![]() Printing the model in PyTorch with print(model) shows we have the same architecture. model.summary() in Keras outputs the following.Įmbedding (Embedding) (None, None, 128) 2560000īidirectional (Bidirectional (None, None, 128) 98816īidirectional_1 (Bidirection (None, 128) 98816 And even the details are quite important to achieve similar results, but bear with me, because this is the closest I could get to the Keras implementation.Ī quick sanity check would be to verify the number of parameters of both implementations match. Well… As you can see, even for very basic architectures, transferring code is not an easy job. named_parameters() if 'weight_hh' in name)ī = (param. named_parameters() if 'weight_ih' in name) Let's get straight to the point and use the following method to adapt PyTorch initialization to Keras. For example, you can see that the Embedding layer from Keras initialize its weights with a uniform distribution, whereas it is a normal distribution which is used by PyTorch. And the main difference is initialization! It requires a fair amount of digging to make both frameworks agree on initializations. Now the sad news is this implementation is not exactly the same as the one with Keras. In PyTorch we simply detail a bit more the computation whereas Keras infer them as we stack the layers. If you read part one, there won't be any surprise in this code. This is important to train on batches as it is not possible to parallelize computation of the error gradient on sequences of variable length.ĭef _init_(self, input_dim, embedding_dim, hidden_dim): In addition, we will pad the movie reviews to have sequences of same length. Yet, to make sure the data we feed to Keras and the data we feed to PyTorch is the same, I will simply adapt the downloaded data using Keras to PyTorch, so we are sure to have consistency between both implementations, especially in terms of data cleaning and how the text is tokenized. With torchtext, PyTorch can also download and load the IMDB dataset in the same fashion. It splits the data in half into training and validation sets. ![]() This creates a data/ folder and downloads the dataset inside. ![]() Print(len(x_val), "Validation sequences") Print(len(x_train), "Training sequences")
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