I am currently trying to include an embedding layer to my sequence-to-sequence autoencoder, built with the keras functional API.
The model code looks like this:
#Encoder inputs
encoder_inputs = Input(shape=(None,))
#Embedding
embedding_layer = Embedding(input_dim=n_tokens, output_dim=2)
encoder_embedded = embedding_layer(encoder_inputs)
#Encoder LSTM
encoder_outputs, state_h, state_c = LSTM(n_hidden, return_state=True)(encoder_embedded)
lstm_states = [state_h, state_c]
#Decoder Inputs
decoder_inputs = Input(shape=(None,))
#Embedding
decoder_embedded = embedding_layer(decoder_inputs)
#Decoder LSTM
decoder_lstm = LSTM(n_hidden, return_sequences=True, return_state=True, )
decoder_outputs, _, _ = decoder_lstm(decoder_embedded, initial_state=lstm_states)
#Dense + Time
decoder_dense = TimeDistributed(Dense(n_tokens, activation='softmax'), input_shape=(None, None, 256))
#decoder_dense = Dense(n_tokens, activation='softmax', )
decoder_outputs = decoder_dense(decoder_outputs)
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
The model is trained like this:
model.fit([X, y], X, epochs=n_epoch, batch_size=n_batch)
with X and y having a shape (n_samples, n_seq_len)
The compiling of the model works flawless, while when trying to train, I will always get:
ValueError: Error when checking target: expected time_distributed_1 to have 3 dimensions, but got array with shape (n_samples, n_seq_len)
Does anybody have an idea?
Keras Version is 2.2.4
Tensorflow backend version 1.12.0
In such an autoencoder, since the last layer is a softmax classifier you need to one-hot encode the labels:
from keras.utils import to_categorical
one_hot_X = to_categorical(X)
model.fit([X, y], one_hot_X, ...)
As a side note, since the Dense layer is applied on the last axis, there is no need to wrap the Dense layer in TimeDistributed layer.