# Restore a character-level sequence to sequence model from to generate predictions.

This script loads the `s2s.h5` model saved by lstm_seq2seq.py and generates sequences from it. It assumes that no changes have been made (for example: `latent_dim` is unchanged, and the input data and model architecture are unchanged).

See lstm_seq2seq.py for more details on the model architecture and how it is trained.

    from __future__ import print_function

    from keras.models import Model, load_model
    from keras.layers import Input
    import numpy as np

    batch_size = 64  # Batch size for training.
    epochs = 100  # Number of epochs to train for.
    latent_dim = 256  # Latent dimensionality of the encoding space.
    num_samples = 10000  # Number of samples to train on.
    # Path to the data txt file on disk.
    data_path = 'fra-eng/fra.txt'

    # Vectorize the data.  We use the same approach as the training script.
    # NOTE: the data must be identical, in order for the character -> integer
    # mappings to be consistent.
    # We omit encoding target_texts since they are not needed.
    input_texts = []
    target_texts = []
    input_characters = set()
    target_characters = set()
    with open(data_path, 'r', encoding='utf-8') as f:
        lines = f.read().split('\n')
    for line in lines[: min(num_samples, len(lines) - 1)]:
        input_text, target_text = line.split('\t')
        # We use "tab" as the "start sequence" character
        # for the targets, and "\n" as "end sequence" character.
        target_text = '\t' + target_text + '\n'
        input_texts.append(input_text)
        target_texts.append(target_text)
        for char in input_text:
            if char not in input_characters:
                input_characters.add(char)
        for char in target_text:
            if char not in target_characters:
                target_characters.add(char)

    input_characters = sorted(list(input_characters))
    target_characters = sorted(list(target_characters))
    num_encoder_tokens = len(input_characters)
    num_decoder_tokens = len(target_characters)
    max_encoder_seq_length = max([len(txt) for txt in input_texts])
    max_decoder_seq_length = max([len(txt) for txt in target_texts])

    print('Number of samples:', len(input_texts))
    print('Number of unique input tokens:', num_encoder_tokens)
    print('Number of unique output tokens:', num_decoder_tokens)
    print('Max sequence length for inputs:', max_encoder_seq_length)
    print('Max sequence length for outputs:', max_decoder_seq_length)

    input_token_index = dict(
        [(char, i) for i, char in enumerate(input_characters)])
    target_token_index = dict(
        [(char, i) for i, char in enumerate(target_characters)])

    encoder_input_data = np.zeros(
        (len(input_texts), max_encoder_seq_length, num_encoder_tokens),
        dtype='float32')

    for i, input_text in enumerate(input_texts):
        for t, char in enumerate(input_text):
            encoder_input_data[i, t, input_token_index[char]] = 1.

    # Restore the model and construct the encoder and decoder.
    model = load_model('s2s.h5')

    encoder_inputs = model.input[0]   # input_1
    encoder_outputs, state_h_enc, state_c_enc = model.layers[2].output   # lstm_1
    encoder_states = [state_h_enc, state_c_enc]
    encoder_model = Model(encoder_inputs, encoder_states)

    decoder_inputs = model.input[1]   # input_2
    decoder_state_input_h = Input(shape=(latent_dim,), name='input_3')
    decoder_state_input_c = Input(shape=(latent_dim,), name='input_4')
    decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
    decoder_lstm = model.layers[3]
    decoder_outputs, state_h_dec, state_c_dec = decoder_lstm(
        decoder_inputs, initial_state=decoder_states_inputs)
    decoder_states = [state_h_dec, state_c_dec]
    decoder_dense = model.layers[4]
    decoder_outputs = decoder_dense(decoder_outputs)
    decoder_model = Model(
        [decoder_inputs] + decoder_states_inputs,
        [decoder_outputs] + decoder_states)

    # Reverse-lookup token index to decode sequences back to
    # something readable.
    reverse_input_char_index = dict(
        (i, char) for char, i in input_token_index.items())
    reverse_target_char_index = dict(
        (i, char) for char, i in target_token_index.items())

    # Decodes an input sequence.  Future work should support beam search.
    def decode_sequence(input_seq):
        # Encode the input as state vectors.
        states_value = encoder_model.predict(input_seq)

        # Generate empty target sequence of length 1.
        target_seq = np.zeros((1, 1, num_decoder_tokens))
        # Populate the first character of target sequence with the start character.
        target_seq[0, 0, target_token_index['\t']] = 1.

        # Sampling loop for a batch of sequences
        # (to simplify, here we assume a batch of size 1).
        stop_condition = False
        decoded_sentence = ''
        while not stop_condition:
            output_tokens, h, c = decoder_model.predict(
                [target_seq] + states_value)

            # Sample a token
            sampled_token_index = np.argmax(output_tokens[0, -1, :])
            sampled_char = reverse_target_char_index[sampled_token_index]
            decoded_sentence += sampled_char

            # Exit condition: either hit max length
            # or find stop character.
            if (sampled_char == '\n' or
               len(decoded_sentence) > max_decoder_seq_length):
                stop_condition = True

            # Update the target sequence (of length 1).
            target_seq = np.zeros((1, 1, num_decoder_tokens))
            target_seq[0, 0, sampled_token_index] = 1.

            # Update states
            states_value = [h, c]

        return decoded_sentence

    for seq_index in range(100):
        # Take one sequence (part of the training set)
        # for trying out decoding.
        input_seq = encoder_input_data[seq_index: seq_index + 1]
        decoded_sentence = decode_sequence(input_seq)
        print('-')
        print('Input sentence:', input_texts[seq_index])
        print('Decoded sentence:', decoded_sentence)

GitHub « Previous Next »