python一线开发 [2021]从0开始的tensorflow2.0 (三) LSTM 假设给出如下需求: 我给你几个行为 A B C D E F 要求知道A B C D E 推测出F 这种使用场景就能使用LSTM,关于LSTM本文就不废话了,因为网上介绍一大堆了,直接上干货: 首先我们需要将数据 A B C D E F 转为编号:0 1 2 3 4 6 其次,对其进行扁平归一化,并且划分训练和测试数据： ```cpp train_path = './result_list.csv' data_frame = pd.read_csv(train_path) data_frame['activity'] = data_frame['activity'].astype('float32') scaler = StandardScaler() data_frame['activity'] = scaler.fit_transform( data_frame['activity'].values.reshape(-1, 1), scaler.fit(data_frame['activity'].values.reshape(-1, 1))) train_size = int(len(data_frame['activity']) * 0.75) trainlist = data_frame['activity'][:train_size] testlist = data_frame['activity'][train_size:] ``` 读出来应该是: 0 1 2 3 4 5 .... 然后构造滑块,成0 1 2 3 4(X), 5(Y)的样子： ```cpp look_back = 64 trainX, trainY = create_dataset(trainlist, look_back, None) testX, testY = create_dataset(testlist, look_back, train_size) ``` 注意网上的create_dataset代码都过时了,大部分你直接抄就会报错,用我的就行: ```cpp def create_dataset(dataset, look_back, start_index): dataX, dataY = [], [] for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back)] dataX.append(a) if start_index != None: dataY.append(dataset[start_index + i + look_back]) else: dataY.append(dataset[i + look_back]) return np.array(dataX), np.array(dataY) ``` 记得要reshap一下: ```cpp trainX = trainX.reshape(trainX.shape[0], trainX.shape[1], 1) testX = testX.reshape(testX.shape[0], testX.shape[1], 1) ``` 之后直接训练即可: ```cpp model = keras.Sequential() model.add(keras.layers.LSTM(128, input_shape=(look_back, 1), return_sequences=True)) model.add(keras.layers.LSTM(256)) model.add(keras.layers.Dense(1)) model.compile(optimizer=keras.optimizers.Adam(), loss='mae', metrics=['MeanSquaredError']) model.fit(trainX, trainY, epochs=26, batch_size=128) model.save('./model_lstm.h5') ``` 测试:  阅读全文 2021-08-14 huoji 0 条评论
