编写pytorch代码，将data_set = np.array(data_set)保存为Numpy数组

以下是将`data_set`保存为Numpy数组的代码：

import numpy as np

# convert to numpy array
data_set = np.array(data_set)

# save as Numpy array
np.save('data_set.npy', data_set)

在这里，我们使用`numpy`库将`data_set`转换为Numpy数组。然后，我们使用`numpy`库的`np.save`函数将Numpy数组保存为Numpy数组文件。我们将Numpy数组文件命名为`data_set.npy`。现在，我们可以在需要使用数据集的代码中使用`numpy.load`函数加载该文件，以便使用Numpy数组。

相关问题

def __getitem__(self, index): if self.args.cached: (label, _, image) = self.data[index] else: (label, _file) = self.file_indexes[index] image = self._load_rml(_file) n_label = self.classes.index(label) return torch.tensor(image).float(), torch.tensor(n_label).long()把这段代码基于pytorch改为基于mindspore

以下是基于MindSpore的代码实现：

import mindspore.dataset as ds
import mindspore.dataset.transforms.py_transforms as py_transforms
import mindspore.common.dtype as mstype
import numpy as np

class MyDataset:
    def __init__(self, args):
        self.args = args
        self.classes = sorted(list(set([item[0] for item in self.args.data])))
        self.data = self.args.data
        self.file_indexes = [(item[0], idx) for idx, item in enumerate(self.args.data)]

    def _load_rml(self, file):
        # 加载数据的代码
        pass

    def __getitem__(self, index):
        if self.args.cached:
            (label, _, image) = self.data[index]
        else:
            (label, file_idx) = self.file_indexes[index]
            image = self._load_rml(self.args.data[file_idx][1])
        n_label = self.classes.index(label)
        return np.array(image).astype(np.float32), np.array(n_label).astype(np.int32)
    
    def __len__(self):
        return len(self.file_indexes)

# 数据增强
transform = py_transforms.Compose([
    py_transforms.Resize((224, 224)),
    py_transforms.RandomHorizontalFlip(),
    py_transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 数据集加载
ds.config.set_seed(1)
ds_train = ds.GeneratorDataset(MyDataset(args), ["image", "label"])
ds_train = ds_train.shuffle(buffer_size=1000)
ds_train = ds_train.batch(batch_size=args.batch_size, drop_remainder=True)
ds_train = ds_train.map(operations=transform, input_columns="image", num_parallel_workers=4)
ds_train = ds_train.map(operations=lambda x, y: (mindspore.Tensor(x, mstype.float32), mindspore.Tensor(y, mstype.int32)))

注意：MindSpore的数据增强需要使用`transforms`模块中的函数，而数据集加载则需要使用`GeneratorDataset`类。在MindSpore中，需要使用`mindspore.Tensor`将数据转换为张量类型。

写一个python Flask销售预测系统，系统有一个suanfa.py文件，该文件的作用：先读取shuju.csv（共有24条数据，包含Year、Month和TotalPrice三个属性），然后用scaler将TotalPrice进行归一化处理，之后定义一个函数def split_data(data, lookback):将数据集划分为测试集（0.2）和训练集（0.8），data_raw = data.to_numpy()，lookback = 4，然后再将划分完成后的测试集和训练集转换为PyTorch张量，然后定义超参数，定义算法模型model=LSTM（）、损失函数和优化器（Adam）然后训练模型求出MSE，保存模型。有一个predict.html文件：里面有一个日期选择框和一个销售额预测按钮，用户选择好年份和月份后点击预测按钮系统就开始调用保存好的模型来对所选月份的销售额进行预测，然后将预测结果返回到日期选择框下面的结果返回框中；有一个app.py文件：定义路径。用flask和bootstrap、LayUI写出完整详细代码

这是一个比较完整的 Flask 销售预测系统，包含了前端页面和后端算法模型。使用了 Bootstrap 和 LayUI 两个前端框架，用 PyTorch 框架实现了 LSTM 模型进行销售预测。

suanfa.py 文件：

import torch
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler


def split_data(data, lookback):
    data_raw = data.to_numpy()
    data = []
    # create all possible sequences of length lookback
    for index in range(len(data_raw) - lookback):
        data.append(data_raw[index: index + lookback])
    data = np.array(data)
    
    test_set_size = int(np.round(0.2*data.shape[0]))
    train_set_size = data.shape[0] - (test_set_size)
    
    x_train = torch.from_numpy(np.array(data[:train_set_size,:-1,:]))
    y_train = torch.from_numpy(np.array(data[:train_set_size,-1,:]))
    
    x_test = torch.from_numpy(np.array(data[train_set_size:,:-1,:]))
    y_test = torch.from_numpy(np.array(data[train_set_size:,-1,:]))
    
    return x_train.float(), y_train.float(), x_test.float(), y_test.float()


class LSTM(torch.nn.Module):
    def __init__(self, input_dim, hidden_dim, num_layers, output_dim):
        super(LSTM, self).__init__()
        self.hidden_dim = hidden_dim
        self.num_layers = num_layers
        self.lstm = torch.nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)
        self.fc = torch.nn.Linear(hidden_dim, output_dim)
    
    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()
        
        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))
        
        out = self.fc(out[:, -1, :])
        return out


if __name__ == '__main__':
    data = pd.read_csv('shuju.csv')
    
    scaler = MinMaxScaler(feature_range=(-1, 1))
    data['TotalPrice'] = scaler.fit_transform(data['TotalPrice'].values.reshape(-1,1))
    
    x_train, y_train, x_test, y_test = split_data(data[['Year','Month','TotalPrice']], 4)
    
    input_dim = 3
    hidden_dim = 12
    num_layers = 1
    output_dim = 1
    num_epochs = 1000
    
    model = LSTM(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers)
    loss_fn = torch.nn.MSELoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
    
    for t in range(num_epochs):
        y_pred = model(x_train)
        loss = loss_fn(y_pred, y_train)
        if t % 100 == 0:
            print("Epoch ", t, "MSE: ", loss.item())
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    
    torch.save(model.state_dict(), 'model_lstm.pth')

predict.html 文件：

<!DOCTYPE html>
<html>
<head>
	<meta charset="UTF-8">
	<title>销售预测系统</title>
	<link rel="stylesheet" href="https://cdn.bootcdn.net/ajax/libs/layui/2.5.7/css/layui.min.css">
	<link rel="stylesheet" href="https://cdn.bootcdn.net/ajax/libs/twitter-bootstrap/4.5.3/css/bootstrap.min.css">
	<script src="https://cdn.bootcdn.net/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
	<script src="https://cdn.bootcdn.net/ajax/libs/layui/2.5.7/layui.min.js"></script>
	<script src="https://cdn.bootcdn.net/ajax/libs/twitter-bootstrap/4.5.3/js/bootstrap.min.js"></script>
</head>
<body>
	<div class="container">
		<div class="row justify-content-center mt-5">
			<div class="col-md-6">
				<div class="form-group">
					<label for="year">年份：</label>
					<select class="form-control" id="year">
						<option value="2014">2014</option>
						<option value="2015">2015</option>
						<option value="2016">2016</option>
						<option value="2017">2017</option>
						<option value="2018">2018</option>
						<option value="2019">2019</option>
						<option value="2020">2020</option>
					</select>
				</div>
				<div class="form-group">
					<label for="month">月份：</label>
					<select class="form-control" id="month">
						<option value="1">1</option>
						<option value="2">2</option>
						<option value="3">3</option>
						<option value="4">4</option>
						<option value="5">5</option>
						<option value="6">6</option>
						<option value="7">7</option>
						<option value="8">8</option>
						<option value="9">9</option>
						<option value="10">10</option>
						<option value="11">11</option>
						<option value="12">12</option>
					</select>
				</div>
				<div class="form-group">
					<button class="btn btn-primary" onclick="predict()">销售额预测</button>
				</div>
				<div class="form-group">
					<label for="result">预测结果：</label>
					<input type="text" class="form-control" id="result" disabled>
				</div>
			</div>
		</div>
	</div>
	<script>
		function predict() {
			var year = $('#year').val();
			var month = $('#month').val();
			$.ajax({
				url: '/predict',
				method: 'POST',
				data: {
					'year': year,
					'month': month
				},
				success: function(response) {
					$('#result').val(response);
				}
			});
		}
	</script>
</body>
</html>

app.py 文件：

from flask import Flask, render_template, request, jsonify
import suanfa
import torch
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

app = Flask(__name__)

@app.route('/')
def index():
    return render_template('predict.html')

@app.route('/predict', methods=['POST'])
def predict():
    year = int(request.form['year'])
    month = int(request.form['month'])
    
    data = pd.read_csv('shuju.csv')
    scaler = MinMaxScaler(feature_range=(-1, 1))
    data['TotalPrice'] = scaler.fit_transform(data['TotalPrice'].values.reshape(-1,1))
    
    x_train, y_train, x_test, y_test = suanfa.split_data(data[['Year','Month','TotalPrice']], 4)
    
    input_dim = 3
    hidden_dim = 12
    num_layers = 1
    output_dim = 1
    
    model = suanfa.LSTM(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers)
    model.load_state_dict(torch.load('model_lstm.pth'))
    
    test_inputs = torch.from_numpy(np.array(data[['Year','Month','TotalPrice']][-3:].values))
    test_inputs = test_inputs.view(1, -1, 3).float()
    model.eval()
    y_pred = scaler.inverse_transform(model(test_inputs).detach().numpy())
    
    return jsonify(round(float(y_pred)), 2)

if __name__ == '__main__':
    app.run(debug=True)

注意：在运行之前需要将 `shuju.csv` 文件和 `model_lstm.pth` 文件放到与 `app.py` 文件同一目录下。另外，需要安装 Flask 和 PyTorch 库，可以使用以下命令进行安装：

pip install flask torch pandas scikit-learn