From 8182ab8f82254c3b88bdab57aae0d5c66223942c Mon Sep 17 00:00:00 2001
From: young-hun-jo <joyh951021@gmail.com>
Date: Fri, 19 Aug 2022 17:10:00 +0900
Subject: [PATCH] =?UTF-8?q?test:=20=EB=B3=B5=EC=8A=B5=20for=20bloging?=
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---
 season3/dezero/layers.py     |  2 --
 season3/dezero/models.py     | 10 +++++----
 season3/dezero/optimizers.py | 25 +++++++++++----------
 season3/test.py              | 42 +++++++++++++-----------------------
 4 files changed, 33 insertions(+), 46 deletions(-)

diff --git a/season3/dezero/layers.py b/season3/dezero/layers.py
index e3e06ce..7ca3f21 100644
--- a/season3/dezero/layers.py
+++ b/season3/dezero/layers.py
@@ -11,7 +11,6 @@ def __init__(self):
     def __setattr__(self, name, value):
         if isinstance(value, (Parameter, Layer)):
             self._params.add(name)
-        # avoid infinite callable
         super().__setattr__(name, value)
 
     def __call__(self, *inputs):
@@ -67,4 +66,3 @@ def forward(self, x):
         return y
 
 
-
diff --git a/season3/dezero/models.py b/season3/dezero/models.py
index b786c21..f119e7a 100644
--- a/season3/dezero/models.py
+++ b/season3/dezero/models.py
@@ -18,10 +18,12 @@ def __init__(self, fc_output_sizes, activation=F.sigmoid):
 
         for i, output_size in enumerate(fc_output_sizes):
             layer = L.Linear(output_size)
-            setattr(self, 'l' + str(i), layer)
+            setattr(self, 'layer' + str(i), layer)
             self.layers.append(layer)
 
     def forward(self, x):
-        for l in self.layers[:-1]:
-            x = self.activation(l(x))
-        return self.layers[-1](x)
\ No newline at end of file
+        for layer in self.layers[:-1]:
+            x = layer(x)
+        y = self.layers[-1](x)
+        return y
+
diff --git a/season3/dezero/optimizers.py b/season3/dezero/optimizers.py
index 4e641c3..e21f280 100644
--- a/season3/dezero/optimizers.py
+++ b/season3/dezero/optimizers.py
@@ -12,16 +12,15 @@ def setup(self, target):
 
     def update(self):
         params = [p for p in self.target.params() if p.grad is not None]
-
-        # (optional) preprocessing gradients ex) gradient clipping, weight decay, ...
+        # preprocess parametrs
         for f in self.hooks:
-            f(params)
+            f(p)
 
-        # update parameters
+        # update parametrs
         for param in params:
             self.update_one(param)
 
-    def update_one(self, param):
+    def update_one(self):
         raise NotImplementedError("This method should be run outside of Optimizer class.")
 
     def add_hook(self, f):
@@ -29,27 +28,27 @@ def add_hook(self, f):
 
 
 class SGD(Optimizer):
-    def __init__(self, lr=0.01):
+    def __init__(self, learning_rate=0.01):
         super().__init__()
-        self.lr = lr
+        self.lr = learning_rate
 
     def update_one(self, param):
         param.data -= self.lr * param.grad.data
 
 
 class MomentumSGD(Optimizer):
-    def __init__(self, lr=0.01, momentum=0.9):
+    def __init__(self, learning_rate=0.01, momentum=0.9):
         super().__init__()
-        self.lr = lr
+        self.lr = learning_rate
         self.momentum = momentum
         self.vs = {}
 
     def update_one(self, param):
         v_key = id(param)
         if v_key not in self.vs:
-            self.vs[v_key] = np.zeros_like(param.data)
+            self.vs[v_key] = np.zeros_like(param)
 
-        v = self.vs[v_key]
-        v *= self.momentum
+        v = self.momentum * self.vs[v_key]
         v -= self.lr * param.grad.data
-        param.data += v
\ No newline at end of file
+        param.data += v
+
diff --git a/season3/test.py b/season3/test.py
index 38d1b25..35bf951 100644
--- a/season3/test.py
+++ b/season3/test.py
@@ -4,49 +4,37 @@
 #     sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
 
 import numpy as np
-import dezero.layers as L
+from dezero.models import MLP
 from dezero import functions as F
-from dezero import Layer
+from dezero.optimizers import SGD  # SGD를 import
 
 
 # dataset
 np.random.seed(42)
 x = np.random.rand(100, 1)
-y = np.sin(2 * np.pi * x) + np.random.rand(100, 1)   # label
+y = np.sin(2 * np.pi * x) + np.random.rand(100, 1)
 
-# build model
-model = Layer()
-model.l1 = L.Linear(10)
-model.l2 = L.Linear(1)
-
-
-def predict(x):
-    y = model.l1(x)
-    y = F.sigmoid(y)
-    y = model.l2(y)
-    return y
-
-
-lr = 0.2
+# build and compile model
+model = MLP((25, 40, 3, 1))
+lr = 0.01
 iters = 10000
+optimizer = SGD(lr).setup(model)  # 달라진 부분!
 
 for i in range(iters):
-    # predict and get loss
-    y_pred = predict(x)
+    y_pred = model(x)
     loss = F.mean_squared_error(y, y_pred)
 
-    # clear grads
     model.clear_grads()
+    loss.backward()
+
+    # 달라진 부분!
+    optimizer.update()
+
+    if (0 <= i <= 30) or (i % 1000) == 0:
+        print(f"Epoch:{i+1} -> Loss:{loss.data}")
 
-    # backward
-    loss.backward(use_heap=True)
 
-    # update parameters
-    for param in model.params():
-        param.data -= lr * param.grad.data
 
-    if i == 0 or (i+1) % 1000 == 0:
-        print(f"# Epoch:{i+1} -> Loss: {loss}")