# [layer2, 2, -1, 'valid', (2, 1), 0.25, 'relu', 'none'] ], full_connected_layer_units=[ # (hidden1, 0.5, 'relu', 'batch_normalization'), # (hidden2, 0.5, 'relu', 'none'), ], embedding_dropout_rate=0., nb_epoch=30, nb_batch=5, earlyStoping_patience=20, lr=1e-2, ) onehot_cnn.print_model_descibe() print(onehot_cnn.fit( (train_X_feature, train_y), (test_X_feature, test_y))) # onehot_cnn.accuracy((train_X_feature, train_y), transform_input=False) y_pred, is_correct, accu, f1, test_loss = onehot_cnn.accuracy((test_X_feature, test_y)) result_file_path = 'result/onehot_%s_%d.csv' % (feature_type, rand_seed) data_util.save_result(test_data, predict=y_pred, is_correct=is_correct, path=result_file_path) logging.debug('=' * 20) # **************************************************************** # ------------- region end : 3、构建onehot编码 ------------- # **************************************************************** logging.debug('=' * 20) # ------------------------------------------------------------------------------
[layer1, 2, -1, "valid", [-2, 1]], # [100,3,*word_embedding_dim,'valid',[1,1]], [layer1, 4, -1, "valid", [-2, 1]], [layer1, 6, -1, "valid", [-2, 1]], ], conv2_filter_type=[[layer2, 3, -1, "valid", [-2, 1]]], full_connected_layer_units=[hidden1, hidden2], output_dropout_rate=0.5, nb_epoch=30, nb_batch=32, earlyStoping_patience=30, lr=1e-2, ) onehot_cnn.print_model_descibe() onehot_cnn.fit((train_X_feature, train_y), (test_X_feature, test_y)) onehot_cnn.accuracy((train_X_feature, train_y), transform_input=False) onehot_cnn.accuracy((test_X_feature, test_y), transform_input=False) # 五折 print ("五折") counter = 0 for dev_X, dev_y, val_X, val_y in data_util.get_k_fold_data(k=5, data=(train_X_feature, train_y)): counter += 1 # quit() # print ("第%d个验证" % counter) print ("-" * 80) onehot_cnn = OnehotBowCNN(