From f981107b374b9c2729baaf6791c43386251e0af1 Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Thu, 29 Apr 2021 10:15:41 +0800
Subject: [PATCH 01/25] =?UTF-8?q?=E6=96=B0=E5=BB=BA=2019210680053?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 assignment-2/submission/19210680053/.keep | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 assignment-2/submission/19210680053/.keep

diff --git a/assignment-2/submission/19210680053/.keep b/assignment-2/submission/19210680053/.keep
new file mode 100644
index 0000000..e69de29
-- 
Gitee


From 256978ed39d49eb86f27851db167f6c7201f2b4e Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Thu, 29 Apr 2021 10:16:14 +0800
Subject: [PATCH 02/25] add assignment-2/submission/19210680053/README.md.

---
 assignment-2/submission/19210680053/README.md | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 assignment-2/submission/19210680053/README.md

diff --git a/assignment-2/submission/19210680053/README.md b/assignment-2/submission/19210680053/README.md
new file mode 100644
index 0000000..e69de29
-- 
Gitee


From ecd6ae6d4cbb0b0eb33f63cd6a232fe536ba52f6 Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Thu, 29 Apr 2021 10:19:12 +0800
Subject: [PATCH 03/25] update assignment-2/submission/19210680053/README.md.

---
 assignment-2/submission/19210680053/README.md | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/assignment-2/submission/19210680053/README.md b/assignment-2/submission/19210680053/README.md
index e69de29..9ca331f 100644
--- a/assignment-2/submission/19210680053/README.md
+++ b/assignment-2/submission/19210680053/README.md
@@ -0,0 +1,9 @@
+#实验报告
+###1，模型设计
+神经网络设计如下图所示
+###2，算子补充
+matmul:
+Relu:
+Log:
+Softmax:
+###3,mini_batch函数优化
-- 
Gitee


From dff8e0110f08b708cac9ef0d8789eadaa745b9cb Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Thu, 29 Apr 2021 10:19:43 +0800
Subject: [PATCH 04/25] update assignment-2/submission/19210680053/README.md.

---
 assignment-2/submission/19210680053/README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/assignment-2/submission/19210680053/README.md b/assignment-2/submission/19210680053/README.md
index 9ca331f..7183b76 100644
--- a/assignment-2/submission/19210680053/README.md
+++ b/assignment-2/submission/19210680053/README.md
@@ -1,9 +1,9 @@
-#实验报告
-###1，模型设计
+# 实验报告
+### 1，模型设计
 神经网络设计如下图所示
-###2，算子补充
+### 2，算子补充
 matmul:
 Relu:
 Log:
 Softmax:
-###3,mini_batch函数优化
+### 3,mini_batch函数优化
-- 
Gitee


From faaa4142894ef55ab0fd46f6b552606035349cfa Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Thu, 29 Apr 2021 10:20:05 +0800
Subject: [PATCH 05/25] update assignment-2/submission/19210680053/README.md.

---
 assignment-2/submission/19210680053/README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/assignment-2/submission/19210680053/README.md b/assignment-2/submission/19210680053/README.md
index 7183b76..2127b67 100644
--- a/assignment-2/submission/19210680053/README.md
+++ b/assignment-2/submission/19210680053/README.md
@@ -1,9 +1,9 @@
 # 实验报告
-### 1，模型设计
+### 1.模型设计
 神经网络设计如下图所示
-### 2，算子补充
+### 2.算子补充
 matmul:
 Relu:
 Log:
 Softmax:
-### 3,mini_batch函数优化
+### 3.mini_batch函数优化
-- 
Gitee


From 0569a4d97276fb7df23843b3e1f4098d9078fab9 Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Fri, 30 Apr 2021 14:26:46 +0800
Subject: [PATCH 06/25] update assignment-2/submission/19210680053/README.md.

---
 assignment-2/submission/19210680053/README.md | 42 ++++++++++++++++++-
 1 file changed, 40 insertions(+), 2 deletions(-)

diff --git a/assignment-2/submission/19210680053/README.md b/assignment-2/submission/19210680053/README.md
index 2127b67..be78970 100644
--- a/assignment-2/submission/19210680053/README.md
+++ b/assignment-2/submission/19210680053/README.md
@@ -2,8 +2,46 @@
 ### 1.模型设计
 神经网络设计如下图所示
 ### 2.算子补充
-matmul:
-Relu:
+####Matmul
+$$
+**Forward**
+h=X*W
+$$
+**Backward**
+$$
+\frac{\partial Y}{\partial X} = W^{T}
+$$
+
+$$
+\frac{\partial Y}{\partial W} = X^{T}
+$$
+维度变化以及Python实现如下所示：
+$$
+```
+        """
+        grad_y: shape(N, d')
+        w.T: shape(d', d)
+        """
+        grad_x=np.matmul(grad_y, W.T)
+        """
+        grad_y: shape(N, d')
+        x.T: shape(d, N)
+        """
+        grad_W=np.matmul(x.T, grad_y)
+```
+####Relu
+$$
+**Forward**
+Y=\begin{cases}
+X&X\ge0\\\\
+0&\text{otherwise}
+\end{cases}
+$$
+**Backward**
+\frac{\partial Y}{\partial X}=\begin{cases}1&X\ge0\\\\
+0&\text{otherwise}
+\end{cases}
+$$
 Log:
 Softmax:
 ### 3.mini_batch函数优化
-- 
Gitee


From 7966db3fb84c75f3504ac6c00eddaf83eaf031c1 Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Fri, 30 Apr 2021 14:27:51 +0800
Subject: [PATCH 07/25] update assignment-2/submission/19210680053/README.md.

---
 assignment-2/submission/19210680053/README.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/assignment-2/submission/19210680053/README.md b/assignment-2/submission/19210680053/README.md
index be78970..609a3c5 100644
--- a/assignment-2/submission/19210680053/README.md
+++ b/assignment-2/submission/19210680053/README.md
@@ -4,10 +4,10 @@
 ### 2.算子补充
 ####Matmul
 $$
-**Forward**
+Forward
 h=X*W
 $$
-**Backward**
+Backward
 $$
 \frac{\partial Y}{\partial X} = W^{T}
 $$
@@ -30,14 +30,14 @@ $$
         grad_W=np.matmul(x.T, grad_y)
 ```
 ####Relu
-$$
-**Forward**
+
+Forward
 Y=\begin{cases}
 X&X\ge0\\\\
 0&\text{otherwise}
 \end{cases}
 $$
-**Backward**
+Backward
 \frac{\partial Y}{\partial X}=\begin{cases}1&X\ge0\\\\
 0&\text{otherwise}
 \end{cases}
-- 
Gitee


From cb7e91275bd07e488d972b570ec58408aeed1e1c Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Fri, 30 Apr 2021 14:28:31 +0800
Subject: [PATCH 08/25] update assignment-2/submission/19210680053/README.md.

---
 assignment-2/submission/19210680053/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/assignment-2/submission/19210680053/README.md b/assignment-2/submission/19210680053/README.md
index 609a3c5..5676ade 100644
--- a/assignment-2/submission/19210680053/README.md
+++ b/assignment-2/submission/19210680053/README.md
@@ -2,7 +2,7 @@
 ### 1.模型设计
 神经网络设计如下图所示
 ### 2.算子补充
-####Matmul
+Matmul
 $$
 Forward
 h=X*W
@@ -29,7 +29,7 @@ $$
         """
         grad_W=np.matmul(x.T, grad_y)
 ```
-####Relu
+Relu
 
 Forward
 Y=\begin{cases}
-- 
Gitee


From 9f85ff06eae314c63d67bfdfaba3178b06f3728a Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Fri, 30 Apr 2021 14:28:46 +0800
Subject: [PATCH 09/25] update assignment-2/submission/19210680053/README.md.

---
 assignment-2/submission/19210680053/README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/assignment-2/submission/19210680053/README.md b/assignment-2/submission/19210680053/README.md
index 5676ade..23bcee1 100644
--- a/assignment-2/submission/19210680053/README.md
+++ b/assignment-2/submission/19210680053/README.md
@@ -5,6 +5,7 @@
 Matmul
 $$
 Forward
+$$
 h=X*W
 $$
 Backward
-- 
Gitee


From 555b033242eb0505e74155d4bc86f43d861cc301 Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Fri, 30 Apr 2021 14:29:13 +0800
Subject: [PATCH 10/25] update assignment-2/submission/19210680053/README.md.

---
 assignment-2/submission/19210680053/README.md | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/assignment-2/submission/19210680053/README.md b/assignment-2/submission/19210680053/README.md
index 23bcee1..7745180 100644
--- a/assignment-2/submission/19210680053/README.md
+++ b/assignment-2/submission/19210680053/README.md
@@ -2,12 +2,10 @@
 ### 1.模型设计
 神经网络设计如下图所示
 ### 2.算子补充
-Matmul
-$$
+Matmul////
 Forward
 $$
-h=X*W
-$$
+h=X*W////
 Backward
 $$
 \frac{\partial Y}{\partial X} = W^{T}
-- 
Gitee


From f5634d9d68c9ee9b44dadfa89bd17b8dfdf2b1db Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Fri, 30 Apr 2021 14:34:45 +0800
Subject: [PATCH 11/25] update relu

---
 assignment-2/submission/19210680053/README.md | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/assignment-2/submission/19210680053/README.md b/assignment-2/submission/19210680053/README.md
index 7745180..3691dd4 100644
--- a/assignment-2/submission/19210680053/README.md
+++ b/assignment-2/submission/19210680053/README.md
@@ -2,20 +2,21 @@
 ### 1.模型设计
 神经网络设计如下图所示
 ### 2.算子补充
-Matmul////
+**Matmul**
+
 Forward
 $$
-h=X*W////
+h=X*W
+$$
+
 Backward
 $$
 \frac{\partial Y}{\partial X} = W^{T}
 $$
-
 $$
 \frac{\partial Y}{\partial W} = X^{T}
 $$
 维度变化以及Python实现如下所示：
-$$
 ```
         """
         grad_y: shape(N, d')
@@ -28,15 +29,17 @@ $$
         """
         grad_W=np.matmul(x.T, grad_y)
 ```
-Relu
+**Relu**
 
 Forward
+$$
 Y=\begin{cases}
 X&X\ge0\\\\
 0&\text{otherwise}
 \end{cases}
 $$
 Backward
+$$
 \frac{\partial Y}{\partial X}=\begin{cases}1&X\ge0\\\\
 0&\text{otherwise}
 \end{cases}
-- 
Gitee


From 0fc5d8ef11be0b6d89932bfd77a9c28e21e67c6c Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Fri, 30 Apr 2021 16:35:21 +0800
Subject: [PATCH 12/25] operator edied

---
 assignment-2/submission/19210680053/README.md | 105 +++++++++++++++++-
 1 file changed, 104 insertions(+), 1 deletion(-)

diff --git a/assignment-2/submission/19210680053/README.md b/assignment-2/submission/19210680053/README.md
index 3691dd4..2771ae1 100644
--- a/assignment-2/submission/19210680053/README.md
+++ b/assignment-2/submission/19210680053/README.md
@@ -44,6 +44,109 @@ $$
 0&\text{otherwise}
 \end{cases}
 $$
-Log:
+Python实现如下所示：
+```
+ def forward(self, x):
+        self.memory['x'] = x
+        return np.where(x > 0, x, np.zeros_like(x))
+    
+    def backward(self, grad_y):
+        """
+        grad_y: same shape as x
+        """
+        x=self.memory['x']
+        grad_x=grad_y*np.where(x>0,1,0)
+        return grad_x
+```
+**Log**
+
+Forward
+$$
+Y=Log(x+epsilon)
+$$
+Backward
+$$
+Y=1/(x+epsilon)
+$$
+Python实现如下所示：
+```
+    def forward(self, x):
+        """
+        x: shape(N, c)
+        """
+        
+        out = np.log(x + self.epsilon)
+        self.memory['x'] = x
+        
+        return out
+    
+    def backward(self, grad_y):
+        
+        """
+        grad_y: same shape as x
+        """
+        x=self.memory['x']
+        grad_x=grad_y*(1./(x+self.epsilon))
+        return grad_x
+```
 Softmax:
+
+Forward
+$$
+Y_i = \frac{e^{X_i}}{\sum_{k=1}^n e^{X_k}}
+$$
+
+Backward
+$$
+\frac{\partial Y_i}{\partial X_j} =
+	\begin{cases}
+    Y_i \times (1 - Y_i) & i = j\\\\
+    -Y_i \times Y_j & i \neq j
+    \end{cases}
+$$
+Python实现如下所示：
+
+softmax的反向传播通过逐个元素判断求导进行实现
+```
+    def forward(self, x):
+        """
+        x: shape(N, c)
+        """
+        ex = np.exp(x)
+        rowsum = np.sum(ex,axis=1)
+        rowsum = rowsum[:,np.newaxis]
+        softmax = ex / rowsum
+        self.memory['softmax'] = softmax
+        return softmax
+ 
+    def backward(self, grad_y):
+        softmax = self.memory['softmax']
+        # print(sumx.shape)
+        [ROWS, COLUMNS] = softmax.shape
+        grad_x = []
+        grad_x=[[0 for i in range(COLUMNS)] for j in range(ROWS)]
+        for i in range(len(grad_x)):
+            for j in range(len(grad_x[0])):
+                for k in range(len(grad_x[0])):
+                    if j == k:
+                        
+                        grad_x[i][j] += (1 - softmax[i][k]) * softmax[i][k] * grad_y[i][k]
+                    else:
+                        grad_x[i][j] += -softmax[i][j] * softmax[i][k] * grad_y[i][k]
+        grad_x = np.array(grad_x)
+```
 ### 3.mini_batch函数优化
+原有mini_batch方法是将元素打乱重排进行训练 分别将数据和标签储存进对应list 根据batch_size 从打乱的index中一次取出相应大小的数据
+Python实现如下所示：
+```
+def mini_batch(dataset,batch_size=128):
+    data = []
+    label = []
+    for each in dataset:
+        data.append(np.array(each[0]))
+        label.append(each[1])
+    data = np.array(data)
+    label = np.array(label)
+    index=data.shape[0]
+    index = list(np.random.permutation(index))
+    return [(data[index[i:i + batch_size]], label[index[i:i + batch_size]]) for i in range(0, len(data), batch_size)]
-- 
Gitee


From 58449ac164562e205d8ff62936058f0e773a4b71 Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Fri, 30 Apr 2021 16:35:52 +0800
Subject: [PATCH 13/25] huan

---
 assignment-2/submission/19210680053/README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/assignment-2/submission/19210680053/README.md b/assignment-2/submission/19210680053/README.md
index 2771ae1..1c208b0 100644
--- a/assignment-2/submission/19210680053/README.md
+++ b/assignment-2/submission/19210680053/README.md
@@ -137,6 +137,7 @@ softmax的反向传播通过逐个元素判断求导进行实现
 ```
 ### 3.mini_batch函数优化
 原有mini_batch方法是将元素打乱重排进行训练 分别将数据和标签储存进对应list 根据batch_size 从打乱的index中一次取出相应大小的数据
+
 Python实现如下所示：
 ```
 def mini_batch(dataset,batch_size=128):
-- 
Gitee


From d34a7a1939a8e29832561f9778c3b3ea49f96734 Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Fri, 30 Apr 2021 16:37:03 +0800
Subject: [PATCH 14/25] cub

---
 assignment-2/submission/19210680053/README.md | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/assignment-2/submission/19210680053/README.md b/assignment-2/submission/19210680053/README.md
index 1c208b0..1367801 100644
--- a/assignment-2/submission/19210680053/README.md
+++ b/assignment-2/submission/19210680053/README.md
@@ -4,12 +4,12 @@
 ### 2.算子补充
 **Matmul**
 
-Forward
+**Forward**
 $$
 h=X*W
 $$
 
-Backward
+**Backward**
 $$
 \frac{\partial Y}{\partial X} = W^{T}
 $$
@@ -31,14 +31,14 @@ $$
 ```
 **Relu**
 
-Forward
+**Forward**
 $$
 Y=\begin{cases}
 X&X\ge0\\\\
 0&\text{otherwise}
 \end{cases}
 $$
-Backward
+**Backward**
 $$
 \frac{\partial Y}{\partial X}=\begin{cases}1&X\ge0\\\\
 0&\text{otherwise}
@@ -64,7 +64,7 @@ Forward
 $$
 Y=Log(x+epsilon)
 $$
-Backward
+**Backward**
 $$
 Y=1/(x+epsilon)
 $$
@@ -91,12 +91,12 @@ Python实现如下所示：
 ```
 Softmax:
 
-Forward
+**Forward**
 $$
 Y_i = \frac{e^{X_i}}{\sum_{k=1}^n e^{X_k}}
 $$
 
-Backward
+**Backward**
 $$
 \frac{\partial Y_i}{\partial X_j} =
 	\begin{cases}
-- 
Gitee


From 1ca1ab6cc75933f66b7fc7d8e7e74113a635d582 Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Fri, 30 Apr 2021 16:38:51 +0800
Subject: [PATCH 15/25] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20img?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 assignment-2/submission/19210680053/img/.keep | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 assignment-2/submission/19210680053/img/.keep

diff --git a/assignment-2/submission/19210680053/img/.keep b/assignment-2/submission/19210680053/img/.keep
new file mode 100644
index 0000000..e69de29
-- 
Gitee


From 29b82f5bc3206946a08692b7b38e34c52e54efa5 Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Fri, 30 Apr 2021 16:40:59 +0800
Subject: [PATCH 16/25] Loss_mi b

---
 .../19210680053/loss_value mini batch.png       | Bin 0 -> 12585 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 assignment-2/submission/19210680053/loss_value mini batch.png

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From 4a98afc12cf7c57541c1b5c0926d07155d692d8d Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Fri, 30 Apr 2021 16:42:02 +0800
Subject: [PATCH 17/25] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20as?=
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MIME-Version: 1.0
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-- 
Gitee


From a85dbc701f286a15864f845127afe3e5421bd77f Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Fri, 30 Apr 2021 16:42:18 +0800
Subject: [PATCH 18/25] mini bat

---
 .../19210680053/img/loss_value mini batch.png   | Bin 0 -> 12585 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 assignment-2/submission/19210680053/img/loss_value mini batch.png

diff --git a/assignment-2/submission/19210680053/img/loss_value mini batch.png b/assignment-2/submission/19210680053/img/loss_value mini batch.png
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literal 0
HcmV?d00001

-- 
Gitee


From ce4f9d70d6d8ee88d40e6dc77c2ea2e4737676af Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Sat, 1 May 2021 13:14:26 +0800
Subject: [PATCH 19/25] new edit

---
 assignment-2/submission/19210680053/README.md | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/assignment-2/submission/19210680053/README.md b/assignment-2/submission/19210680053/README.md
index 1367801..a3c32f8 100644
--- a/assignment-2/submission/19210680053/README.md
+++ b/assignment-2/submission/19210680053/README.md
@@ -151,3 +151,14 @@ def mini_batch(dataset,batch_size=128):
     index=data.shape[0]
     index = list(np.random.permutation(index))
     return [(data[index[i:i + batch_size]], label[index[i:i + batch_size]]) for i in range(0, len(data), batch_size)]
+```
+### 4.实验结果
+**准确率如下**
+
+[0] Accuracy: 0.9367
+
+[1] Accuracy: 0.9607
+
+[2] Accuracy: 0.9687
+
+![](./img/loss_value mini batch.jpg)
\ No newline at end of file
-- 
Gitee


From 36e4286308977367d8342ef2a8e2fb8df4f5a873 Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Sat, 1 May 2021 13:35:39 +0800
Subject: [PATCH 20/25] mini_batch from util

---
 .../19210680053/img/mini_batch_orig.png         | Bin 0 -> 12684 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 assignment-2/submission/19210680053/img/mini_batch_orig.png

diff --git a/assignment-2/submission/19210680053/img/mini_batch_orig.png b/assignment-2/submission/19210680053/img/mini_batch_orig.png
new file mode 100644
index 0000000000000000000000000000000000000000..c4225c0e01087cb4abe343c4c8d295cb35ec7e22
GIT binary patch
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literal 0
HcmV?d00001

-- 
Gitee


From bf1dd637e66bd59ad556d495124d579f32a44a9e Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Sat, 1 May 2021 13:36:37 +0800
Subject: [PATCH 21/25] new info

---
 assignment-2/submission/19210680053/README.md | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/assignment-2/submission/19210680053/README.md b/assignment-2/submission/19210680053/README.md
index a3c32f8..3eff49c 100644
--- a/assignment-2/submission/19210680053/README.md
+++ b/assignment-2/submission/19210680053/README.md
@@ -129,14 +129,17 @@ softmax的反向传播通过逐个元素判断求导进行实现
             for j in range(len(grad_x[0])):
                 for k in range(len(grad_x[0])):
                     if j == k:
-                        
                         grad_x[i][j] += (1 - softmax[i][k]) * softmax[i][k] * grad_y[i][k]
                     else:
                         grad_x[i][j] += -softmax[i][j] * softmax[i][k] * grad_y[i][k]
         grad_x = np.array(grad_x)
 ```
 ### 3.mini_batch函数优化
-原有mini_batch方法是将元素打乱重排进行训练 分别将数据和标签储存进对应list 根据batch_size 从打乱的index中一次取出相应大小的数据
+原有mini_batch方法是将元素打乱重排进行训练 分别将数据和标签储存进对应list 
+
+将index进行打乱
+
+根据batch_size 从乱序index中一次取出相应大小的数据及标签进行训练
 
 Python实现如下所示：
 ```
@@ -161,4 +164,12 @@ def mini_batch(dataset,batch_size=128):
 
 [2] Accuracy: 0.9687
 
-![](./img/loss_value mini batch.jpg)
\ No newline at end of file
+![](./img/loss_value%20mini%20batch.png)
+
+[0] Accuracy: 0.9441
+
+[1] Accuracy: 0.9635
+
+[2] Accuracy: 0.9721
+
+![](./img/mini_batch_orig.png)
\ No newline at end of file
-- 
Gitee


From 76a1f45befde3f551426ccdc6612cb1b478f7338 Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Sat, 1 May 2021 13:39:41 +0800
Subject: [PATCH 22/25] updated_fully

---
 assignment-2/submission/19210680053/README.md | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/assignment-2/submission/19210680053/README.md b/assignment-2/submission/19210680053/README.md
index 3eff49c..ff88aaf 100644
--- a/assignment-2/submission/19210680053/README.md
+++ b/assignment-2/submission/19210680053/README.md
@@ -158,6 +158,8 @@ def mini_batch(dataset,batch_size=128):
 ### 4.实验结果
 **准确率如下**
 
+使用**更新后的mini_batch函数**
+
 [0] Accuracy: 0.9367
 
 [1] Accuracy: 0.9607
@@ -166,10 +168,14 @@ def mini_batch(dataset,batch_size=128):
 
 ![](./img/loss_value%20mini%20batch.png)
 
+使用**util.py中的mini_batch函数**
+
 [0] Accuracy: 0.9441
 
 [1] Accuracy: 0.9635
 
 [2] Accuracy: 0.9721
 
-![](./img/mini_batch_orig.png)
\ No newline at end of file
+![](./img/mini_batch_orig.png)
+
+两者准确性基本相同
\ No newline at end of file
-- 
Gitee


From 960b2e1b7e33bf566d5024075bdfe0c1c4cbdac5 Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Sat, 1 May 2021 13:47:54 +0800
Subject: [PATCH 23/25] =?UTF-8?q?operator=E2=80=94model=5Ftrain?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../submission/19210680053/numpy_fnn.py       | 214 ++++++++++++++++++
 .../submission/19210680053/numpy_mnist.py     |  57 +++++
 2 files changed, 271 insertions(+)
 create mode 100644 assignment-2/submission/19210680053/numpy_fnn.py
 create mode 100644 assignment-2/submission/19210680053/numpy_mnist.py

diff --git a/assignment-2/submission/19210680053/numpy_fnn.py b/assignment-2/submission/19210680053/numpy_fnn.py
new file mode 100644
index 0000000..34a4a69
--- /dev/null
+++ b/assignment-2/submission/19210680053/numpy_fnn.py
@@ -0,0 +1,214 @@
+import numpy as np
+
+
+class NumpyOp:
+    
+    def __init__(self):
+        self.memory = {}
+        self.epsilon = 1e-12
+
+
+class Matmul(NumpyOp):
+    
+    def forward(self, x, W):
+        """
+        x: shape(N, d)
+        w: shape(d, d')
+        """
+        self.memory['x'] = x
+        self.memory['W'] = W
+        h = np.matmul(x, W)
+        return h
+    
+    def backward(self, grad_y):
+        """
+        grad_y: shape(N, d')
+        """
+        
+        
+        ####################
+        #      code 1      #
+        ####################
+        x=self.memory['x']
+        W=self.memory['W']
+        """
+        grad_y: shape(N, d')
+        w.T: shape(d', d)
+        """
+        grad_x=np.matmul(grad_y, W.T)
+        """
+        grad_y: shape(N, d')
+        x.T: shape(d, N)
+        """
+        grad_W=np.matmul(x.T, grad_y)
+
+        return grad_x, grad_W
+
+
+class Relu(NumpyOp):
+    
+    def forward(self, x):
+        self.memory['x'] = x
+        return np.where(x > 0, x, np.zeros_like(x))
+    
+    def backward(self, grad_y):
+        """
+        grad_y: same shape as x
+        """
+        ####################
+        #      code 2      #
+        ####################
+        x=self.memory['x']
+        grad_x=grad_y*np.where(x>0,1,0)
+        return grad_x
+
+
+class Log(NumpyOp):
+    
+    def forward(self, x):
+        """
+        x: shape(N, c)
+        """
+        
+        out = np.log(x + self.epsilon)
+        self.memory['x'] = x
+        
+        return out
+    
+    def backward(self, grad_y):
+        
+        """
+        grad_y: same shape as x
+        """
+        ####################
+        #      code 3      #
+        ####################
+        x=self.memory['x']
+        grad_x=grad_y*(1./(x+self.epsilon))
+        return grad_x
+
+
+class Softmax(NumpyOp):
+    """
+    softmax over last dimension
+    """
+    
+    def forward(self, x):
+        """
+        x: shape(N, c)
+        """
+        ####################
+       #      code 4      #
+       ####################
+#        self.memory['x'] = x
+#        exp_x=np.exp(x)
+#        softmax=np.exp(x)/np.sum(exp_x,axis=1)
+#        self.memory['softmax']=softmax
+        ex = np.exp(x)
+        rowsum = np.sum(ex,axis=1)
+        rowsum = rowsum[:,np.newaxis]
+        softmax = ex / rowsum
+        self.memory['softmax'] = softmax
+        return softmax
+ 
+    def backward(self, grad_y):
+        """
+        grad_y: same shape as x
+        """
+        ####################
+        #      code 5      #
+        ####################
+#        softmax = self.memory['softmax']
+
+        softmax = self.memory['softmax']
+        # print(sumx.shape)
+        [ROWS, COLUMNS] = softmax.shape
+        grad_x = []
+        # print(grad_y)
+#        grad_x=[[] for i in range(ROWS)]
+        grad_x=[[0 for i in range(COLUMNS)] for j in range(ROWS)]
+        for i in range(len(grad_x)):
+            for j in range(len(grad_x[0])):
+#            for j in range(m):
+#                out[i].append(0)
+                for k in range(len(grad_x[0])):
+                    if j == k:
+                        
+                        grad_x[i][j] += (1 - softmax[i][k]) * softmax[i][k] * grad_y[i][k]
+                    else:
+                        grad_x[i][j] += -softmax[i][j] * softmax[i][k] * grad_y[i][k]
+        grad_x = np.array(grad_x)
+
+        return grad_x
+class NumpyLoss:
+    
+    def __init__(self):
+        self.target = None
+    
+    def get_loss(self, pred, target):
+        self.target = target
+        return (-pred * target).sum(axis=1).mean()
+    
+    def backward(self):
+        return -self.target / self.target.shape[0]
+
+class NumpyModel:
+    def __init__(self):
+        self.W1 = np.random.normal(size=(28 * 28, 256))
+        self.W2 = np.random.normal(size=(256, 64))
+        self.W3 = np.random.normal(size=(64, 10))
+
+
+        # 以下算子会在 forward 和 backward 中使用
+        self.matmul_1 = Matmul()
+        self.relu_1 = Relu()
+        self.matmul_2 = Matmul()
+        self.relu_2 = Relu()
+        self.matmul_3 = Matmul()
+        self.softmax = Softmax()
+        self.log = Log()
+
+        # 以下变量需要在 backward 中更新
+        self.x1_grad, self.W1_grad = None, None
+        self.relu_1_grad = None
+        self.x2_grad, self.W2_grad = None, None
+        self.relu_2_grad = None
+        self.x3_grad, self.W3_grad = None, None
+        self.softmax_grad = None
+        self.log_grad = None
+        
+    
+    def forward(self, x):
+        x = x.reshape(-1, 28 * 28)
+        
+        ####################
+        #      code 6      #
+        ####################
+        
+        A1 = self.matmul_1.forward(x, self.W1) # shape(5, 4)
+        z1 = self.relu_1.forward(A1)
+        A2 = self.matmul_2.forward(z1, self.W2)
+        z2=self.relu_2.forward(A2)
+        A3=self.matmul_3.forward(z2,self.W3)
+        z3 = self.softmax.forward(A3)
+        R = self.log.forward(z3)
+        return R
+    
+    def backward(self, y):
+        ####################
+        #      code 7      #
+        ####################
+        self.log_grad=self.log.backward(y)
+        self.soft_grad=self.softmax.backward(self.log_grad)
+        self.x3_grad,self.W3_grad=self.matmul_3.backward(self.soft_grad)
+        self.relu_2_grad=self.relu_2.backward(self.x3_grad)
+        self.x2_grad,self.W2_grad=self.matmul_2.backward(self.relu_2_grad)
+        self.relu_1_grad=self.relu_1.backward(self.x2_grad)
+        self.x1_grad,self.W1_grad=self.matmul_1.backward(self.relu_1_grad)
+        pass
+        
+    
+    def optimize(self, learning_rate):
+        self.W1 -= learning_rate * self.W1_grad
+        self.W2 -= learning_rate * self.W2_grad
+        self.W3 -= learning_rate * self.W3_grad
diff --git a/assignment-2/submission/19210680053/numpy_mnist.py b/assignment-2/submission/19210680053/numpy_mnist.py
new file mode 100644
index 0000000..73e8750
--- /dev/null
+++ b/assignment-2/submission/19210680053/numpy_mnist.py
@@ -0,0 +1,57 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Apr 28 22:11:32 2021
+
+@author: hyt
+"""
+
+import numpy as np
+from numpy_fnn import NumpyModel, NumpyLoss
+from utils import download_mnist, batch, get_torch_initialization, plot_curve, one_hot
+def mini_batch(dataset,batch_size=128):
+    data = []
+    label = []
+    for each in dataset:
+        data.append(np.array(each[0]))
+        label.append(each[1])
+    data = np.array(data)
+    label = np.array(label)
+    index=data.shape[0]
+    index = list(np.random.permutation(index))
+    return [(data[index[i:i + batch_size]], label[index[i:i + batch_size]]) for i in range(0, len(data), batch_size)]
+    
+        
+def numpy_run():
+    train_dataset, test_dataset = download_mnist()
+    
+    model = NumpyModel()
+    numpy_loss = NumpyLoss()
+    model.W1, model.W2, model.W3 = get_torch_initialization()
+    
+    train_loss = []
+    
+    epoch_number = 3
+    learning_rate = 0.1
+    
+    for epoch in range(epoch_number):
+        for x, y in mini_batch(train_dataset):
+            y = one_hot(y)
+            
+            y_pred = model.forward(x)
+#            y_pred = model.forward(x.numpy())
+            loss = numpy_loss.get_loss(y_pred, y)
+
+            model.backward(numpy_loss.backward())
+            model.optimize(learning_rate)
+            
+            train_loss.append(loss.item())
+        
+        x, y = batch(test_dataset)[0]
+        accuracy = np.mean((model.forward(x).argmax(axis=1) == y))
+        print('[{}] Accuracy: {:.4f}'.format(epoch, accuracy))
+    
+    plot_curve(train_loss)
+
+
+if __name__ == "__main__":
+    numpy_run()
\ No newline at end of file
-- 
Gitee


From 4521e549a2e4b59792bce2a1efe913fe3f5e74ae Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Sat, 1 May 2021 20:24:21 +0800
Subject: [PATCH 24/25] smaller_batch

---
 .../submission/19210680053/img/sma_bat.png      | Bin 0 -> 11674 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 assignment-2/submission/19210680053/img/sma_bat.png

diff --git a/assignment-2/submission/19210680053/img/sma_bat.png b/assignment-2/submission/19210680053/img/sma_bat.png
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HcmV?d00001

-- 
Gitee


From 5e928787179b371ef88a246b42b89b84025e0b88 Mon Sep 17 00:00:00 2001
From: Yantong He <8850706+yantong-he@user.noreply.gitee.com>
Date: Sat, 1 May 2021 20:25:16 +0800
Subject: [PATCH 25/25] update

---
 assignment-2/submission/19210680053/README.md | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/assignment-2/submission/19210680053/README.md b/assignment-2/submission/19210680053/README.md
index ff88aaf..abdc546 100644
--- a/assignment-2/submission/19210680053/README.md
+++ b/assignment-2/submission/19210680053/README.md
@@ -178,4 +178,14 @@ def mini_batch(dataset,batch_size=128):
 
 ![](./img/mini_batch_orig.png)
 
-两者准确性基本相同
\ No newline at end of file
+经过比对，两者准确性基本相同
+
+使用**更新后的mini_batch函数**，选取更小**batch**
+
+[0] Accuracy: 0.9594
+
+[1] Accuracy: 0.9702
+
+[2] Accuracy: 0.9771
+
+![](./img/sma_bat.png)
\ No newline at end of file
-- 
Gitee