Sparse categorical cross entropy3/17/2023 This tutorial explores two examples using sparse_categorical_crossentropy to keep integer as chars' / multi-class classification labels without transforming to one-hot labels. Need to call reset_states() before prediction to reset LSTMs' initial states.įor more implementation detail of the model, please refer to my GitHub repository.The prediction model loads the trained model weights and predicts five chars at a time, it is, By making it stateful, the LSTMs' last state for each sample in a batch will be used as the initial state for the sample in the following batch, or put it simply, those five characters predicted at a time and following predicted batches are characters in one sequence. Once the model is trained, we can make it "stateful" and predict five characters at a time. Model output shape: (batch_size, seq_len, MAX_TOKENS).Model input shape: (batch_size, seq_len).We can further visualize the structure of the model to understand its input and output shape respectively.Įven though the model has 3-dimensional output, when compiled with the loss function sparse_categorical_crossentropy , we can feed the training targets as sequences of integers. Similarly to the previous example, without the help of sparse_categorical_crossentropy, one need first to convert the output integers to one-hot encoded form to fit the model. RMSPropOptimizer ( learning_rate = 0.01 ), loss = 'sparse_categorical_crossentropy', metrics = ) return model training_model = lstm_model ( seq_len = 100, batch_size = 128, stateful = False, max_tokens = MAX_TOKENS ) Dense ( max_tokens, activation = 'softmax' ))( lstm_2 ) model = tf. LSTM ( EMBEDDING_DIM, stateful = stateful, return_sequences = True )( lstm_1 ) predicted_char = tf. LSTM ( EMBEDDING_DIM, stateful = stateful, return_sequences = True )( embedding ) lstm_2 = tf. Embedding ( input_dim = max_tokens, output_dim = EMBEDDING_DIM )( source ) lstm_1 = tf. Input ( name = 'seed', shape = ( seq_len ,), batch_size = batch_size, dtype = tf. Let's build a simple sequence to sequence model in Keras.ĮMBEDDING_DIM = 512 MAX_TOKENS = 256 def lstm_model ( seq_len = 100, batch_size = None, stateful = True, max_tokens = 256 ): """Language model: predict the next char given the current char.""" source = tf. In other words, given characters of timesteps T0~T99 in the sequence, the model predicts characters of timesteps T1~T100. Given a moving window of sequence length 100, the model learns to predict the sequence one time-step in the future. As a result, we have a list of integers to represent the whole text. We'll train a model on the combined works of William Shakespeare, then use it to compose a play in the similar style.Įvery character in the text blob is first converted to an integer by calling Python's built-in ord() function which returns an integer representing of a character as its ASCII value. For example, ord('a') returns the integer 97. Example two - character level sequence to sequence prediction Note this won't affect the model output shape, it still outputs ten probability scores for each input sample. a one-dimensional array like array(, dtype=uint8) Adadelta (), loss = 'sparse_categorical_crossentropy', metrics = ) # The conventional way # pile( # optimizer=(), # loss=_crossentropy, # metrics=)Īfter that, you can train the model with integer targets, i.e.
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