Entrega 1

sdifiore · sdifiore · commit 24b9e486cfbd · 2022-02-28T16:36:59.000-03:00
diff --git a/.vscode/settings.json b/.vscode/settings.json
@@ -0,0 +1,3 @@
+{
+    "jupyter.jupyterServerType": "local"
+}
diff --git a/Método SGD.py b/Método SGD.py
@@ -0,0 +1,51 @@
+
+import random
+
+def SGD(self, training_data, epochs, mini_batch_size, eta,
+        test_data=None):
+
+    if test_data:
+            test_data = list(test_data)
+            n_test = len(test_data)
+
+    for j in range(epochs):
+            random.shuffle(training_data)
+            mini_batches = [ training_data[k:k+mini_batch_size]
+                for k in range(0, n, mini_batch_size)]
+                
+            for mini_batch in mini_batches:
+                self.update_mini_batch(mini_batch, eta)
+
+            if test_data:
+                print("Epoch {} : {} / {}".format(j,self.evaluate(test_data),n_test))
+            else:
+                print("Epoch {} complete".format(j))
+
+def update_mini_batch(self, mini_batch, eta):
+
+        nabla_b = [np.zeros(b.shape) for b in self.biases]
+        nabla_w = [np.zeros(w.shape) for w in self.weights]
+
+        for x, y in mini_batch:
+            delta_nabla_b, delta_nabla_w = self.backprop(x, y)
+            nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]
+            nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]
+
+        self.weights = [w-(eta/len(mini_batch))*nw
+                        for w, nw in zip(self.weights, nabla_w)]
+                        
+        self.biases = [b-(eta/len(mini_batch))*nb
+                       for b, nb in zip(self.biases, nabla_b)]
+
+def update_mini_batch(self, mini_batch, eta):
+
+        nabla_b = [np.zeros(b.shape) for b in self.biases]
+        nabla_w = [np.zeros(w.shape) for w in self.weights]
+        for x, y in mini_batch:
+            delta_nabla_b, delta_nabla_w = self.backprop(x, y)
+            nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]
+            nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]
+        self.weights = [w-(eta/len(mini_batch))*nw
+                        for w, nw in zip(self.weights, nabla_w)]
+        self.biases = [b-(eta/len(mini_batch))*nb
+                       for b, nb in zip(self.biases, nabla_b)]
diff --git a/network.py b/network.py
@@ -147,3 +147,4 @@ def sigmoid(z):
 def sigmoid_prime(z):
     """Derivative of the sigmoid function."""
     return sigmoid(z)*(1-sigmoid(z))
+

Original file line number	Diff line number	Diff line change
`@@ -0,0 +1,3 @@`
	`1`	`+{`
	`2`	`+ "jupyter.jupyterServerType": "local"`
	`3`	`+}`