Understanding Keras LSTMs

Question

I am trying to reconcile my understand of LSTMs and pointed out here in this post by Christopher Olah implemented in Keras. I am following the blog written by Jason Brownlee

Answer 1

When you have return_sequences in your last layer of RNN you cannot use a simple Dense layer instead use TimeDistributed.

Here is an example piece of code this might help others.

words = keras.layers.Input(batch_shape=(None, self.maxSequenceLength), name = "input")

    # Build a matrix of size vocabularySize x EmbeddingDimension 
    # where each row corresponds to a "word embedding" vector.
    # This layer will convert replace each word-id with a word-vector of size Embedding Dimension.
    embeddings = keras.layers.embeddings.Embedding(self.vocabularySize, self.EmbeddingDimension,
        name = "embeddings")(words)
    # Pass the word-vectors to the LSTM layer.
    # We are setting the hidden-state size to 512.
    # The output will be batchSize x maxSequenceLength x hiddenStateSize
    hiddenStates = keras.layers.GRU(512, return_sequences = True, 
                                        input_shape=(self.maxSequenceLength,
                                        self.EmbeddingDimension),
                                        name = "rnn")(embeddings)
    hiddenStates2 = keras.layers.GRU(128, return_sequences = True, 
                                        input_shape=(self.maxSequenceLength, self.EmbeddingDimension),
                                        name = "rnn2")(hiddenStates)

    denseOutput = TimeDistributed(keras.layers.Dense(self.vocabularySize), 
        name = "linear")(hiddenStates2)
    predictions = TimeDistributed(keras.layers.Activation("softmax"), 
        name = "softmax")(denseOutput)  

    # Build the computational graph by specifying the input, and output of the network.
    model = keras.models.Model(input = words, output = predictions)
    # model.compile(loss='kullback_leibler_divergence', \
    model.compile(loss='sparse_categorical_crossentropy', \
        optimizer = keras.optimizers.Adam(lr=0.009, \
            beta_1=0.9,\
            beta_2=0.999, \
            epsilon=None, \
            decay=0.01, \
            amsgrad=False))