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画图x-y折线图

随机梯度下降​

%matplotlib inline
import random
import torch
import matplotlib.pyplot as plt

def synthetic_data(w, b, num_examples):
    """y=wx+b+noise"""
    X = torch.normal(0, 1, size=(num_examples, len(w)))
    y = X.matmul(w) + b + torch.randn(num_examples, 1) * 0.001
    return X, y
true_w = torch.tensor([[2, -3.4, 4.2]]).reshape(3, 1)
true_b = torch.tensor(4.3)

num_examples = 10000

features, labels = synthetic_data(true_w, true_b, num_examples)

# 画图x-y折线图
figure = plt.figure(figsize=(10, 10))
plt.scatter(features[:, 2], labels, s=10, alpha=0.5)
def data_iter(batch_size, features, labels):
    num_examples = len(features)
    indices = list(range(num_examples))
    random.shuffle(indices)
    for i in range(0, num_examples, batch_size):
        j = indices[i:min(i + batch_size, num_examples)]
        yield features[j], labels[j]
batch_size = 10

for X, y in data_iter(batch_size, features, labels):
    print(X, '\n', y)
    break
w = torch.normal(0, 1, (3, 1), requires_grad=True)
b = torch.zeros(1, requires_grad=True)
def loss(y_true, y_pred):
    return sum((y_true - y_pred) ** 2) / len(y_true)

model = torch.nn.Linear(3, 1)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

epoches = 1000
for epoch in range(epoches):
    y_pred = model(features)
    loss_val = loss(labels, y_pred)
    optimizer.zero_grad()
    loss_val.backward()
    optimizer.step()
    if epoch % 100 == 0:
        print(f"Epoch: {epoch}, Loss: {loss_val.item():.4f}")
# eval
X_test, y_test = synthetic_data(true_w, true_b, num_examples//10)
y_pred = model(X_test)
print(f"loss:{loss(y_test, y_pred).item()}")

单头注意力​

import torch
import torch.nn as nn
import numpy as np

class ScaledDotProductAttention(nn.Module):
    """ Scaled Dot-Product Attention """

    def __init__(self, scale):
        super().__init__()

        self.scale = scale
        self.softmax = nn.Softmax(dim=2)

    def forward(self, q, k, v, mask=None):
        u = torch.bmm(q, k.transpose(1, 2)) # 1.Matmul [B, n_q, n_k]
        u = u / self.scale # 2.Scale

        if mask is not None:
            u = u.masked_fill(mask, -np.inf) # 3.Mask

        attn = self.softmax(u) # 4.Softmax
        output = torch.bmm(attn, v) # 5.Output [B, n_q, d_v]

        return attn, output


if __name__ == "__main__":
    n_q, n_k, n_v = 2, 4, 4
    d_q, d_k, d_v = 128, 128, 64
    batch = 128

    q = torch.randn(batch, n_q, d_q) # [128 2 128]
    k = torch.randn(batch, n_k, d_k) # [128 4 128]
    v = torch.randn(batch, n_v, d_v) # [128 4 64]
    mask = torch.zeros(batch, n_q, n_k).bool()

    attention = ScaledDotProductAttention(scale=np.power(d_k, 0.5))
    attn, output = attention(q, k, v, mask=mask)

    print(attn.shape)
    print(output.shape)
torch.Size([128, 2, 4])
torch.Size([128, 2, 64])

多头注意力​

import torch
import torch.nn as nn
import numpy as np

class MultiHeadAttention1(nn.Module):
    """ Multi-Head Attention """

    def __init__(self, n_head, d_k_, d_v_, d_k, d_v, d_o):
        super().__init__()

        self.n_head = n_head
        self.d_k = d_k
        self.d_v = d_v

        self.fc_q = nn.Linear(d_k_, n_head * d_k)
        self.fc_k = nn.Linear(d_k_, n_head * d_k)
        self.fc_v = nn.Linear(d_v_, n_head * d_v)

        self.attention = ScaledDotProductAttention(scale=np.power(d_k, 0.5))

        self.fc_o = nn.Linear(n_head * d_v, d_o)

    def forward(self, q, k, v, mask=None):

        n_head, d_q, d_k, d_v = self.n_head, self.d_k, self.d_k, self.d_v

        batch, n_q, d_q_ = q.size()
        batch, n_k, d_k_ = k.size()
        batch, n_v, d_v_ = v.size()

        q = self.fc_q(q) # 1.单头变多头
        k = self.fc_k(k)
        v = self.fc_v(v)
        q = q.view(batch, n_q, n_head, d_q).permute(2, 0, 1, 3).contiguous().view(-1, n_q, d_q)
        k = k.view(batch, n_k, n_head, d_k).permute(2, 0, 1, 3).contiguous().view(-1, n_k, d_k)
        v = v.view(batch, n_v, n_head, d_v).permute(2, 0, 1, 3).contiguous().view(-1, n_v, d_v)

        if mask is not None:
            mask = mask.repeat(n_head, 1, 1)
        attn, output = self.attention(q, k, v, mask=mask) # 2.当成单头注意力求输出

        output = output.view(n_head, batch, n_q, d_v).permute(1, 2, 0, 3).contiguous().view(batch, n_q, -1) # 3.Concat
        output = self.fc_o(output) # 4.仿射变换得到最终输出

        return attn, output


if __name__ == "__main__":
    n_q, n_k, n_v = 2, 4, 4
    d_q_, d_k_, d_v_ = 128, 128, 128
    batch = 32

    q = torch.randn(batch, n_q, d_q_)
    k = torch.randn(batch, n_k, d_k_)
    v = torch.randn(batch, n_v, d_v_)    
    mask = torch.zeros(batch, n_q, n_k).bool()

    mha = MultiHeadAttention1(n_head=4, d_k_=d_k_, d_v_=d_v_, d_k=32, d_v=32, d_o=128)
    attn, output = mha(q, k, v, mask=mask)

    print(attn.size())
    print(output.size())
torch.Size([128, 2, 4])
torch.Size([32, 2, 128])

多头注意力(另一个版本)​

import math
from typing import Optional, List
import torch
from torch import nn
from labml import tracker

class PrepareForMultiHeadAttention(nn.Module):
    def __init__(self, d_model: int, heads: int, d_k: int, bias: bool):
        super().__init__()
        self.linear = nn.Linear(d_model, heads * d_k, bias=bias)
        self.d_head = heads
        self.d_k = d_k

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        head_shape = x.shape[:-1]
        x = self.linear(x)
        x = x.view(*head_shape, self.d_head, self.d_k)
        return x

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model: int, heads: int, d_k: int, bias: bool):
        super().__init__()
        self.query = PrepareForMultiHeadAttention(d_model, heads, d_k, bias)
        self.key = PrepareForMultiHeadAttention(d_model, heads, d_k, bias)
        self.value = PrepareForMultiHeadAttention(d_model, heads, d_k, bias)
        self.softmax = nn.Softmax(dim=-1)
        self.out = nn.Linear(heads * d_k, d_model, bias=bias)
        self.scale = 1/math.sqrt(d_k)
        self.atten = None

    def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
        batch_size, seq_len, _ = q.shape
        q = self.query(q).permute(0, 2, 1, 3)
        k = self.key(k).permute(0, 2, 1, 3)
        v = self.value(v).permute(0, 2, 1, 3)
        
        attn = torch.matmul(q, k.transpose(-2, -1))
        attn = attn * self.scale
        if mask is not None:
            attn = attn.masked_fill(mask == 0, -1e9)
        attn = self.softmax(attn)
        self.atten = attn.detach()
        out = torch.matmul(attn, v)
        out = out.permute(0, 2, 1, 3).contiguous()
        out = out.view(batch_size, seq_len, -1)
        out = self.out(out)
        return attn, out

batch = 4
n_q, n_k, n_v = 2, 4, 4
d_q_, d_k_, d_v_ = 128, 128, 128
heads = 4

q = torch.randn(batch, n_q, d_q_)
k = torch.randn(batch, n_k, d_k_)
v = torch.randn(batch, n_v, d_v_)    
mask = torch.zeros(batch, heads, n_q, n_k).bool()

mha = MultiHeadAttention(d_model=128, heads=heads, d_k=32, bias=True)
attn, output = mha(q, k, v, mask=mask)

print(attn.size())
print(output.size())
torch.Size([4, 4, 2, 4])
torch.Size([4, 2, 128])

self attention​

class SelfAttention(nn.Module):
    """ Self-Attention """

    def __init__(self, n_head, d_k, d_v, d_x, d_o):
        super(SelfAttention, self).__init__()
        self.wq = nn.Parameter(torch.Tensor(d_x, d_k))
        self.wk = nn.Parameter(torch.Tensor(d_x, d_k))
        self.wv = nn.Parameter(torch.Tensor(d_x, d_v))

        self.mha = MultiHeadAttention1(n_head=n_head, d_k_=d_k, d_v_=d_v, d_k=d_k, d_v=d_v, d_o=d_o)

        self.init_parameters()

    def init_parameters(self):
        for param in self.parameters():
            stdv = 1. / np.power(param.size(-1), 0.5)
            param.data.uniform_(-stdv, stdv)

    def forward(self, x, mask=None):
        q = torch.matmul(x, self.wq)   
        k = torch.matmul(x, self.wk)
        v = torch.matmul(x, self.wv)

        attn, output = self.mha(q, k, v, mask=mask)

        return attn, output


if __name__ == "__main__":
    n_x = 4
    d_x = 80

    x = torch.randn(batch, n_x, d_x)
    mask = torch.zeros(batch, n_x, n_x).bool()

    selfattn = SelfAttention(n_head=8, d_k=128, d_v=64, d_x=80, d_o=80)
    attn, output = selfattn(x, mask=mask)

    print(attn.size())
    print(output.size())
torch.Size([256, 4, 4])
torch.Size([32, 4, 80])

beamsearch​

import torch
import torch.nn.functional as F

def beam_search(LM_prob, beam_size=3):
    batch,seqlen,vocab_size = LM_prob.shape
    #对LM_prob取对数
    log_LM_prob = LM_prob.log()
    #先选择第0个位置的最大beam_size个token，log_emb_prob与indices的shape为(batch,beam)
    log_beam_prob, indices = log_LM_prob[:,0,:].topk(beam_size,sorted = True)
    indices = indices.unsqueeze(-1)
    print(log_beam_prob.shape,indices.shape, log_LM_prob.shape)
    #对每个长度进行beam search
    for i in range(1,seqlen):
        #log_beam_prob (batch,beam,vocab_size),每个beam的可能产生的概率
        log_beam_prob = log_beam_prob.unsqueeze(-1) + log_LM_prob[:,i,:].unsqueeze(1).repeat(1,beam_size,1)
        #选择当前步概率最高的token
        log_beam_prob, index = log_beam_prob.view(batch,-1).topk(beam_size,sorted = True)
        #下面的计算：beam_id选出新beam来源于之前的哪个beam;index代表真实的token id
        #beam_id,index (batch,beam)
        beam_id = index//vocab_size
        index = index%vocab_size
        mid = torch.Tensor([])
        #对batch内每个样本循环，选出beam的同时拼接上新生成的token id
        for j,bid,idx in zip(range(batch),beam_id,index):
            x = torch.cat([indices[j][bid],idx.unsqueeze(-1)],-1)
            mid = torch.cat([mid,x.unsqueeze(0)],0)
        indices = mid
    return indices,log_beam_prob

if __name__=='__main__':
    # 建立一个语言模型 LM_prob (batch,seqlen,vocab_size)
    LM_prob = F.softmax(torch.randn([32,20,1000]),dim = -1)
    #最终返回每个候选，以及每个候选的log_prob，shape为(batch,beam_size,seqlen)
    indices,log_prob = beam_search(LM_prob,beam_size = 3)

    print(indices.shape,log_prob.shape)
    print(indices[0, :, :])

torch.Size([32, 3]) torch.Size([32, 3, 1]) torch.Size([32, 20, 1000])
torch.Size([32, 3, 20]) torch.Size([32, 3])
tensor([[114., 442., 752., 774., 195., 187., 712., 711., 599., 941.,  36., 146.,
         301., 290., 575.,  27., 628., 676.,   3., 672.],
        [114., 442., 752., 774., 195., 187., 712., 711., 599., 941.,  36., 146.,
         301., 290., 575.,  27., 628., 756.,   3., 672.],
        [114., 442., 752., 774., 195., 187., 712., 711., 599., 941.,  36., 146.,
         301., 290., 575.,  27., 547., 676.,   3., 672.]])

K-means Clustering​

import numpy as np
def kmeans(data, k, thresh=1, max_iterations=100):
  # 随机初始化k个中心点
  centers = data[np.random.choice(data.shape[0], k, replace=False)]

  for _ in range(max_iterations):
    # 计算每个样本到各个中心点的距离
    distances = np.linalg.norm(data[:, None] - centers, axis=2)

    # 根据距离最近的中心点将样本分配到对应的簇
    labels = np.argmin(distances, axis=1)

    # 更新中心点为每个簇的平均值
    new_centers = np.array([data[labels == i].mean(axis=0) for i in range(k)])

    # 判断中心点是否收敛，多种收敛条件可选
    # 条件1：中心点不再改变
    if np.all(centers == new_centers):
      break
    # 条件2：中心点的阈值小于某个阈值
    # center_change = np.linalg.norm(new_centers - centers)
    # if center_change < thresh:
    #     break
    centers = new_centers

  return labels, centers

# 生成一些随机数据作为示例输入
data = np.random.rand(100, 2)  # 100个样本，每个样本有两个特征

# 手动实现K均值算法
k = 3  # 聚类数为3
labels, centers = kmeans(data, k)

# 打印簇标签和聚类中心点
print("簇标签:", labels)
print("聚类中心点:", centers)
簇标签: [0 1 0 0 2 1 2 1 1 0 0 0 1 1 2 1 2 1 0 2 2 1 2 0 0 1 1 0 2 0 1 1 2 0 1 0 1
 0 0 1 2 2 1 2 2 2 2 2 1 1 0 0 0 2 0 0 1 2 2 1 2 1 1 1 0 1 1 0 1 0 0 1 2 2
 1 2 0 0 0 0 0 1 0 1 2 2 0 0 2 2 2 0 0 2 1 1 1 0 1 2]
聚类中心点: [[0.77716581 0.33047021]
 [0.50876926 0.77695796]
 [0.22867546 0.25998421]]

Layer Normalization​

import torch
from torch import nn
 
class LN(nn.Module):
    # 初始化
    def __init__(self, normalized_shape,  # 在哪个维度上做LN
                 eps:float = 1e-5, # 防止分母为0
                 elementwise_affine:bool = True):  # 是否使用可学习的缩放因子和偏移因子
        super(LN, self).__init__()
        # 需要对哪个维度的特征做LN, torch.size查看维度
        self.normalized_shape = normalized_shape  # [c,w*h]
        self.eps = eps
        self.elementwise_affine = elementwise_affine
        # 构造可训练的缩放因子和偏置
        if self.elementwise_affine:  
            self.gain = nn.Parameter(torch.ones(normalized_shape))  # [c,w*h]
            self.bias = nn.Parameter(torch.zeros(normalized_shape))  # [c,w*h]
 
    # 前向传播
    def forward(self, x: torch.Tensor): # [b,c,w*h]
        # 需要做LN的维度和输入特征图对应维度的shape相同
        assert self.normalized_shape == x.shape[-len(self.normalized_shape):]  # [-2:]
        # 需要做LN的维度索引
        dims = [-(i+1) for i in range(len(self.normalized_shape))]  # [b,c,w*h]维度上取[-1,-2]维度，即[c,w*h]
        # 计算特征图对应维度的均值和方差
        mean = x.mean(dim=dims, keepdims=True)  # [b,1,1]
        mean_x2 = (x**2).mean(dim=dims, keepdims=True)  # [b,1,1]
        var = mean_x2 - mean**2  # [b,c,1,1]
        x_norm = (x-mean) / torch.sqrt(var+self.eps)  # [b,c,w*h]
        # 线性变换
        if self.elementwise_affine:
            x_norm = self.gain * x_norm + self.bias  # [b,c,w*h]
        return x_norm
 

x = torch.linspace(0, 23, 24, dtype=torch.float32)  # 构造输入层
x = x.reshape([2,3,2*2])  # [b,c,w*h]
print(f"x[0] before LN: {x[0]}")
# 实例化
ln = LN(x.shape[1:])
# 前向传播
x = ln(x)
print(f"x[0] after LN: {x[0]}")
print(x.shape)
x[0] before LN: tensor([[ 0.,  1.,  2.,  3.],
        [ 4.,  5.,  6.,  7.],
        [ 8.,  9., 10., 11.]])
x[0] after LN: tensor([[-1.5933, -1.3036, -1.0139, -0.7242],
        [-0.4345, -0.1448,  0.1448,  0.4345],
        [ 0.7242,  1.0139,  1.3036,  1.5933]], grad_fn=<SelectBackward0>)
torch.Size([2, 3, 4])

Batch Normalization​

import numpy as np


class MyBN:
    def __init__(self, momentum=0.01, eps=1e-5, feat_dim=12):
        """
        初始化参数值
        :param momentum: 动量，用于计算每个batch均值和方差的滑动均值
        :param eps: 防止分母为0
        :param feat_dim: 特征维度
        """
        # 均值和方差的滑动均值
        self._running_mean = np.zeros(shape=(feat_dim, ))
        self._running_var = np.ones(shape=(feat_dim, ))
        # 更新self._running_xxx时的动量
        self._momentum = momentum
        # 防止分母计算为0
        self._eps = eps
        # 对应Batch Norm中需要更新的beta和gamma，采用pytorch文档中的初始化值
        self._beta = np.zeros(shape=(feat_dim, ))
        self._gamma = np.ones(shape=(feat_dim, ))
        self.training = True

    def batch_norm(self, x):
        """
        BN向传播
        :param x: 数据
        :return: BN输出
        """
        if self.training:
            x_mean = x.mean(axis=0)
            x_var = x.var(axis=0)
            # 对应running_mean的更新公式
            self._running_mean = (1-self._momentum)*x_mean + self._momentum*self._running_mean
            self._running_var = (1-self._momentum)*x_var + self._momentum*self._running_var
            # 对应论文中计算BN的公式
            x_hat = (x-x_mean)/np.sqrt(x_var+self._eps)
        else:
            x_hat = (x-self._running_mean)/np.sqrt(self._running_var+self._eps)
        return self._gamma*x_hat + self._beta
    def __call__(self, x):
        """
        调用方法，实现BN的前向传播
        :param x: 输入数据
        :return: BN输出
        """
        return self.batch_norm(x)


x = np.linspace(0, 23, 24*10, dtype=np.float32)  # 构造输入层
x = x.reshape([20,3*2*2])  # [b,c*w*h]
print(f"x[0] before BN: {x[:,0]}")
# 实例化
bn = MyBN()
# 前向传播
x = bn(x)
print(f"x[0] after BN: {x[:,0]}")
print(x.shape)
x[0] before BN: [ 0.         1.1548117  2.3096235  3.464435   4.619247   5.7740583
  6.92887    8.083682   9.238494  10.393306  11.548117  12.702929
 13.85774   15.012552  16.167364  17.322176  18.476988  19.6318
 20.786612  21.941423 ]
x[0] after BN: [-1.64750874 -1.47408676 -1.30066478 -1.1272428  -0.95382082 -0.78039891
 -0.60697693 -0.43355498 -0.26013297 -0.08671099  0.08671085  0.26013285
  0.43355483  0.60697681  0.78039879  0.95382077  1.1272428   1.30066478
  1.47408676  1.64750874]
(20, 12)

二维卷积​

import numpy as np
 
# 2D 卷积实现，带偏置
def conv2d(image, kernel, bias=0, stride=1, padding=0):
    # 获取输入图像和卷积核的大小
    img_h, img_w = image.shape
    kernel_h, kernel_w = kernel.shape
    
    # 计算输出特征图的大小
    output_h = (img_h - kernel_h + 2 * padding) // stride + 1
    output_w = (img_w - kernel_w + 2 * padding) // stride + 1
    
    # 对输入图像进行填充
    if padding > 0:
        image = np.pad(image, ((padding, padding), (padding, padding)), mode='constant', constant_values=0)
    
    # 初始化输出矩阵
    output = np.zeros((output_h, output_w))
    
    # 执行卷积操作
    for i in range(0, output_h):
        for j in range(0, output_w):
            # 选择输入图像中的对应区域
            region = image[i*stride:i*stride+kernel_h, j*stride:j*stride+kernel_w]
            # 进行逐点乘积并求和，再加上偏置
            output[i, j] = np.sum(region * kernel) + bias
    
    return output
 
# 示例输入图像和卷积核
image = np.array([
    [1, 1, 1, 0, 0],
    [0, 1, 1, 1, 0],
    [0, 0, 1, 1, 1],
    [1, 1, 0, 0, 0],
    [0, 1, 1, 1, 0]
])
 
kernel = np.array([
    [1, 0, 1],
    [0, 1, 0],
    [1, 0, 1]
])
 
# 定义偏置项
bias = 0.1
 
# 执行卷积
output = conv2d(image, kernel, bias=bias, stride=1, padding=0)
print("卷积结果带偏置：")
print(output)
卷积结果带偏置：
[[4.1 3.1 4.1]
 [2.1 4.1 2.1]
 [3.1 3.1 3.1]]

训练模型​

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

# 超参数设置
batch_size = 64
epochs = 10
learning_rate = 0.001
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# 数据预处理与加载
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 定义模型
class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(28*28, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = x.view(-1, 28*28)  # Flatten
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

model = SimpleNN().to(device)

# 损失函数与优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练循环
for epoch in range(epochs):
    model.train()  # 设置为训练模式
    running_loss = 0.0
    correct = 0
    total = 0

    for inputs, labels in train_loader:
        inputs, labels = inputs.to(device), labels.to(device)

        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        _, predicted = torch.max(outputs, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    epoch_loss = running_loss / len(train_loader)
    epoch_accuracy = correct / total * 100

    print(f"Epoch [{epoch+1}/{epochs}], Loss: {epoch_loss:.4f}, Accuracy: {epoch_accuracy:.2f}%")

    # 验证模型
    model.eval()  # 设置为评估模式
    correct = 0
    total = 0
    with torch.no_grad():
        for inputs, labels in test_loader:
            inputs, labels = inputs.to(device), labels.to(device)
            outputs = model(inputs)
            _, predicted = torch.max(outputs, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    accuracy = correct / total * 100
    print(f"Validation Accuracy: {accuracy:.2f}%")

# 保存模型
torch.save(model.state_dict(), 'model.pth')
print("Model saved!")
100%|██████████| 9.91M/9.91M [01:54<00:00, 86.9kB/s]
100%|██████████| 28.9k/28.9k [00:00<00:00, 137kB/s]
100%|██████████| 1.65M/1.65M [00:14<00:00, 113kB/s] 
100%|██████████| 4.54k/4.54k [00:00<00:00, 2.27MB/s]


Epoch [1/10], Loss: 0.3842, Accuracy: 88.89%
Validation Accuracy: 93.28%
Epoch [2/10], Loss: 0.1961, Accuracy: 94.20%
Validation Accuracy: 95.34%
Epoch [3/10], Loss: 0.1411, Accuracy: 95.78%
Validation Accuracy: 95.85%
Epoch [4/10], Loss: 0.1137, Accuracy: 96.63%
Validation Accuracy: 96.58%
Epoch [5/10], Loss: 0.0977, Accuracy: 97.01%
Validation Accuracy: 97.03%
Epoch [6/10], Loss: 0.0864, Accuracy: 97.33%
Validation Accuracy: 95.62%
Epoch [7/10], Loss: 0.0782, Accuracy: 97.55%
Validation Accuracy: 97.34%
Epoch [8/10], Loss: 0.0683, Accuracy: 97.83%
Validation Accuracy: 97.16%
Epoch [9/10], Loss: 0.0629, Accuracy: 97.95%
Validation Accuracy: 97.41%
Epoch [10/10], Loss: 0.0585, Accuracy: 98.15%
Validation Accuracy: 97.59%
Model saved!