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Fail Fast Prototype Mode

When building neural networks, you want things to either work or fail fast. Long iteration loops are the truest enemy of the machine learning practitioner.

To that end, the following techniques will help you out.

import torch
import torch.nn as nn

# 2dim tensor.. aka a matrix
x = torch.randn(4, 5)

# this is the same as:
batch_size = 4
feature_size = 5
x = torch.randn(batch_size, feature_size)

# now let's try out some NN layer
output_size = 10
fc = nn.Linaer(feature_size, output_size)
print(fc(x).shape)

You can construct whatever prototype variables you want doing this.

Prototyping an embedding

import torch
import torch.nn as nn

batch_size = 4
sequence_size = 5
integer_range = 100
embedding_size = 25
# notice rand vs randn.  rand is uniform (0,1), and randn is normal (-1,1)
random_numbers = (torch.rand(batch_size, sequence_size) * integer_range).long()

embedder = nn.Embedding(num_embeddings=integer_range,
                        embedding_dim=embedding_size)

print(embedder(x).shape)