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diff --git a/assignment-2/submission/17307130285/README.md b/assignment-2/submission/17307130285/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4af83b6606f4c7d14a52e57dbda8d5850f993137
--- /dev/null
+++ b/assignment-2/submission/17307130285/README.md
@@ -0,0 +1,197 @@
+# Assignment2
+
+**17307130285 王茹**
+
+## Formulas
+
+#### matmul
+
+$$
+Y = X*W
+$$
+矩阵求导:
+$$
+\frac{\partial Y}{\partial X} = W^{T}
+$$
+
+$$
+\frac{\partial Y}{\partial W} = X^{T}
+$$
+
+```python
+def backward(self, grad_y):
+        """
+        grad_y: shape(N, d')
+        """
+        
+        ####################
+        #      code 1      #
+        ####################
+        grad_W = np.matmul(self.memory['x'].T, grad_y)
+        grad_x = np.matmul(grad_y, self.memory['W'].T)
+        
+        return grad_x, grad_W
+```
+
+#### Relu
+
+$$
+Y_{ij}=\begin{cases}
+X_{ij}&X_{ij}\ge0\\\\
+0&\text{otherwise}
+\end{cases}
+$$
+求导：
+$$
+\frac{\partial Y_{ij}}{\partial X_{ij}}=\begin{cases}
+1&X_{ij}>0\\\\
+0&\text{otherwise}
+\end{cases}
+$$
+
+```python
+def backward(self, grad_y):
+        """
+        grad_y: same shape as x
+        """
+        
+        ####################
+        #      code 2      #
+        ####################
+        #若x大于0， grad_x的结果就是传入的grad_y。否则结果为0
+        x = self.memory['x']
+        grad_x = np.where(x > 0, grad_y, np.zeros_like(grad_y))
+        
+        return grad_x
+```
+
+#### Log
+
+$$
+Y=\ln(X+\epsilon)
+$$
+求导：
+$$
+\frac{\partial Y}{\partial X} = \frac1{X+\epsilon}
+$$
+
+```python
+def backward(self, grad_y):
+        """
+        grad_y: same shape as x
+        """
+        
+        ####################
+        #      code 3      #
+        ####################
+        grad_x = np.multiply(1./(self.memory['x'] + self.epsilon), grad_y)
+        return grad_x
+```
+
+
+
+#### Softmax
+
+![sm](README.assets/sm.png)
+
+![softmax](README.assets/softmax.png)
+
+```python
+def forward(self, x):
+        """
+        x: shape(N, c)
+        """
+        
+        ####################
+        #      code 4      #
+        ####################
+        out = []
+        for i in range(x.shape[0]):
+            t = x[i]
+            t = t - max(t)
+            t = np.exp(t)
+            out.append(t/sum(t))
+        out = np.array(out)
+        self.memory['out'] = out
+        return out
+      
+def backward(self, grad_y):
+        """
+        grad_y: same shape as x
+        """
+        
+        ####################
+        #      code 5      #
+        ####################
+        out = self.memory['out']
+        j = np.array([np.diag(i) - np.outer(i, i) for i in out])
+
+        grad_y = grad_y[:, np.newaxis, :]
+        grad_x = np.matmul(grad_y, j).squeeze(axis=1)
+        
+        return grad_x
+
+```
+
+
+
+## Experiments
+
+- mini_batch
+
+```python
+def mini_batch(dataset, batch_size=32):
+    x_train = np.array([np.array(d[0]) for d in dataset])
+    y_train = np.array([d[1] for d in dataset])
+    # 打乱数据集
+    size = x_train.shape[0]
+    idx = np.arange(size)
+    np.random.shuffle(idx)
+    # 防止batch_size不整除
+    num = int(np.ceil(size / batch_size)) * batch_size
+    batches = [(x_train[idx[i:i + batch_size]], y_train[idx[i:i + batch_size]]) for i in range(0, num, batch_size)]
+    return batches
+```
+
+
+
+### Before implementing mini_batch with numpy
+
+#### Experiment 1
+
+- epoch num: 3
+- learning rate: 0.1
+- batch size = 128
+
+[0] Accuracy: 0.9465
+[1] Accuracy: 0.9606
+[2] Accuracy: 0.9674
+
+![torch_batch](README.assets/torch_batch.png)
+
+### After
+
+#### Experiment 2
+
+- epoch num: 3
+- learning rate: 0.1
+- batch size = 128
+
+[0] Accuracy: 0.9492
+[1] Accuracy: 0.9642
+[2] Accuracy: 0.9708
+
+![ex2](README.assets/ex2.png)
+
+#### Experiment 3
+
+- epoch num: 3
+- learning rate: 0.1
+- batch size = 32
+
+[0] Accuracy: 0.9646
+[1] Accuracy: 0.9738
+[2] Accuracy: 0.9754
+
+![ex3](README.assets/ex3.png)
+
diff --git a/assignment-2/submission/17307130285/numpy_fnn.py b/assignment-2/submission/17307130285/numpy_fnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..a394d6c9cae87be907101173e2cc70592f6fb015
--- /dev/null
+++ b/assignment-2/submission/17307130285/numpy_fnn.py
@@ -0,0 +1,198 @@
+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      #
+        ####################
+        grad_W = np.matmul(self.memory['x'].T, grad_y)
+        grad_x = np.matmul(grad_y, self.memory['W'].T)
+        
+        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      #
+        ####################
+        #若x大于0， grad_x的结果就是传入的grad_y。否则结果为0
+        x = self.memory['x']
+        grad_x = np.where(x > 0, grad_y, np.zeros_like(grad_y))
+        
+        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      #
+        ####################
+        grad_x = np.multiply(1./(self.memory['x'] + self.epsilon), grad_y)
+        return grad_x
+
+
+class Softmax(NumpyOp):
+    """
+    softmax over last dimension
+    """
+    
+    def forward(self, x):
+        """
+        x: shape(N, c)
+        """
+        
+        ####################
+        #      code 4      #
+        ####################
+        out = []
+        for i in range(x.shape[0]):
+            t = x[i]
+            t = t - max(t)
+            t = np.exp(t)
+            out.append(t/sum(t))
+        out = np.array(out)
+        self.memory['out'] = out
+        return out
+    
+    def backward(self, grad_y):
+        """
+        grad_y: same shape as x
+        """
+        
+        ####################
+        #      code 5      #
+        ####################
+        out = self.memory['out']
+        j = np.array([np.diag(i) - np.outer(i, i) for i in out])
+
+        grad_y = grad_y[:, np.newaxis, :]
+        grad_x = np.matmul(grad_y, j).squeeze(axis=1)
+        
+        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。 softmax_grad, log_grad 等为算子反向传播的梯度（ loss 关于算子输入的偏导）
+        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      #
+        ####################
+        x = x.reshape(-1, 28 * 28)
+        x = self.relu_1.forward(self.matmul_1.forward(x, self.W1))
+        x = self.relu_2.forward(self.matmul_2.forward(x, self.W2))
+        x = self.matmul_3.forward(x, self.W3)
+        x = self.log.forward(self.softmax.forward(x))
+
+        
+        return x
+    
+    def backward(self, y):
+        
+        ####################
+        #      code 7      #
+        ####################
+        self.log_grad = self.log.backward(y)
+        grad_y = self.log_grad
+        self.softmax_grad = self.softmax.backward(grad_y)
+        grad_y = self.softmax_grad
+        self.x3_grad, self.W3_grad = self.matmul_3.backward(grad_y)
+        grad_y = self.x3_grad
+        self.relu_2_grad =self.relu_2.backward(grad_y)
+        grad_y = self.relu_2_grad
+        self.x2_grad, self.W2_grad =self.matmul_2.backward(grad_y)
+        grad_y = self.x2_grad
+        self.relu_1_grad = self.relu_1.backward(grad_y)
+        grad_y = self.relu_1_grad
+        self.x1_grad, self.W1_grad = self.matmul_1.backward(grad_y)
+        
+        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/17307130285/numpy_mnist.py b/assignment-2/submission/17307130285/numpy_mnist.py
new file mode 100644
index 0000000000000000000000000000000000000000..10ad4b609c44b30aef08c24917032b9b488ce4f0
--- /dev/null
+++ b/assignment-2/submission/17307130285/numpy_mnist.py
@@ -0,0 +1,51 @@
+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=32):
+    x_train = np.array([np.array(d[0]) for d in dataset])
+    y_train = np.array([d[1] for d in dataset])
+    # 打乱数据集
+    size = x_train.shape[0]
+    idx = np.arange(size)
+    np.random.shuffle(idx)
+    # 防止batch_size不整除
+    num = int(np.ceil(size / batch_size)) * batch_size
+    batches = [(x_train[idx[i:i + batch_size]], y_train[idx[i:i + batch_size]]) for i in range(0, num, batch_size)]
+    return batches
+
+
+
+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)
+            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()