質問編集履歴

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2020/11/30 08:19

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@@ -372,7 +372,7 @@
-```エラーコード
+```Error
 Epoch: 1

変更点追加

2020/11/30 08:19

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@@ -20,7 +20,11 @@
 import matplotlib.pyplot as plt
-↓
+```
+```python
 stock_data = pd.read_csv(
@@ -318,6 +322,10 @@
 ↓
+```
+```python
 for epoch in range(epochs):
@@ -360,13 +368,11 @@
   plt.show()
-↓
+```
-↓エラー内容
+```エラーコード
-↓
 Epoch: 1
@@ -406,6 +412,4 @@
 AttributeError: 'Tensor' object has no attribute 'vierq'
-コード
 ```

コード追加

2020/11/30 08:18

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@@ -6,43 +6,349 @@
+一連の流れ。
 ```python
+import torch
+import torch.nn as nn
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+↓
+stock_data = pd.read_csv(
+    "/content/drive/MyDrive/^GSPC.csv",
+    index_col = 0,
+    parse_dates=True
+)
+stock_data
+↓
+stock_data.drop(
+    ["Open", "High", "Low", "Close", "Volume"],
+    axis="columns",
+    inplace=True
+)
+stock_data
+↓
+stock_data.plot(figsize=(12, 4))
+↓
+y = stock_data["Adj Close"].values
+y
+↓
+from sklearn.preprocessing import MinMaxScaler
+↓
+scaler = MinMaxScaler(feature_range=(-1, 1))
+scaler.fit(y.reshape(-1, 1))
+y =scaler.transform(y.reshape(-1, 1))
+y
+↓
+y = torch.FloatTensor(y).view(-1)
+y
+↓
+test_size = 24
+train_seq = y[:-test_size]
+test_seq = y[-test_size:]
+↓
+plt.figure(figsize=(12, 4))
+plt.xlim(-20, len(train_seq)+20)
+plt.grid(True)
+plt.plot(train_seq)
+↓
+train_window_size = 12
+↓
+def input_data(seq, ws):
+  out = []
+  L = len(seq)
+  for i in range(L-ws):
+    window = seq[i:i+ws]
+    label = seq[i+ws:i+ws+1]
+    out.append((window, label))
+  return out
+↓
+train_data = input_data(train_seq, train_window_size)
+↓
+print("The NUmber of Training Data: ",len(train_data))
+↓
+class Model(nn.Module):
+  def __init__(self, input=1, h=50, output=1):
+    super().__init__()
+    self.hidden_size = h
+    self.lstm = nn.LSTM(input, h)
+    self.fc = nn.Linear(h, output)
+    self.hidden  = (
+        torch.zeros(1, 1, h),
+        torch.zeros(1, 1, h)
+    )
+  def forward(self, seq):
+    out, _=self.lstm(
+        seq.vierq(len(seq), 1, -1),
+        self.hidden
+    )
+    out = self.fc(
+        out.view(len(seq), -1)
+    )
+    return out[-1]
+↓
+torch.manual_seed(123)
+model = Model()
+criterion = nn.MSELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+↓
+epochs = 10
+train_losses = []
+test_losses = []
+↓
+def run_train():
+  model.train()
+  for train_window, correct_label in train_data:
+    optimizer.zero_grad()
+    model.hidden = (
+        torch.zeros(1, 1, model.hidden_size),
+        torch.zeros(1, 1, model.hidden_size),
+    )
+    train_predicred_label = model.forward(train_window)
+    train_loss = criterion(train_predicted_label, correct_label)
+    train_loss.backward()
+    optimizer.step()
+    train_losses.append(train_loss)
+↓
+a = torch.tensor([3])
+a.item()
+↓
+def run_test():
+  model.eval()
+  for i in range(test_size):
+    test_window = torch.FloatTensor(extending_seq[-test_size:])
+    with torch.no_grad():
+        model.hidden = (
+        torch.zeros(1, 1, model.hidden_size),
+        torch.zeros(1, 1, model.hidden_size),
+    )
+    test_predicted_label = mode.forward(test_window)
+    extending_seq.append(test_predicted_label())
+    test_loss = criterion(
+        torch.FloatTensor(extending_seq[-test_size:]),
+        y[len(y)-test_size:]
+    )
+    test_losses.append(test_loss)
+↓
+train_seq[-test_size:]
+↓
+train_seq[-test_size:].tolist()
+↓
 for epoch in range(epochs):
   print()
   print(f'Epoch: {epoch+1}')
-run_train()
+  run_train()
-extending_seq = train_seq[-test_size:].to;ost()
+  extending_seq = train_seq[-test_size:].tolist()
-run_test()
+  run_test()
-plt.figure(figsize=(12, 4))
+  plt.figure(figsize=(12, 4))
-plt.xlim(-20, len(y)+20)
+  plt.xlim(-21, len(y)+20)
-plt.grid(True)
+  plt.grid(True)
-plt.plot(y.numpy())
-plt.plot(
+  plt.plot(
       range(len(y)-test_size, len(y)),
@@ -52,30 +358,54 @@
-plt.show()
+  plt.show()
+↓
+↓エラー内容
+↓
+Epoch: 1
+---------------------------------------------------------------------------
+AttributeError                            Traceback (most recent call last)
+<ipython-input-85-98e8d85b09e9> in <module>()
+      4   print(f'Epoch: {epoch+1}')
+      5
+----> 6   run_train()
+      7
+      8   extending_seq = train_seq[-test_size:].tolist()
+1 frames
+<ipython-input-77-b3af88443259> in forward(self, seq)
+     18
+     19     out, _=self.lstm(
+---> 20         seq.vierq(len(seq), 1, -1),
+     21         self.hidden
+     22     )
+AttributeError: 'Tensor' object has no attribute 'vierq'
+コード
 ```
-↓エラーコード
-```error
-NameError                                 Traceback (most recent call last)
-<ipython-input-11-24515b2a1814> in <module>()
-----> 1 for epoch in range(epochs):
-      2   print()
-      3   print(f'Epoch: {epoch+1}')
-      4
-      5 run_train()
-NameError: name 'epochs' is not defined
-```

変更点追加

2020/11/30 06:28

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	@@ -1 +1 @@
1	- python ~~株分析　練習~~　エラーコード
1	+ NameError: name 'epochs' is not defined　python エラーコード

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文法修正

2020/11/30 05:39

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	@@ -1 +1 @@
1	- 株価の分析~~実装中~~　エラーコード
1	+ python 株分析　練習　エラーコード

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