output_dropout_rate=config['output_dropout_rate'],
nb_epoch=int(config['cnn_nb_epoch']),
earlyStoping_patience=config['earlyStoping_patience'],
feature_encoder=feature_encoder.vocabulary_size+1,
optimizers='sgd',
lr= 1e-1,
batch_size = 128,
)
w2v_embedding_cnn.print_model_descibe()
if config['refresh_all_model'] or not os.path.exists(model_file_path):
# 训练模型
w2v_embedding_cnn.fit((train_w2v_features, train_y),
(test_w2v_features, test_y))
# 保存模型
w2v_embedding_cnn.save_model(model_file_path)
else:
# 从保存的pickle中加载模型
w2v_embedding_cnn.model_from_pickle(model_file_path)
# -------------- code start : 结束 -------------
if verbose > 2:
logging.debug('-' * 20)
print '-' * 20
# -------------- region end : 3. 初始化CNN模型并训练 ---------------
print index_to_label[w2v_embedding_cnn.predict(feature_encoder.transform_sentence('你好吗'))]
num_labels=len(label_to_index),
conv_filter_type=config['conv_filter_type'],
k=config['kmax_k'],
embedding_dropout_rate=config['embedding_dropout_rate'],
output_dropout_rate=config['output_dropout_rate'],
nb_epoch=int(config['cnn_nb_epoch']),
earlyStoping_patience=config['earlyStoping_patience'],
)
rand_embedding_cnn.print_model_descibe()
if config['refresh_all_model'] or not os.path.exists(model_file_path):
# 训练模型
rand_embedding_cnn.fit((train_X_feature, train_y),
(test_X_feature, test_y))
# 保存模型
rand_embedding_cnn.save_model(model_file_path)
else:
# 从保存的pickle中加载模型
rand_embedding_cnn.model_from_pickle(model_file_path)
# -------------- code start : 结束 -------------
if verbose > 2:
logging.debug('-' * 20)
print '-' * 20
# -------------- region end : 3. 初始化CNN模型并训练 ---------------
# -------------- region start : 4. 预测 -------------
if verbose > 1:
logging.debug('-' * 20)
print '-' * 20
logging.debug('4. 预测')
num_labels=len(label_to_index),
conv_filter_type=config['conv_filter_type'],
k=config['kmax_k'],
embedding_dropout_rate=config['embedding_dropout_rate'],
output_dropout_rate=config['output_dropout_rate'],
nb_epoch=int(config['cnn_nb_epoch']),
earlyStoping_patience=config['earlyStoping_patience'],
)
rand_embedding_cnn.print_model_descibe()
if config['refresh_all_model'] or not os.path.exists(model_file_path):
# 训练模型
rand_embedding_cnn.fit((feature_encoder.train_padding_index, train_y),
(map(feature_encoder.transform_sentence, test_X), test_y))
# 保存模型
rand_embedding_cnn.save_model(model_file_path)
else:
# 从保存的pickle中加载模型
rand_embedding_cnn.model_from_pickle(model_file_path)
# -------------- code start : 结束 -------------
if verbose > 2:
logging.debug('-' * 20)
print '-' * 20
# -------------- region end : 3. 初始化CNN模型并训练 ---------------
# -------------- region start : 4. 预测 -------------
if verbose > 1:
logging.debug('-' * 20)
print '-' * 20
logging.debug('4. 预测')
