human
I am working on the file ".ipynb_checkpoints/jpx-tokyo-simple-lstm-network-scuec-checkpoint.ipynb".
The first task is:
Trainig
For each stock, LSTM training is conducted by repeatedly creating a data set and training the model.
To see the learning status, check the phenomenon of the loss function.
　→Can be learned.
Can you write Python program for it?
assistant
#Here is the Python program you requested.

import matplotlib.pyplot as plt
plt.plot(log_train[0][1:], log_train[1][1:])
plt.plot(log_eval[0][1:], log_eval[1][1:])
plt.xlabel('epoch')
plt.ylabel('loss')
plt.show()

from tqdm import tqdm
import time
import os
import copy

output_dir = "output_lstm"
if not os.path.exists(output_dir):
    os.makedirs(output_dir)

epochs = 20
batch_size = 512
seq_len = 14
num_layers = 2
input_size = 5
lstm_dim = 64
dropout = 0.

# Check wheter GPU is available
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

# Model Instantiation
model = LSTM(d_feat=input_size, hidden_size=lstm_dim, num_layers=num_layers, dropout=dropout)
model.to(device)
model.train()
# setting optimizer
lr = 0.0001
weight_decay = 1.0e-05
optimizer = torch.optim.Adagrad(model.parameters(), lr=lr, weight_decay=weight_decay)
# setting criterion
criterion = nn.MSELoss()

def train_epoch(train_df, model, seq_len=30, batch_size=512):
    groups = train_df.groupby(['SecuritiesCode'])
    total_loss = 0.
    iteration = 0
    model.train()
    
    def collect_batch_index():  # index for a stock with seq_len continuous days
        batch_index = []
        for sc, group in groups:
            indices = np.arange(len(group))
            for i in range(len(indices))[:: seq_len]:
                if len(indices) - i < seq_len:
                    break
                batch_index.append(group.index[i: i + seq_len])
        return batch_index
            

    batch_index = collect_batch_index() 
    indices = np.arange(len(batch_index))
    np.random.shuffle(indices)
    
    for i in range(len(indices))[:: batch_size]:

        # if len(indices) - i < batch_size:
        #     break
        
        x_train = []
        y_train = []
        for index in indices[i: i + batch_size]:
            values = train_df.loc[batch_index[index]].values
            x_train.append(values[:, 3: 3 + input_size].astype(np.float32))
            y_train.append(values[:, -1][-1])
        
        # print(y_train)
            
        feature =...