TensorFlow教程：快速入门深度学习五步法（附Keras实例）

from __future__ import print_function

from keras.models import Model
from keras.layers import Input, LSTM, Dense
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.
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')
decoder_input_data = np.zeros(
   (len(input_texts), max_decoder_seq_length, num_decoder_tokens),
   dtype= 'float32')
decoder_target_data = np.zeros(
   (len(input_texts), max_decoder_seq_length, num_decoder_tokens),
   dtype= 'float32')

for i, (input_text, target_text) in enumerate(zip(input_texts, target_texts)):
    for t, char in enumerate(input_text):
       encoder_input_data[i, t, input_token_index[char]] = 1.
    for t, char in enumerate(target_text):
        # decoder_target_data is ahead of decoder_input_data by one timestep
       decoder_input_data[i, t, target_token_index[char]] = 1.
        if t > 0:
            # decoder_target_data will be ahead by one timestep
            # and will not include the start character.
           decoder_target_data[i, t - 1, target_token_index[char]] = 1.

# Define an input sequence and process it.
encoder_inputs = Input(shape=( None, num_encoder_tokens))
encoder = LSTM(latent_dim, return_state= True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
# We discard `encoder_outputs` and only keep the states.
encoder_states = [state_h, state_c]

# Set up the decoder, using `encoder_states` as initial state.
decoder_inputs = Input(shape=( None, num_decoder_tokens))
# We set up our decoder to return full output sequences,
# and to return internal states as well. We don't use the
# return states in the training model, but we will use them in inference.
decoder_lstm = LSTM(latent_dim, return_sequences= True, return_state= True)
decoder_outputs, _, _ = decoder_lstm(decoder_inputs,
                                    initial_state=encoder_states)
decoder_dense = Dense(num_decoder_tokens, activation= 'softmax')
decoder_outputs = decoder_dense(decoder_outputs)

# Define the model that will turn
# `encoder_input_data` & `decoder_input_data` into `decoder_target_data`
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)

# Run training
model.compile(optimizer= 'rmsprop', loss= 'categorical_crossentropy')
model.fit([encoder_input_data, decoder_input_data], decoder_target_data,
         batch_size=batch_size,
         epochs=epochs,
         validation_split= 0.2)
# Save model
model.save( 's2s.h5')

encoder_model = Model(encoder_inputs, encoder_states)

decoder_state_input_h = Input(shape=(latent_dim,))
decoder_state_input_c = Input(shape=(latent_dim,))
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
decoder_outputs, state_h, state_c = decoder_lstm(
   decoder_inputs, initial_state=decoder_states_inputs)
decoder_states = [state_h, state_c]
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())


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)