通过创建一个简单的文本生成器来享受LSTM的乐趣。___下载.zip

from __future__ import print_function import matplotlib.pyplot as plt import numpy as np import time import csv from keras.models import Sequential from keras.layers.core import Dense, Activation, Dropout from keras.layers.recurrent import LSTM, SimpleRNN from keras.layers.wrappers import TimeDistributed import argparse from RNN_utils import * # Parsing arguments for Network definition ap = argparse.ArgumentParser() ap.add_argument('-data_dir', default='./data/test.txt') ap.add_argument('-batch_size', type=int, default=50) ap.add_argument('-layer_num', type=int, default=2) ap.add_argument('-seq_length', type=int, default=50) ap.add_argument('-hidden_dim', type=int, default=500) ap.add_argument('-generate_length', type=int, default=500) ap.add_argument('-nb_epoch', type=int, default=20) ap.add_argument('-mode', default='train') ap.add_argument('-weights', default='') args = vars(ap.parse_args()) DATA_DIR = args['data_dir'] BATCH_SIZE = args['batch_size'] HIDDEN_DIM = args['hidden_dim'] SEQ_LENGTH = args['seq_length'] WEIGHTS = args['weights'] GENERATE_LENGTH = args['generate_length'] LAYER_NUM = args['layer_num'] # Creating training data X, y, VOCAB_SIZE, ix_to_char = load_data(DATA_DIR, SEQ_LENGTH) # Creating and compiling the Network model = Sequential() model.add(LSTM(HIDDEN_DIM, input_shape=(None, VOCAB_SIZE), return_sequences=True)) for i in range(LAYER_NUM - 1): model.add(LSTM(HIDDEN_DIM, return_sequences=True)) model.add(TimeDistributed(Dense(VOCAB_SIZE))) model.add(Activation('softmax')) model.compile(loss="categorical_crossentropy", optimizer="rmsprop") # Generate some sample before training to know how bad it is! generate_text(model, args['generate_length'], VOCAB_SIZE, ix_to_char) if not WEIGHTS == '': model.load_weights(WEIGHTS) nb_epoch = int(WEIGHTS[WEIGHTS.rfind('_') + 1:WEIGHTS.find('.')]) else: nb_epoch = 0 # Training if there is no trained weights specified if args['mode'] == 'train' or WEIGHTS == '': while True: print('\n\nEpoch: {}\n'.format(nb_epoch)) model.fit(X, y, batch_size=BATCH_SIZE, verbose=1, nb_epoch=1) nb_epoch += 1 generate_text(model, GENERATE_LENGTH, VOCAB_SIZE, ix_to_char) if nb_epoch % 10 == 0: model.save_weights('checkpoint_layer_{}_hidden_{}_epoch_{}.hdf5'.format(LAYER_NUM, HIDDEN_DIM, nb_epoch)) # Else, loading the trained weights and performing generation only elif WEIGHTS == '': # Loading the trained weights model.load_weights(WEIGHTS) generate_text(model, GENERATE_LENGTH, VOCAB_SIZE, ix_to_char) print('\n\n') else: print('\n\nNothing to do!')