What Is PyTorch Forward Pass?

This Pytorch code example introduces you to the concept of PyTorch forward pass using a simple PyTorch example.

Objective: What Is PyTorch Forward Pass?

This PyTorch code example will introduce you to PyTorch Forward Pass with the help of a PyTorch tensor example.

What Is Forward Pass PyTorch?

The PyTorch forward pass is the process of computing the output of a neural network given an input. It is the first step in training a neural network and is also used to make predictions on new data. The forward pass is implemented by the forward() method of a PyTorch model. This method takes the input data and returns the output data as output. The forward pass can be as simple as a single linear layer or as complex as a multi-layer neural network with multiple hidden layers.

PyTorch Vs. Tensorflow - Which One Should You Choose For Your Next Deep Learning Project?

Steps Showing How To Perform PyTorch Forward Pass

The following steps will show you how to perform a PyTorch forward pass with the help of a simple PyTorch tensor example.

Step 1 - Import Library For PyTorch Forward Pass

First, you must import the required libraries.

import torch, torchvision

Step 2 - Build The Model And Define Parameters

As shown in the code below, from the torch vision module, you will load a pre-trained ResNet 18 model after that, you will create data, which is random tensor data for representing a single image with three channels and a height and width of 64. Then, you will initialize its corresponding labels to some random values.

torch_model = torchvision.models.resnet18(pretrained=True)

torch_data = torch.rand(1, 3, 64, 64)

data_labels = torch.rand(1, 1000)

Step 3 - Generate PyTorch Forward Pass Predictions

The final step involves making predictions on the data, which is a forward pass.

predict = torch_model(torch_data)

predict

The output of the above code is-

tensor([[-5.4893e-01, -6.0326e-01, -5.9082e-01, -1.6569e+00, -8.5535e-01,

         -4.1704e-01, -6.0561e-01,  5.1417e-01,  1.7487e-01, -1.0317e+00,

         -8.1430e-01, -8.6720e-01, -2.0595e-01, -8.1306e-01, -6.0807e-01,

         -6.7518e-01, -8.3621e-01, -1.9989e-01, -6.9752e-01, -2.8247e-01,

         -1.4040e+00, -7.4798e-01, -1.5800e+00,  2.4213e-02, -8.2695e-01,

         -1.2802e+00, -1.0552e+00, -1.2388e+00, -9.1404e-01, -6.3612e-01,

         -1.0002e+00, -9.1282e-01, -4.3767e-01, -4.0623e-01, -2.8015e-01,

         -3.2605e-01,  5.4419e-01, -8.3449e-01, -3.4951e-01, -8.9941e-02,

         -8.4479e-01, -7.0490e-01, -1.0243e+00, -1.4262e-01, -6.7143e-01,

         -5.1290e-01, -8.2436e-01, -7.2680e-01, -1.1551e+00, -8.4922e-01,

         -3.9632e-01,  3.8016e-01, -3.9695e-01, -7.6557e-01, -1.7351e-01,

         -1.2692e+00, -3.8533e-01, -1.6109e+00, -6.1131e-01, -7.4420e-01,

          7.9733e-01,  2.2349e-01,  2.8536e-02,  1.5157e-01, -6.0809e-01,

         -3.5001e-01, -3.9387e-01, -4.3531e-01, -7.0313e-01, -1.1597e+00,

         -1.5863e+00,  3.4348e-02, -1.4724e+00, -1.3654e-01, -1.1384e+00,

         -1.3416e+00, -1.4510e-01, -5.4486e-01,  2.5158e-01,  4.2203e-02,

         -9.1153e-01, -1.5224e+00, -5.8208e-02, -7.0314e-01, -7.2005e-01,

         -1.7612e-01,  1.7193e-01,  4.2740e-01, -1.0537e-01, -6.8799e-01,

         -1.2660e+00, -1.2349e+00, -1.6433e+00, -4.7735e-01,  2.8602e-01,

         -1.8771e+00, -6.5530e-01, -4.4569e-01, -1.4636e+00, -3.1984e-01,

         -1.2790e+00, -1.2212e+00, -1.3640e+00, -3.2355e-01, -2.1420e-01,

         -6.3889e-01, -4.0521e-01, -1.5429e+00, -1.3129e+00, -1.3621e+00,

         -1.3520e+00, -8.1833e-01,  1.1169e+00,  2.4998e-01,  1.7689e-01,

         -1.3288e+00, -7.6131e-01, -2.3009e-01,  5.4524e-01, -4.1945e-01,

         -9.0595e-01, -2.3513e-01,  4.2868e-01,  2.8783e-02,  8.2983e-01,

         -6.9502e-02,  3.4771e-01, -1.7464e+00, -1.2903e+00, -1.2731e+00,

         -1.6200e+00, -1.6067e+00, -1.2997e+00, -1.5542e+00, -9.0064e-01,

         -1.7992e+00, -1.3601e+00, -1.1782e+00, -1.5451e+00, -1.5033e+00,

         -1.2866e+00, -1.7537e+00, -2.0205e+00, -1.7837e+00, -7.9393e-01,

         -6.3382e-01, -1.3840e+00, -2.1626e+00, -1.3783e+00, -1.6434e+00,

          6.0317e-01,  1.2140e+00, -9.2346e-01, -5.1239e-01, -2.1404e-01,

          3.2854e-01, -6.8853e-02, -2.5733e-01,  2.3334e-02,  2.6542e-02,

          5.2867e-01,  1.0187e+00,  4.9803e-01,  7.2084e-01,  4.0169e-01,

         -1.4474e-01, -1.9940e-01, -4.3206e-01,  7.8037e-01, -5.3784e-01,

         -2.8162e-01,  6.0236e-01, -1.8639e-02, -1.7878e-01,  3.6213e-01,

         -8.1641e-01, -1.0044e-01, -2.3166e-01,  4.5369e-01,  5.7538e-01,

          5.1817e-01, -6.7936e-02,  2.5825e-01,  3.3790e-01,  3.7571e-01,

          4.9383e-01,  2.9511e-01,  1.4003e-01,  3.1396e-01,  5.3728e-01,

         -4.0961e-01,  5.2575e-01,  1.9075e-01,  4.6439e-01, -8.7552e-01,

          6.7330e-01,  2.3853e-01,  3.2199e-01,  4.5139e-01,  5.5539e-01,

         -5.7650e-02,  2.1204e-01,  4.7050e-01,  4.7046e-01,  1.6229e-01,

          2.3843e-01,  1.9818e-01,  3.4966e-01,  1.2687e+00,  4.3866e-01,

         -2.4659e-01,  8.4488e-02,  4.6087e-01, -9.9765e-02,  9.5816e-02,

          2.4042e-01, -1.6282e-02,  4.6005e-01, -1.4120e-01,  8.2343e-01,

          2.4602e-01,  1.7783e-01, -8.6233e-02,  7.7626e-01,  3.0159e-01,

          3.5114e-01,  2.8590e-02,  8.2412e-01, -1.8842e-01, -5.8583e-02,

          1.0962e-02,  1.9287e-01,  2.9142e-01,  5.1235e-02,  8.1307e-01,

          6.5394e-01,  2.4035e-01,  3.0465e-01,  6.3528e-01,  1.2961e-01,

          5.1572e-01, -5.4466e-03,  2.8223e-01,  3.6045e-01, -1.9469e-01,

          4.2426e-01,  3.0720e-01,  8.1802e-02,  6.0954e-01,  2.7055e-01,

          3.0279e-01,  6.8366e-01, -7.5701e-01,  4.5096e-01,  1.0884e+00,

         -8.0719e-01,  4.4794e-01,  2.1084e-01, -3.5386e-01, -1.0159e-01,

         -3.2589e-01, -7.0790e-01, -4.9758e-01,  3.5975e-01,  6.3938e-01,

          6.8244e-01,  4.1838e-01,  6.1006e-01, -4.1660e-01, -4.9142e-01,

         -1.0036e+00, -1.2500e+00, -8.4451e-01,  5.9699e-01, -1.1230e+00,

         -1.2530e+00, -1.5295e+00, -1.2254e+00, -1.6554e+00, -7.4507e-01,

         -3.7370e-01,  9.1579e-01,  8.9618e-01, -2.8663e-01,  2.2462e-02,

          8.8263e-01, -2.1811e-02, -4.1726e-01, -5.6378e-01, -1.4142e+00,

         -5.4122e-01, -1.1948e+00, -4.0054e-02, -8.7278e-01, -1.0961e+00,

         -8.0407e-01, -8.4833e-01, -1.4067e+00, -5.7839e-01, -3.4441e-01,

         -1.7407e+00, -7.3073e-01, -3.2385e-01, -2.8595e-01, -1.0154e+00,

         -6.5335e-01,  3.9680e-01, -7.9147e-01, -1.2213e+00, -5.0339e-01,

          4.3798e-01, -6.8147e-01, -5.3384e-01,  8.5350e-02,  9.4674e-01,

         -1.0312e+00, -1.0698e+00, -1.4612e+00, -1.3063e+00, -7.2571e-01,

         -1.3863e+00, -1.4992e+00, -1.4210e+00, -1.5849e+00, -1.5408e+00,

         -1.6322e+00, -1.2104e+00, -1.7003e-01, -2.1719e-01, -4.7170e-01,

         -3.2856e-01, -6.2113e-01, -3.1928e-01,  3.0936e-01, -9.0798e-01,

         -6.2744e-01, -1.5262e+00,  2.0861e-01,  6.3372e-01, -1.2384e+00,

         -5.4701e-01,  3.2668e-01, -6.6810e-01, -1.6415e+00, -7.7181e-01,

          5.4336e-01, -8.3646e-01, -1.5369e+00, -2.4535e-01, -1.3977e+00,

         -1.1574e+00, -2.4248e+00, -1.4346e+00, -9.3605e-01, -4.9174e-01,

          2.5710e-01,  9.6237e-01,  5.9434e-02,  3.8533e-01,  3.1132e-01,

          1.5045e-02,  2.0748e-01, -3.9605e-01,  1.2726e-01, -4.7697e-01,

         -7.1818e-01, -9.5797e-01, -3.3738e-01, -1.0642e+00, -4.9280e-01,

         -7.4266e-01, -3.3368e-01, -6.8823e-01, -1.9681e-01, -5.7090e-01,

         -1.2021e+00, -1.2938e+00,  2.5256e-01, -8.2980e-01, -5.9566e-01,

          2.1798e-01, -5.0915e-01, -1.9408e-01, -7.5128e-01, -1.0407e+00,

         -4.5687e-01, -1.0063e+00, -1.3641e+00, -1.2147e+00, -1.3296e-01,

          6.6002e-01,  4.8747e-02, -1.5793e+00, -2.0910e+00, -3.3404e-01,

          6.4746e-01, -1.3011e+00, -4.8904e-01,  2.8291e-01,  2.7638e-01,

         -6.0599e-01,  7.0913e-01,  2.3410e-01, -2.3227e+00, -2.0628e+00,

         -5.1947e-01, -3.7589e-02,  4.1415e-03, -1.3804e-01,  1.1961e+00,

          7.0591e-02,  3.5246e-01,  1.8740e+00,  1.0446e+00,  5.0173e-01,

          1.2331e+00, -2.4608e-01,  3.7157e-01,  8.9861e-02,  8.2854e-01,

          8.2692e-01,  1.3610e+00,  1.3023e-01,  3.3372e-01,  2.3647e-01,

         -8.1345e-01,  4.3355e-01,  1.4532e+00,  1.4987e+00,  6.9580e-01,

         -4.2474e-01, -9.7892e-02,  3.6486e-01,  1.0295e+00,  1.0008e+00,

          1.0101e+00,  2.5390e-02, -8.2267e-02,  3.5014e-01,  7.2203e-01,

          8.7037e-01,  4.3105e-01,  4.5594e-01, -5.4294e-01, -8.4187e-03,

          3.1997e-01,  3.7356e-01,  1.4225e+00,  1.2607e+00, -4.3051e-01,

         -1.1956e-01,  6.8275e-01,  1.0023e+00, -1.7211e-01, -2.7399e-01,

          6.3634e-01,  1.7784e+00,  1.2979e+00,  2.0670e-03,  6.2137e-01,

         -4.9876e-01,  7.0538e-01,  1.4194e+00,  2.3155e+00,  8.2529e-01,

         -6.6562e-03, -1.4618e+00, -1.9672e-01,  2.1126e-03,  1.7571e+00,

          1.1571e+00,  6.8560e-01,  2.7352e-01,  5.5746e-01,  1.5580e-01,

         -2.5426e-01,  1.0675e-02,  2.7841e-01,  6.6019e-01,  3.8814e-01,

          6.8866e-02,  2.9788e-01,  3.2089e-01, -8.2915e-01, -1.1054e+00,

         -4.7236e-02, -1.4728e-01,  1.2712e+00,  1.6846e+00,  7.5850e-01,

          8.1812e-01,  7.5421e-01,  8.2766e-01, -1.0792e+00,  1.2012e+00,

         -9.3967e-01, -5.9330e-02, -1.8176e-01, -8.8823e-02,  1.4995e+00,

         -1.5081e+00,  6.4003e-01,  1.5899e+00,  3.8492e-01,  7.3817e-01,

          1.4646e+00,  1.3144e+00,  7.7931e-01,  2.3837e-01,  5.5162e-02,

         -1.2678e+00, -8.3715e-01,  8.6130e-01,  1.9170e-01,  9.3733e-01,

          1.7609e+00,  3.1674e-01,  2.2364e-01,  1.2678e+00,  8.0203e-01,

         -1.0542e+00,  2.8802e-01,  1.2406e+00,  1.6046e+00,  4.5291e-01,

         -7.5963e-01, -2.9392e-01, -5.5456e-01,  1.2600e-01,  3.2510e-01,

          1.2424e+00,  2.7038e-01,  2.3308e-01, -8.8874e-01,  2.6379e-01,

         -6.8432e-01, -4.6373e-01, -7.5495e-01,  5.2803e-01,  1.3214e+00,

         -1.2065e+00,  1.4618e+00,  8.7268e-01,  6.8804e-01,  6.4578e-01,

          9.5584e-01,  5.2821e-01, -1.7937e+00, -1.2261e+00, -2.8558e-01,

         -2.2431e-01, -1.2040e-01,  7.6256e-01,  5.8941e-02, -1.5987e+00,

         -7.4905e-01,  3.2718e-01,  4.8996e-01,  1.2558e+00,  8.6192e-01,

          8.6113e-03, -1.1062e-01,  8.9745e-01, -2.5339e-01, -8.9582e-01,

         -6.7721e-01, -7.8638e-02,  1.1366e+00,  4.8849e-01, -4.3938e-01,

          9.7614e-01,  5.7081e-02,  8.5853e-01, -1.1486e+00,  5.3200e-01,

         -3.6621e-02, -9.6299e-01,  1.2055e+00,  4.6403e-01,  2.6044e-01,

          2.8532e-01, -2.0474e-01,  6.0208e-01,  9.6717e-01,  8.4615e-01,

          8.2479e-01, -3.6018e-01,  1.8168e+00,  9.1113e-01,  1.2849e+00,

         -5.8416e-01,  6.5076e-01, -9.9379e-01,  7.1605e-01,  3.3052e-01,

         -6.3120e-01,  1.2910e+00, -2.9590e-01, -7.3656e-01,  9.4005e-01,

          2.4377e+00,  3.8498e-01,  5.3058e-02, -6.0870e-01,  2.5055e-01,

         -1.7097e-01,  1.2273e+00, -2.4777e-01,  7.4426e-01, -2.0205e-01,

          1.1026e+00,  6.1937e-01, -4.8880e-01,  7.8294e-01,  7.6542e-02,

          4.4684e-01,  1.5125e+00,  5.6724e-01,  2.1657e+00,  1.1974e+00,

          8.2063e-01,  5.8036e-01, -3.8443e-02,  4.0587e-01, -3.1664e-02,

         -9.0428e-01,  7.3601e-01, -1.9472e-01, -1.2280e+00,  1.2535e-01,

          2.3088e-01,  7.2603e-01,  7.2610e-01,  1.0632e+00, -3.8823e-02,

          4.2447e-01,  1.3644e+00,  6.8777e-01,  7.6282e-01,  2.4063e-01,

         -1.6408e+00,  1.1271e+00, -6.5269e-02,  1.3137e+00,  5.9574e-01,

         -6.9501e-01,  9.3232e-01,  2.8346e-01, -7.7563e-01, -1.1053e+00,

          1.3760e+00,  1.4931e-01,  7.3529e-01,  7.4021e-01,  1.8326e-01,

          7.0709e-01,  1.4456e-01,  1.1122e-01,  2.8127e-01,  4.5740e-01,

          2.6462e-01, -1.0356e+00,  1.9397e-01, -5.5271e-01,  9.4586e-01,

         -8.0238e-02,  1.5312e+00,  5.8519e-01, -5.4462e-01, -4.0325e-01,

          5.7546e-02,  5.0450e-02, -1.1325e-01,  3.5751e-01,  1.5598e+00,

         -5.4421e-01,  1.4543e+00,  1.2678e+00,  8.0839e-01,  4.7271e-01,

          4.8508e-01,  3.0178e-01, -2.0575e-01,  6.0853e-01,  1.2660e+00,

         -1.5019e+00,  7.2002e-03, -1.2636e+00, -3.9473e-01, -5.0290e-01,

         -6.8470e-01,  7.5209e-01,  7.8588e-01,  6.2179e-01, -1.1047e+00,

          9.6801e-01,  1.2656e+00, -3.7503e-02, -3.0146e-01,  8.4946e-01,

          1.8967e+00, -3.5063e-01,  1.0567e-01,  3.3678e-01,  4.1981e-01,

         -1.3238e-01, -1.4004e-01,  2.0441e-01,  1.0884e+00,  7.6667e-03,

          6.5183e-01,  9.3076e-01,  1.1148e-01, -2.8322e-01,  1.4491e-01,

         -8.4217e-02,  4.8407e-01, -8.3668e-01, -3.1540e-01,  5.8997e-01,

          7.2239e-02,  2.6469e-01,  1.1026e+00,  4.3939e-03, -5.0173e-01,

          1.3235e+00, -4.8997e-01,  7.2086e-02,  1.5205e+00, -3.0592e-01,

         -1.0444e-01,  2.1883e+00, -6.7297e-01,  2.1282e+00, -1.1288e+00,

          5.0798e-01,  2.7532e-02,  5.9175e-01,  1.0267e+00,  1.1562e-01,

          1.1937e+00,  1.6744e-02,  3.5649e-01,  5.0543e-01,  6.8748e-01,

          1.4175e-01,  2.2079e-01,  8.4759e-01,  7.0747e-01,  1.5009e+00,

          3.9015e-01, -8.6542e-02,  3.8673e-01,  4.1267e-01,  9.6939e-01,

         -5.7022e-01,  1.0427e+00,  1.7874e-01,  1.6595e+00, -2.4363e-01,

         -9.9748e-02,  4.9677e-01,  9.2415e-01,  7.1617e-01,  1.3697e+00,

          7.6435e-01,  3.2089e-01,  1.8775e-01, -3.1474e-01,  1.4299e+00,

          2.9227e-01,  3.6850e-01,  1.4583e+00,  6.4091e-01,  7.1821e-01,

          8.8317e-01,  2.9403e-01,  5.7572e-01,  1.5512e+00, -6.0079e-01,

         -8.9416e-01, -6.6842e-01,  1.0538e+00,  1.0254e+00,  1.3416e+00,

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          1.0145e+00,  4.5393e-01,  2.8292e-01,  1.4075e+00,  1.0391e-01,

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          6.8422e-01, -3.5706e-01, -6.6274e-02, -3.2889e-01,  1.0197e+00,

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          4.0115e-01, -6.7843e-01,  7.4255e-02, -1.0358e+00,  5.2950e-01,

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         -7.6885e-01,  1.1862e+00,  4.8462e-01,  1.8794e+00,  2.0923e+00,

         -8.9625e-01, -4.3343e-01,  1.7446e+00,  1.1010e+00,  1.2332e+00,

          4.1681e-01, -1.9071e-01,  9.9241e-01, -1.2409e+00,  8.8529e-01,

          1.2270e+00,  1.4844e+00,  6.0924e-01, -4.8827e-01, -1.8296e+00,

         -1.3563e-01, -7.5254e-02, -1.3429e-01,  1.3912e-01,  4.4616e-01,

          3.4253e-02,  9.8513e-01, -7.3364e-01,  6.0251e-01, -3.1832e-01,

         -9.3215e-01, -1.0010e+00, -3.8237e-01, -1.0850e-01,  1.5963e+00,

          1.4690e-01,  1.3709e-01,  3.5415e-01, -1.7841e+00, -1.1100e-01,

         -6.3617e-01,  4.1254e-01,  3.2008e-01, -1.9500e-01,  7.7376e-02,

         -2.6895e-02, -5.8362e-01, -1.3833e-01,  2.4711e-01, -6.3571e-01,

         -8.4207e-01, -1.2893e+00,  4.7617e-01,  6.2128e-01, -3.7386e-01,

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         -3.3988e-01, -3.8388e-01, -5.8168e-01,  1.2285e-01, -8.0350e-01,

          4.2262e-01,  3.8409e-01, -1.5496e-01, -8.2679e-01, -1.1909e+00,

          4.0232e-02,  2.6163e-01, -8.0494e-01,  4.3072e-01,  3.3797e-02,

          1.1832e-01,  9.0386e-01, -3.6286e-01, -5.0607e-01, -1.7431e+00,

          1.1139e+00, -1.4494e+00,  7.5701e-01,  3.6081e-01, -5.9323e-01,

         -6.3955e-01,  1.9242e-01,  5.8523e-01, -8.2932e-01, -1.0614e+00,

         -1.3004e+00, -2.2665e+00,  1.6767e+00, -2.3216e-01, -1.0597e+00,

         -1.7095e-01, -1.4423e+00, -8.6673e-01, -1.9782e+00, -7.1479e-01,

         -5.7188e-01,  2.4036e-01, -1.7320e-01,  1.4438e+00,  7.1238e-01]],

       grad_fn=)

Ace The PyTorch Forward Pass With ProjectPro

This PyTorch code example explores the concept of the PyTorch forward pass, a fundamental step in neural network computation. We have walked you through the essential steps for performing a forward pass, which involves passing input data through a network to make predictions. You can further build solid expertise in PyTorch by working on enterprise-grade projects by ProjectPro. These end-to-end solved projects will help you understand the implementation of PyTorch and other tools in real-world scenarios to build effective data science and big data solutions.

FAQs on PyTorch Forward Pass

1. What Makes PyTorch Forward Pass Always Output Zero?

There are a few reasons why your PyTorch forward pass might always output zero-

  • Your model is not initialized correctly. 

  • Your model is not learning.

  • Your model is overfitting.

  • Your model has a bug.

2. What do you mean by PyTorch Forward Pass of SqueezeNet?

The forward pass of SqueezeNet in PyTorch is the process of computing the output of the SqueezeNet model given an input image. The SqueezeNet model is a convolutional neural network designed to be efficient and lightweight. It is often used for image classification and object detection tasks.

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