLayers.1.cnn_layer.convs1.0.weight torch.Size([300, 1, 5, 768])
taskLayers.1.cnn_layer.convs1.0.bias torch.Size([300])
```

It is a binary classification network that take a 3d tensor as input [N,K,768] and gives output [N,2] tensor

I am not able to figure out "Why at every run it is giving me different results"?

Please help me with this – I am new to pytorch.

And let me know if any other information is needed.

## Solution

I suspect this is due to you not having set the model to inference mode with

```
model.eval()
```

If you don’t do this, your dropout layer(s) will remain activated and randomly dropout `p`

proportion of neurons on each call.

Answered By – iacob

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