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Aug 12, 2020 · # AUTOENCODER # apply the reshape layer to the output of the encoder query_autoencoder_output = query_decoder.layers[1](query_encoder_output) # rebuild the autoencoder by applying each layer of the decoder to the output of the encoder for decoder_layer in query_decoder.layers[2:]: # this fails and I don't know why query_autoencoder_output = decoder_layer(query_autoencoder_output) # the code never gets here query_autoencoder = Model(inputs=query_encoder_input, outputs=query_autoencoder_output)
Dec 19, 2018 · Step 1 - Saving your model. Save your tokenizer (if applicable). Then individually save the weights of the model you used to train your data (naming your layers helps here). pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL) weights = layer.get_weights()
Then for prediction, they define infernce models as follow: # define the encoder model. encoder_model = Model(encoder_inputs, encoder_states) encoder_model.summary() # Redefine the decoder model with decoder will be getting below inputs from encoder while in prediction. decoder_state_input_h = Input(shape=(50,))
Feb 3, 2021 · In encoder-decoder nets there is exactly one latent space (L) with a nonlinear mapping from the input (X) to that space (E: X->L), and a corresponding mapping from that latent space to the output space (D: L->Y). There's a clear distinction between the encoder and decoder: the encoder changes representation of each sample into some "code" in the latent space, and the decoder is able to construct outputs given only such codes.
Nov 26, 2021 · encoder_model = Model(encoder_inputs, encoder_states) # Redefine the decoder model with decoder will be getting below inputs from encoder while in prediction decoder_state_input_h = Input(shape=(512,)) decoder_state_input_c = Input(shape=(512,)) decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c] inference_decoder_embeddings = decoder_embeddings(decoder_inputs) decoder_outputs2, state_h2, state_c2 = decoder_lstm(inference_decoder_embeddings, initial_state=decoder_states ...
May 7, 2021 · The authors also further mention that the distinction between decoder-only and encoder-only architectures is a bit blurry. For example, machine translation, which is a sequence to sequence task can be solved using GPT models. Similarly, encoder only models like BERT can also be applied to summarization tasks.
Sep 20, 2020 · We import tensorflow_addons. In lines 2-4 we create the input layers for the encoder, for the decoder, and for the raw strings. We could see in the picture where these would go. A first confusion arises here: Why is the shape of encoder_inputs and decoder_inputs a list with the element None in in, while the shape of sequence_lengths is an empty ...
Sep 11, 2018 · I have problems (see second step) to extract the encoder and decoder layers from the trained and saved autoencoder. For step one I have the very simple network as follows: input_img = Input(shape=(784,)) # encoded representation. encoded = Dense(encoding_dim, activation='relu')(input_img) # lossy reconstruction.
Feb 10, 2021 · In the class called 'RNN' we just initialize our Encoder and Decoder and we do the standart things for every simple RNN (anyway, I'm not sure whether I'm implementing all of them in a right order because now I have kind of two RNNs in one program (if it would be only one RNN, I'm sure that I'm doing everything right)).
Oct 14, 2019 · My problem is that if I compile and fit the whole Autoencoder, written as Decoder()Encoder()(x) where x is the input, I get a different prediction when I do. autoencoder.predict (training_set) w.r.t. if I first encode the training set in a set of central features, and then let the decoder decode them. These two approaches should give identical ...
