數據集為cora數據集,cora數據集由機器學習論文組成,共以下7類:
由cora.content和cora.cities文件構成。共2708個樣本,每個樣本的特征維度是1433。
下載地址:https://linqs.soe.ucsc.edu/data
cora.content:
每一行由論文id+特征向量+標簽構成。
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cora.cities:
引用關系:被引論文編號以及引論文編號
35 103335 10348235 10351535 105067935 110396035 110398535 110919935 1112911......
讀取數據集:
import numpy as npimport scipy.sparse as spimport torchfrom sklearn.preprocessing import LabelBinarizerdef normalize_adj(adjacency): adjacency += sp.eye(adjacency.shape[0]) degree = np.array(adjacency.sum(1)) d_hat = sp.diags(np.power(degree, -0.5).flatten()) return d_hat.dot(adjacency).dot(d_hat).tocoo()def normalize_features(features): return features / features.sum(1)def load_data(path="/content/drive/My Drive/nlpdata/cora/", dataset="cora"): """Load citation network dataset (cora only for now)""" print('Loading {} dataset...'.format(dataset)) idx_features_labels = np.genfromtxt("{}{}.content".format(path,dataset), dtype=np.dtype(str)) features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32) encode_onehot = LabelBinarizer() labels = encode_onehot.fit_transform(idx_features_labels[:, -1]) # build graph idx = np.array(idx_features_labels[:, 0], dtype=np.int32) idx_map = {j: i for i, j in enumerate(idx)} edges_unordered = np.genfromtxt("{}{}.cites".format(path, dataset), dtype=np.int32) edges = np.array(list(map(idx_map.get, edges_unordered.flatten())), dtype=np.int32).reshape(edges_unordered.shape) adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), shape=(labels.shape[0], labels.shape[0]), dtype=np.float32) features = normalize_features(features) adj = normalize_adj(adj) idx_train = range(140) idx_val = range(200, 500) idx_test = range(500, 1500) features = torch.FloatTensor(np.array(features)) labels = torch.LongTensor(np.where(labels)[1]) num_nodes = features.shape[0] train_mask = np.zeros(num_nodes, dtype=np.bool) val_mask = np.zeros(num_nodes, dtype=np.bool) test_mask = np.zeros(num_nodes, dtype=np.bool) train_mask[idx_train] = True val_mask[idx_val] = True test_mask[idx_test] = True return adj, features, labels, train_mask, val_mask, test_mask構建網絡:
import numpy as npimport scipy.sparse as spimport torchimport torch.nn as nnimport torch.nn.functional as Fimport torch.nn.init as initimport torch.optim as optimimport matplotlib.pyplot as pltfrom load_cora import *import syssys.path.append("/content/drive/My Drive/nlpdata/cora/")class GraphConvolution(nn.Module): def __init__(self, input_dim, output_dim, use_bias=True): """圖卷積:L*X*\theta Args: ---------- input_dim: int 節點輸入特征的維度 output_dim: int 輸出特征維度 use_bias : bool, optional 是否使用偏置 """ super(GraphConvolution, self).__init__() self.input_dim = input_dim self.output_dim = output_dim self.use_bias = use_bias self.weight = nn.Parameter(torch.Tensor(input_dim, output_dim)) if self.use_bias: self.bias = nn.Parameter(torch.Tensor(output_dim)) else: self.register_parameter('bias', None) self.reset_parameters() def reset_parameters(self): init.kaiming_uniform_(self.weight) if self.use_bias: init.zeros_(self.bias) def forward(self, adjacency, input_feature): """鄰接矩陣是稀疏矩陣,因此在計算時使用稀疏矩陣乘法 Args: ------- adjacency: torch.sparse.FloatTensor 鄰接矩陣 input_feature: torch.Tensor 輸入特征 """ device = "cuda" if torch.cuda.is_available() else "cpu" support = torch.mm(input_feature, self.weight.to(device)) output = torch.sparse.mm(adjacency, support) if self.use_bias: output += self.bias.to(device) return output def __repr__(self): return self.__class__.__name__ + ' (' + str(self.in_features) + ' -> ' + str(self.out_features) + ')'# ## 模型定義class GcnNet(nn.Module): """ 定義一個包含兩層GraphConvolution的模型 """ def __init__(self, input_dim=1433): super(GcnNet, self).__init__() self.gcn1 = GraphConvolution(input_dim, 16) self.gcn2 = GraphConvolution(16, 7) def forward(self, adjacency, feature): h = F.relu(self.gcn1(adjacency, feature)) logits = self.gcn2(adjacency, h) return logits進行訓練和測試:
# ## 模型訓練# 超參數定義learning_rate = 0.1weight_decay = 5e-4epochs = 200# 模型定義:Model, Loss, Optimizerdevice = "cuda" if torch.cuda.is_available() else "cpu"model = GcnNet().to(device)criterion = nn.CrossEntropyLoss().to(device)optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)adjacency, features, labels, train_mask, val_mask, test_mask= load_data()tensor_x = features.to(device)tensor_y = labels.to(device)tensor_train_mask = torch.from_numpy(train_mask).to(device)tensor_val_mask = torch.from_numpy(val_mask).to(device)tensor_test_mask = torch.from_numpy(test_mask).to(device)indices = torch.from_numpy(np.asarray([adjacency.row, adjacency.col]).astype('int64')).long()values = torch.from_numpy(adjacency.data.astype(np.float32))tensor_adjacency = torch.sparse.FloatTensor(indices, values, (2708, 2708)).to(device)# 訓練主體函數def train(): loss_history = [] val_acc_history = [] model.train() train_y = tensor_y[tensor_train_mask] for epoch in range(epochs): logits = model(tensor_adjacency, tensor_x) # 前向傳播 train_mask_logits = logits[tensor_train_mask] # 只選擇訓練節點進行監督 loss = criterion(train_mask_logits, train_y) # 計算損失值 optimizer.zero_grad() loss.backward() # 反向傳播計算參數的梯度 optimizer.step() # 使用優化方法進行梯度更新 train_acc, _, _ = test(tensor_train_mask) # 計算當前模型訓練集上的準確率 val_acc, _, _ = test(tensor_val_mask) # 計算當前模型在驗證集上的準確率 # 記錄訓練過程中損失值和準確率的變化,用于畫圖 loss_history.append(loss.item()) val_acc_history.append(val_acc.item()) print("Epoch {:03d}: Loss {:.4f}, TrainAcc {:.4}, ValAcc {:.4f}".format( epoch, loss.item(), train_acc.item(), val_acc.item())) return loss_history, val_acc_history# 測試函數def test(mask): model.eval() with torch.no_grad(): logits = model(tensor_adjacency, tensor_x) test_mask_logits = logits[mask] predict_y = test_mask_logits.max(1)[1] accuarcy = torch.eq(predict_y, tensor_y[mask]).float().mean() return accuarcy, test_mask_logits.cpu().numpy(), tensor_y[mask].cpu().numpy()if __name__ == "__main__": train() test_accuracy, _, _ = test(tensor_test_mask) print("測試準確率是:{:.4f}".format(test_accuracy))結果:
Loading cora dataset...Epoch 000: Loss 1.9681, TrainAcc 0.3286, ValAcc 0.3467Epoch 001: Loss 1.7307, TrainAcc 0.4786, ValAcc 0.4033Epoch 002: Loss 1.5521, TrainAcc 0.5214, ValAcc 0.4033Epoch 003: Loss 1.3685, TrainAcc 0.6143, ValAcc 0.5100Epoch 004: Loss 1.1594, TrainAcc 0.75, ValAcc 0.5767Epoch 005: Loss 0.9785, TrainAcc 0.7857, ValAcc 0.5900Epoch 006: Loss 0.8226, TrainAcc 0.8286, ValAcc 0.5867Epoch 007: Loss 0.6849, TrainAcc 0.8929, ValAcc 0.6200Epoch 008: Loss 0.5448, TrainAcc 0.9429, ValAcc 0.6433Epoch 009: Loss 0.4152, TrainAcc 0.9429, ValAcc 0.6667Epoch 010: Loss 0.3221, TrainAcc 0.9857, ValAcc 0.6767Epoch 011: Loss 0.2547, TrainAcc 1.0, ValAcc 0.7033Epoch 012: Loss 0.1979, TrainAcc 1.0, ValAcc 0.7167Epoch 013: Loss 0.1536, TrainAcc 1.0, ValAcc 0.7000Epoch 014: Loss 0.1276, TrainAcc 1.0, ValAcc 0.6700Epoch 015: Loss 0.1122, TrainAcc 1.0, ValAcc 0.6867Epoch 016: Loss 0.0979, TrainAcc 1.0, ValAcc 0.6800Epoch 017: Loss 0.0876, TrainAcc 1.0, ValAcc 0.6700Epoch 018: Loss 0.0821, TrainAcc 1.0, ValAcc 0.6667Epoch 019: Loss 0.0799, TrainAcc 1.0, ValAcc 0.6800Epoch 020: Loss 0.0804, TrainAcc 1.0, ValAcc 0.6933Epoch 021: Loss 0.0852, TrainAcc 1.0, ValAcc 0.6833Epoch 022: Loss 0.0904, TrainAcc 1.0, ValAcc 0.6700Epoch 023: Loss 0.0914, TrainAcc 1.0, ValAcc 0.6700Epoch 024: Loss 0.0926, TrainAcc 1.0, ValAcc 0.6400Epoch 025: Loss 0.0953, TrainAcc 1.0, ValAcc 0.6300Epoch 026: Loss 0.0931, TrainAcc 1.0, ValAcc 0.6467Epoch 027: Loss 0.0880, TrainAcc 1.0, ValAcc 0.6600Epoch 028: Loss 0.0851, TrainAcc 1.0, ValAcc 0.6567Epoch 029: Loss 0.0814, TrainAcc 1.0, ValAcc 0.6600Epoch 030: Loss 0.0756, TrainAcc 1.0, ValAcc 0.6433Epoch 031: Loss 0.0709, TrainAcc 1.0, ValAcc 0.6567Epoch 032: Loss 0.0682, TrainAcc 1.0, ValAcc 0.6467Epoch 033: Loss 0.0656, TrainAcc 1.0, ValAcc 0.6700Epoch 034: Loss 0.0628, TrainAcc 1.0, ValAcc 0.6633Epoch 035: Loss 0.0618, TrainAcc 1.0, ValAcc 0.6833Epoch 036: Loss 0.0617, TrainAcc 1.0, ValAcc 0.6700Epoch 037: Loss 0.0615, TrainAcc 1.0, ValAcc 0.6667Epoch 038: Loss 0.0615, TrainAcc 1.0, ValAcc 0.6600Epoch 039: Loss 0.0616, TrainAcc 1.0, ValAcc 0.6733Epoch 040: Loss 0.0613, TrainAcc 1.0, ValAcc 0.6800Epoch 041: Loss 0.0610, TrainAcc 1.0, ValAcc 0.6867Epoch 042: Loss 0.0603, TrainAcc 1.0, ValAcc 0.6800Epoch 043: Loss 0.0598, TrainAcc 1.0, ValAcc 0.6667Epoch 044: Loss 0.0592, TrainAcc 1.0, ValAcc 0.6833Epoch 045: Loss 0.0584, TrainAcc 1.0, ValAcc 0.6833Epoch 046: Loss 0.0575, TrainAcc 1.0, ValAcc 0.6967Epoch 047: Loss 0.0570, TrainAcc 1.0, ValAcc 0.6900Epoch 048: Loss 0.0565, TrainAcc 1.0, ValAcc 0.6933Epoch 049: Loss 0.0560, TrainAcc 1.0, ValAcc 0.6867Epoch 050: Loss 0.0559, TrainAcc 1.0, ValAcc 0.6900Epoch 051: Loss 0.0557, TrainAcc 1.0, ValAcc 0.6900Epoch 052: Loss 0.0555, TrainAcc 1.0, ValAcc 0.6967Epoch 053: Loss 0.0554, TrainAcc 1.0, ValAcc 0.6867Epoch 054: Loss 0.0552, TrainAcc 1.0, ValAcc 0.6867Epoch 055: Loss 0.0550, TrainAcc 1.0, ValAcc 0.6933Epoch 056: Loss 0.0549, TrainAcc 1.0, ValAcc 0.7000Epoch 057: Loss 0.0548, TrainAcc 1.0, ValAcc 0.7000Epoch 058: Loss 0.0547, TrainAcc 1.0, ValAcc 0.7067Epoch 059: Loss 0.0546, TrainAcc 1.0, ValAcc 0.7000Epoch 060: Loss 0.0545, TrainAcc 1.0, ValAcc 0.6967Epoch 061: Loss 0.0545, TrainAcc 1.0, ValAcc 0.6967Epoch 062: Loss 0.0544, TrainAcc 1.0, ValAcc 0.7067Epoch 063: Loss 0.0544, TrainAcc 1.0, ValAcc 0.7067Epoch 064: Loss 0.0543, TrainAcc 1.0, ValAcc 0.7033Epoch 065: Loss 0.0542, TrainAcc 1.0, ValAcc 0.7000Epoch 066: Loss 0.0542, TrainAcc 1.0, ValAcc 0.7000Epoch 067: Loss 0.0542, TrainAcc 1.0, ValAcc 0.7033Epoch 068: Loss 0.0542, TrainAcc 1.0, ValAcc 0.7067Epoch 069: Loss 0.0542, TrainAcc 1.0, ValAcc 0.7033Epoch 070: Loss 0.0543, TrainAcc 1.0, ValAcc 0.7033Epoch 071: Loss 0.0543, TrainAcc 1.0, ValAcc 0.7000Epoch 072: Loss 0.0543, TrainAcc 1.0, ValAcc 0.7033Epoch 073: Loss 0.0544, TrainAcc 1.0, ValAcc 0.7067Epoch 074: Loss 0.0544, TrainAcc 1.0, ValAcc 0.7067Epoch 075: Loss 0.0545, TrainAcc 1.0, ValAcc 0.7133Epoch 076: Loss 0.0545, TrainAcc 1.0, ValAcc 0.7100Epoch 077: Loss 0.0546, TrainAcc 1.0, ValAcc 0.7133Epoch 078: Loss 0.0546, TrainAcc 1.0, ValAcc 0.7067Epoch 079: Loss 0.0547, TrainAcc 1.0, ValAcc 0.7133Epoch 080: Loss 0.0547, TrainAcc 1.0, ValAcc 0.7033Epoch 081: Loss 0.0548, TrainAcc 1.0, ValAcc 0.7100Epoch 082: Loss 0.0549, TrainAcc 1.0, ValAcc 0.7067Epoch 083: Loss 0.0549, TrainAcc 1.0, ValAcc 0.7133Epoch 084: Loss 0.0549, TrainAcc 1.0, ValAcc 0.7067Epoch 085: Loss 0.0550, TrainAcc 1.0, ValAcc 0.7100Epoch 086: Loss 0.0550, TrainAcc 1.0, ValAcc 0.7033Epoch 087: Loss 0.0551, TrainAcc 1.0, ValAcc 0.7133Epoch 088: Loss 0.0551, TrainAcc 1.0, ValAcc 0.7067Epoch 089: Loss 0.0552, TrainAcc 1.0, ValAcc 0.7100Epoch 090: Loss 0.0553, TrainAcc 1.0, ValAcc 0.6967Epoch 091: Loss 0.0553, TrainAcc 1.0, ValAcc 0.7067Epoch 092: Loss 0.0554, TrainAcc 1.0, ValAcc 0.6900Epoch 093: Loss 0.0556, TrainAcc 1.0, ValAcc 0.7100Epoch 094: Loss 0.0557, TrainAcc 1.0, ValAcc 0.6833Epoch 095: Loss 0.0561, TrainAcc 1.0, ValAcc 0.7033Epoch 096: Loss 0.0558, TrainAcc 1.0, ValAcc 0.6833Epoch 097: Loss 0.0557, TrainAcc 1.0, ValAcc 0.7100Epoch 098: Loss 0.0547, TrainAcc 1.0, ValAcc 0.7133Epoch 099: Loss 0.0546, TrainAcc 1.0, ValAcc 0.6900Epoch 100: Loss 0.0555, TrainAcc 1.0, ValAcc 0.7033Epoch 101: Loss 0.0561, TrainAcc 1.0, ValAcc 0.6700Epoch 102: Loss 0.0579, TrainAcc 1.0, ValAcc 0.6967Epoch 103: Loss 0.0577, TrainAcc 1.0, ValAcc 0.6633Epoch 104: Loss 0.0600, TrainAcc 1.0, ValAcc 0.7000Epoch 105: Loss 0.0550, TrainAcc 1.0, ValAcc 0.6967Epoch 106: Loss 0.0540, TrainAcc 1.0, ValAcc 0.6767Epoch 107: Loss 0.0555, TrainAcc 1.0, ValAcc 0.6967Epoch 108: Loss 0.0528, TrainAcc 1.0, ValAcc 0.7000Epoch 109: Loss 0.0571, TrainAcc 1.0, ValAcc 0.6700Epoch 110: Loss 0.0643, TrainAcc 1.0, ValAcc 0.6933Epoch 111: Loss 0.0583, TrainAcc 1.0, ValAcc 0.6800Epoch 112: Loss 0.0533, TrainAcc 1.0, ValAcc 0.6700Epoch 113: Loss 0.0552, TrainAcc 1.0, ValAcc 0.7067Epoch 114: Loss 0.0534, TrainAcc 1.0, ValAcc 0.6967Epoch 115: Loss 0.0555, TrainAcc 1.0, ValAcc 0.6833Epoch 116: Loss 0.0555, TrainAcc 1.0, ValAcc 0.6800Epoch 117: Loss 0.0559, TrainAcc 1.0, ValAcc 0.6933Epoch 118: Loss 0.0601, TrainAcc 1.0, ValAcc 0.6700Epoch 119: Loss 0.0707, TrainAcc 1.0, ValAcc 0.6667Epoch 120: Loss 0.0670, TrainAcc 1.0, ValAcc 0.6500Epoch 121: Loss 0.0574, TrainAcc 1.0, ValAcc 0.6467Epoch 122: Loss 0.0589, TrainAcc 1.0, ValAcc 0.7033Epoch 123: Loss 0.0493, TrainAcc 1.0, ValAcc 0.6800Epoch 124: Loss 0.0591, TrainAcc 1.0, ValAcc 0.6900Epoch 125: Loss 0.0482, TrainAcc 1.0, ValAcc 0.6600Epoch 126: Loss 0.0562, TrainAcc 1.0, ValAcc 0.6667Epoch 127: Loss 0.0538, TrainAcc 1.0, ValAcc 0.6900Epoch 128: Loss 0.0579, TrainAcc 1.0, ValAcc 0.6867Epoch 129: Loss 0.0557, TrainAcc 1.0, ValAcc 0.6833Epoch 130: Loss 0.0615, TrainAcc 1.0, ValAcc 0.6733Epoch 131: Loss 0.0570, TrainAcc 1.0, ValAcc 0.6667Epoch 132: Loss 0.0612, TrainAcc 1.0, ValAcc 0.6700Epoch 133: Loss 0.0669, TrainAcc 1.0, ValAcc 0.6967Epoch 134: Loss 0.0544, TrainAcc 1.0, ValAcc 0.6767Epoch 135: Loss 0.0605, TrainAcc 1.0, ValAcc 0.6567Epoch 136: Loss 0.0546, TrainAcc 1.0, ValAcc 0.6567Epoch 137: Loss 0.0586, TrainAcc 1.0, ValAcc 0.7033Epoch 138: Loss 0.0501, TrainAcc 1.0, ValAcc 0.6833Epoch 139: Loss 0.0600, TrainAcc 1.0, ValAcc 0.7067Epoch 140: Loss 0.0513, TrainAcc 1.0, ValAcc 0.6633Epoch 141: Loss 0.0587, TrainAcc 1.0, ValAcc 0.6733Epoch 142: Loss 0.0556, TrainAcc 1.0, ValAcc 0.6833Epoch 143: Loss 0.0586, TrainAcc 1.0, ValAcc 0.6967Epoch 144: Loss 0.0565, TrainAcc 1.0, ValAcc 0.6900Epoch 145: Loss 0.0586, TrainAcc 1.0, ValAcc 0.6833Epoch 146: Loss 0.0559, TrainAcc 1.0, ValAcc 0.6767Epoch 147: Loss 0.0589, TrainAcc 1.0, ValAcc 0.6800Epoch 148: Loss 0.0562, TrainAcc 1.0, ValAcc 0.6900Epoch 149: Loss 0.0560, TrainAcc 1.0, ValAcc 0.6933Epoch 150: Loss 0.0565, TrainAcc 1.0, ValAcc 0.6833Epoch 151: Loss 0.0547, TrainAcc 1.0, ValAcc 0.6767Epoch 152: Loss 0.0559, TrainAcc 1.0, ValAcc 0.6967Epoch 153: Loss 0.0549, TrainAcc 1.0, ValAcc 0.6933Epoch 154: Loss 0.0567, TrainAcc 1.0, ValAcc 0.7000Epoch 155: Loss 0.0556, TrainAcc 1.0, ValAcc 0.6867Epoch 156: Loss 0.0568, TrainAcc 1.0, ValAcc 0.6967Epoch 157: Loss 0.0558, TrainAcc 1.0, ValAcc 0.6967Epoch 158: Loss 0.0568, TrainAcc 1.0, ValAcc 0.6867Epoch 159: Loss 0.0560, TrainAcc 1.0, ValAcc 0.6867Epoch 160: Loss 0.0563, TrainAcc 1.0, ValAcc 0.6933Epoch 161: Loss 0.0562, TrainAcc 1.0, ValAcc 0.6967Epoch 162: Loss 0.0559, TrainAcc 1.0, ValAcc 0.6833Epoch 163: Loss 0.0556, TrainAcc 1.0, ValAcc 0.7000Epoch 164: Loss 0.0554, TrainAcc 1.0, ValAcc 0.7033Epoch 165: Loss 0.0556, TrainAcc 1.0, ValAcc 0.6967Epoch 166: Loss 0.0553, TrainAcc 1.0, ValAcc 0.6900Epoch 167: Loss 0.0555, TrainAcc 1.0, ValAcc 0.7033Epoch 168: Loss 0.0554, TrainAcc 1.0, ValAcc 0.6967Epoch 169: Loss 0.0560, TrainAcc 1.0, ValAcc 0.6833Epoch 170: Loss 0.0558, TrainAcc 1.0, ValAcc 0.6900Epoch 171: Loss 0.0561, TrainAcc 1.0, ValAcc 0.7033Epoch 172: Loss 0.0560, TrainAcc 1.0, ValAcc 0.6967Epoch 173: Loss 0.0559, TrainAcc 1.0, ValAcc 0.6833Epoch 174: Loss 0.0557, TrainAcc 1.0, ValAcc 0.6967Epoch 175: Loss 0.0554, TrainAcc 1.0, ValAcc 0.7033Epoch 176: Loss 0.0554, TrainAcc 1.0, ValAcc 0.7033Epoch 177: Loss 0.0551, TrainAcc 1.0, ValAcc 0.6933Epoch 178: Loss 0.0553, TrainAcc 1.0, ValAcc 0.6967Epoch 179: Loss 0.0553, TrainAcc 1.0, ValAcc 0.7000Epoch 180: Loss 0.0555, TrainAcc 1.0, ValAcc 0.6900Epoch 181: Loss 0.0556, TrainAcc 1.0, ValAcc 0.7000Epoch 182: Loss 0.0557, TrainAcc 1.0, ValAcc 0.7033Epoch 183: Loss 0.0558, TrainAcc 1.0, ValAcc 0.6933Epoch 184: Loss 0.0556, TrainAcc 1.0, ValAcc 0.6867Epoch 185: Loss 0.0555, TrainAcc 1.0, ValAcc 0.7033Epoch 186: Loss 0.0554, TrainAcc 1.0, ValAcc 0.7000Epoch 187: Loss 0.0552, TrainAcc 1.0, ValAcc 0.6933Epoch 188: Loss 0.0552, TrainAcc 1.0, ValAcc 0.6933Epoch 189: Loss 0.0553, TrainAcc 1.0, ValAcc 0.7000Epoch 190: Loss 0.0554, TrainAcc 1.0, ValAcc 0.7000Epoch 191: Loss 0.0554, TrainAcc 1.0, ValAcc 0.6933Epoch 192: Loss 0.0555, TrainAcc 1.0, ValAcc 0.7000Epoch 193: Loss 0.0555, TrainAcc 1.0, ValAcc 0.7000Epoch 194: Loss 0.0555, TrainAcc 1.0, ValAcc 0.6933Epoch 195: Loss 0.0554, TrainAcc 1.0, ValAcc 0.6933Epoch 196: Loss 0.0553, TrainAcc 1.0, ValAcc 0.7000Epoch 197: Loss 0.0553, TrainAcc 1.0, ValAcc 0.7033Epoch 198: Loss 0.0553, TrainAcc 1.0, ValAcc 0.6933Epoch 199: Loss 0.0553, TrainAcc 1.0, ValAcc 0.7033測試準確率是:0.6480
參考:
https://blog.csdn.net/weixin_39373480/article/details/88742200
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