我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用utils.get_logger()。
def __init__(self, room_url): """ :param room_url: ???url """ # Process.__init__(self) # ???url self.room_url = room_url # ?? self.site_domain = urlparse(self.room_url)[1] # ??? self.room_id = urlparse(self.room_url)[2].replace('/', '') # ???? self.config = utils.load_config() # Logger self.logger = utils.get_logger() if self.site_domain == 'live.bilibili.com': self.room = BiliBiliLive(self.room_id) elif self.site_domain == 'www.panda.tv': self.room = PandaTVLive(self.room_id) elif self.site_domain == 'www.huomao.com': self.room = HuoMaoLive(self.room_id) elif self.site_domain == 'www.zhanqi.tv': self.room = ZhanqiLive(self.room_id)
def start(): if len(sys.argv[1:]) == 0: config = utils.load_config() else: config = utils.load_config(sys.argv[1]) logger = utils.get_logger() logger.info('????') room_count = len(config['ROOM_URLS']) if room_count == 0: logger.info('?????????????') exit(0) pool = ThreadPool(room_count) for room_url in config['ROOM_URLS']: m = Monitor(room_url) pool.apply_async(m.run) pool.close() try: pool.join() except KeyboardInterrupt: logger.warning('????') exit(1)
def evaluate_line(): config = load_config(FLAGS.config_file) logger = get_logger(FLAGS.log_file) # limit GPU memory tf_config = tf.ConfigProto() tf_config.gpu_options.allow_growth = True with open(FLAGS.map_file, "rb") as f: char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f) with tf.Session(config=tf_config) as sess: model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec, config, id_to_char, logger) while True: # try: # line = input("???????:") # result = model.evaluate_line(sess, input_from_line(line, char_to_id), id_to_tag) # print(result) # except Exception as e: # logger.info(e) line = input("???????:") result = model.evaluate_line(sess, input_from_line(line, char_to_id), id_to_tag) print(result)
def evaluate_line(): config = load_config(FLAGS.config_file) logger = get_logger(FLAGS.log_file) tf_config = tf.ConfigProto() tf_config.gpu_options.allow_growth = True with open(FLAGS.map_file, "rb") as f: char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f) with tf.Session(config=tf_config) as sess: model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec, config, id_to_char, logger) while True: # try: # line = input("???????:") # result = model.evaluate_line(sess, input_from_line(line, char_to_id), id_to_tag) # print(result) # except Exception as e: # logger.info(e) line = input("???????:") result = model.evaluate_line(sess, input_from_line(line, char_to_id), id_to_tag) print(result)
def build_model(opts, vocab_size=0, maxnum=50, maxlen=50, embedd_dim=50, embedding_weights=None, verbose=False, init_mean_value=None): N = maxnum L = maxlen logger = get_logger("Build model") logger.info("Model parameters: max_sentnum = %d, max_sentlen = %d, embedding dim = %s, lstm_units = %s, drop rate = %s, l2 = %s" % (N, L, embedd_dim, opts.lstm_units, opts.dropout, opts.l2_value)) word_input = Input(shape=(N*L,), dtype='int32', name='word_input') x = Embedding(output_dim=embedd_dim, input_dim=vocab_size, input_length=N*L, weights=embedding_weights, name='x')(word_input) drop_x = Dropout(opts.dropout, name='drop_x')(x) resh_W = Reshape((N, L, embedd_dim), name='resh_W')(drop_x) z = TimeDistributed(LSTM(opts.lstm_units, return_sequences=True), name='z')(resh_W) avg_z = TimeDistributed(GlobalAveragePooling1D(), name='avg_z')(z) hz = LSTM(opts.lstm_units, return_sequences=True, name='hz')(avg_z) # TODO, random drop sentences drop_hz = Dropout(opts.dropout, name='drop_hz')(hz) avg_hz = GlobalAveragePooling1D(name='avg_hz')(drop_hz) y = Dense(output_dim=1, activation='sigmoid', name='output')(avg_hz) model = Model(input=word_input, output=y) if opts.init_bias and init_mean_value: logger.info("Initialise output layer bias with log(y_mean/1-y_mean)") bias_value = (np.log(init_mean_value) - np.log(1 - init_mean_value)).astype(K.floatx()) model.layers[-1].b.set_value(bias_value) if verbose: model.summary() start_time = time.time() model.compile(loss='mse', optimizer='rmsprop') total_time = time.time() - start_time logger.info("Model compiled in %.4f s" % total_time) return model
def build_bidirectional_model(opts, vocab_size=0, maxnum=50, maxlen=50, embedd_dim=50, embedding_weights=None, verbose=False, init_mean_value=None): N = maxnum L = maxlen logger = get_logger("Build bidirectional model") logger.info("Model parameters: max_sentnum = %d, max_sentlen = %d, embedding dim = %s, lstm_units = %s, drop rate = %s, l2 = %s" % (N, L, embedd_dim, opts.lstm_units, opts.dropout, opts.l2_value)) word_input = Input(shape=(N*L,), dtype='int32', name='word_input') x = Embedding(output_dim=embedd_dim, input_dim=vocab_size, input_length=N*L, weights=embedding_weights, name='x')(word_input) drop_x = Dropout(opts.dropout, name='drop_x')(x) resh_W = Reshape((N, L, embedd_dim), name='resh_W')(drop_x) z_fwd = TimeDistributed(LSTM(opts.lstm_units, return_sequences=True), name='z_fwd')(resh_W) z_bwd = TimeDistributed(LSTM(opts.lstm_units, return_sequences=True, go_backwards=True), name='z_bwd')(resh_W) z_merged = merge([z_fwd, z_bwd], mode='concat', name='z_merged') avg_z = TimeDistributed(GlobalAveragePooling1D(), name='avg_z')(z_merged) hz_fwd = LSTM(opts.lstm_units, return_sequences=True, name='hz_fwd')(avg_z) hz_bwd = LSTM(opts.lstm_units, return_sequences=True, go_backwards=True, name='hz_bwd')(avg_z) hz_merged = merge([hz_fwd, hz_bwd], mode='concat', name='hz_merged') # avg_h = MeanOverTime(mask_zero=True, name='avg_h')(hz) avg_hz = GlobalAveragePooling1D(name='avg_hz')(hz_merged) y = Dense(output_dim=1, activation='sigmoid', name='output')(avg_hz) model = Model(input=word_input, output=y) if opts.init_bias and init_mean_value: logger.info("Initialise output layer bias with log(y_mean/1-y_mean)") bias_value = (np.log(init_mean_value) - np.log(1 - init_mean_value)).astype(K.floatx()) model.layers[-1].b.set_value(bias_value) if verbose: model.summary() start_time = time.time() model.compile(loss='mse', optimizer='rmsprop') total_time = time.time() - start_time logger.info("Model compiled in %.4f s" % total_time) return model
def build_attention_model(opts, vocab_size=0, maxnum=50, maxlen=50, embedd_dim=50, embedding_weights=None, verbose=False, init_mean_value=None): N = maxnum L = maxlen logger = get_logger('Build attention pooling model') logger.info("Model parameters: max_sentnum = %d, max_sentlen = %d, embedding dim = %s, lstm_units = %s, drop rate = %s, l2 = %s" % (N, L, embedd_dim, opts.lstm_units, opts.dropout, opts.l2_value)) word_input = Input(shape=(N*L,), dtype='int32', name='word_input') x = Embedding(output_dim=embedd_dim, input_dim=vocab_size, input_length=N*L, weights=embedding_weights, name='x')(word_input) drop_x = Dropout(opts.dropout, name='drop_x')(x) resh_W = Reshape((N, L, embedd_dim), name='resh_W')(drop_x) z = TimeDistributed(LSTM(opts.lstm_units, return_sequences=True), name='z')(resh_W) avg_z = TimeDistributed(GlobalAveragePooling1D(), name='avg_z')(z) hz = LSTM(opts.lstm_units, return_sequences=True, name='hz')(avg_z) # avg_h = MeanOverTime(mask_zero=True, name='avg_h')(hz) # avg_hz = GlobalAveragePooling1D(name='avg_hz')(hz) attent_hz = Attention(name='attent_hz')(hz) y = Dense(output_dim=1, activation='sigmoid', name='output')(attent_hz) model = Model(input=word_input, output=y) if opts.init_bias and init_mean_value: logger.info("Initialise output layer bias with log(y_mean/1-y_mean)") bias_value = (np.log(init_mean_value) - np.log(1 - init_mean_value)).astype(K.floatx()) model.layers[-1].b.set_value(bias_value) if verbose: model.summary() start_time = time.time() model.compile(loss='mse', optimizer='rmsprop') total_time = time.time() - start_time logger.info("Model compiled in %.4f s" % total_time) return model
def build_attention2_model(opts, vocab_size=0, maxnum=50, maxlen=50, embedd_dim=50, embedding_weights=None, verbose=False, init_mean_value=None): N = maxnum L = maxlen logger = get_logger('Build attention pooling model') logger.info("Model parameters: max_sentnum = %d, max_sentlen = %d, embedding dim = %s, lstm_units = %s, drop rate = %s, l2 = %s" % (N, L, embedd_dim, opts.lstm_units, opts.dropout, opts.l2_value)) word_input = Input(shape=(N*L,), dtype='int32', name='word_input') x = Embedding(output_dim=embedd_dim, input_dim=vocab_size, input_length=N*L, weights=embedding_weights, name='x')(word_input) drop_x = Dropout(opts.dropout, name='drop_x')(x) resh_W = Reshape((N, L, embedd_dim), name='resh_W')(drop_x) z = TimeDistributed(LSTM(opts.lstm_units, return_sequences=True), name='z')(resh_W) att_z = TimeDistributed(Attention(name='att_z'))(z) hz = LSTM(opts.lstm_units, return_sequences=True, name='hz')(att_z) # avg_h = MeanOverTime(mask_zero=True, name='avg_h')(hz) # avg_hz = GlobalAveragePooling1D(name='avg_hz')(hz) attent_hz = Attention(name='attent_hz')(hz) y = Dense(output_dim=1, activation='sigmoid', name='output')(attent_hz) model = Model(input=word_input, output=y) if opts.init_bias and init_mean_value: logger.info("Initialise output layer bias with log(y_mean/1-y_mean)") bias_value = (np.log(init_mean_value) - np.log(1 - init_mean_value)).astype(K.floatx()) model.layers[-1].b.set_value(bias_value) if verbose: model.summary() start_time = time.time() model.compile(loss='mse', optimizer='rmsprop') total_time = time.time() - start_time logger.info("Model compiled in %.4f s" % total_time) return model
def __init__(self, name, keep_growing=True): self.__name = name self.instance2index = {} self.instances = [] self.keep_growing = keep_growing # Index 0 is occupied by default, all else following. I believe this is to hold unk variables self.default_index = 0 self.next_index = 1 self.logger = utils.get_logger('Alphabet')
