added 07 and 08

patrickloeber · patrickloeber · commit 322eeb4aa4b8 · 2019-12-29T23:09:29.000+01:00
diff --git a/07_linear_regression.py b/07_linear_regression.py
@@ -0,0 +1,51 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from sklearn import datasets
+import matplotlib.pyplot as plt
+
+# 0) Prepare data
+X_numpy, y_numpy = datasets.make_regression(n_samples=100, n_features=1, noise=20, random_state=4)
+
+# cast to float Tensor
+X = torch.from_numpy(X_numpy.astype(np.float32))
+y = torch.from_numpy(y_numpy.astype(np.float32))
+y = y.view(y.shape[0], 1)
+
+n_samples, n_features = X.shape
+
+# 1) Model
+# Linear model f = wx + b
+input_size = n_features
+output_size = 1
+model = nn.Linear(input_size, output_size)
+
+# 2) Loss and optimizer
+learning_rate = 0.01
+
+criterion = nn.MSELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  
+
+# 3) Training loop
+num_epochs = 100
+for epoch in range(num_epochs):
+    # Forward pass and loss
+    y_predicted = model(X)
+    loss = criterion(y_predicted, y)
+    
+    # Backward pass and update
+    loss.backward()
+    optimizer.step()
+
+    # zero grad before new step
+    optimizer.zero_grad()
+
+    if (epoch+1) % 10 == 0:
+        print(f'epoch: {epoch+1}, loss = {loss.item():.4f}')
+
+# Plot
+predicted = model(X).detach().numpy()
+
+plt.plot(X_numpy, y_numpy, 'ro')
+plt.plot(X_numpy, predicted, 'b')
+plt.show()
diff --git a/08_logistic_regression.py b/08_logistic_regression.py
@@ -0,0 +1,69 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from sklearn import datasets
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+
+# 0) Prepare data
+bc = datasets.load_breast_cancer()
+X, y = bc.data, bc.target
+
+n_samples, n_features = X.shape
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)
+
+# scale
+sc = StandardScaler()
+X_train = sc.fit_transform(X_train)
+X_test = sc.transform(X_test)
+
+X_train = torch.from_numpy(X_train.astype(np.float32))
+X_test = torch.from_numpy(X_test.astype(np.float32))
+y_train = torch.from_numpy(y_train.astype(np.float32))
+y_test = torch.from_numpy(y_test.astype(np.float32))
+
+y_train = y_train.view(y_train.shape[0], 1)
+y_test = y_test.view(y_test.shape[0], 1)
+
+# 1) Model
+# Linear model f = wx + b , sigmoid at the end
+class Model(nn.Module):
+    def __init__(self, n_input_features):
+        super(Model, self).__init__()
+        self.linear = nn.Linear(n_input_features, 1)  # One in and one out
+
+    def forward(self, x):
+        y_pred = torch.sigmoid(self.linear(x))
+        return y_pred
+
+model = Model(n_features)
+
+# 2) Loss and optimizer
+num_epochs = 100
+learning_rate = 0.01
+criterion = nn.BCELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
+
+# 3) Training loop
+for epoch in range(num_epochs):
+    # Forward pass and loss
+    y_pred = model(X_train)
+    loss = criterion(y_pred, y_train)
+
+    # Backward pass and update
+    loss.backward()
+    optimizer.step()
+
+    # zero grad before new step
+    optimizer.zero_grad()
+
+    if (epoch+1) % 10 == 0:
+        print(f'epoch: {epoch+1}, loss = {loss.item():.4f}')
+
+
+with torch.no_grad():
+    y_predicted = model(X_test)
+    y_predicted_cls = y_predicted.round()
+    acc = y_predicted_cls.eq(y_test).sum() / float(y_test.shape[0])
+    print(f'accuracy: {acc.item():.4f}')
