質問編集履歴
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@@ -96,4 +96,75 @@
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mac/latest,jupyter/latest
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BEST REGARDS.
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BEST REGARDS.
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変更後
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105
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```
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106
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.utils.data import Dataset, DataLoader
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from sklearn.model_selection import train_test_split
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import pandas as pd
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import matplotlib.pyplot as plt
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%matplotlib inline
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class Model(nn.Module):
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def __init__(self, in_features=4, h1=8, h2=9,out_features=3):
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super().__init__()
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self.fc1 = nn.Linear(in_features, h1)
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self.fc2 = nn.Linear(h1,h2)
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self.out = nn.Linear(h2,in_features)
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def forward(self,x):
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc2(x))
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x = self.out(x)
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return x
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torch.manual_seed(32)
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model=Model()
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df = pd.read_csv('../Data/iris.csv')
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df.head()
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X = X = df.drop('target',axis=1).values
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y = df['target'].values
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X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=33)
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X_train = torch.FloatTensor(X_train)
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X_test = torch.FloatTensor(X_test)
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# y_train = F.one_hot(torch.LongTensor(y_train)) # not needed with Cross Entropy Loss
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# y_test = F.one_hot(torch.LongTensor(y_test))
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y_train = torch.LongTensor(y_train)
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y_test = torch.LongTensor(y_test)
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(model.parameters(),lr=0.01)
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model.parameters()
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epochs = 100
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lossses = []
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for i in range(epochs):
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i+=1
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y_pred = model.forward(X_train)
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loss = criterion(y_test, y_pred)
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losses.append(loss)
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if i%10 == 1:
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print(f'epoch: {i:2} loss:{loss.item():10.8f}')
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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```
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