- 模型检查点将保存在 `args.output_path` 指定的路径中。"""net=LeNet5()net_loss=nn.SoftmaxCrossEntropyWithLogits(sparse=True,reduction="mean")net_opt=nn.Momentum(net.trainable_params(),0.01,0.9)model=Model(net,net_loss,net_opt,metrics={"Accuracy":nn.Accuracy()})config_checkpoint=Checkpoint...
reduction='mean')#定义优化器net_opt = nn.Momentum(net.trainable_params(), learning_rate=0.01, momentum=0.9)#设置模型保存参数#每125steps保存一次模型参数,最多保留15个文件config_ck = CheckpointConfig(save_checkpoint_steps=125, keep_checkpoint_max=15)#应用模型保存...
cell.weight.dtype))# 4 定义损失函数和优化器# 学习率 衰减 warmuplr = nn.dynamic_lr.cosine_decay_lr(config.lr_end, config.lr, config.epoch_size * step_size, step_size, config.warmup)# 衰减策略 Momentum 的 SGD 优化器decayed_params = [] no_decayed_params = []forparaminnet.trainable_...
net_opt = nn.Momentum(net.trainable_params(), lr, momentum) config_ck = CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=10) # save the network model and parameters for subsequence fine-tuning ckpoint = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck) # group laye...
net_opt = nn.Momentum(net.trainable_params(), learning_rate=0.01, momentum=0.9) # 设置模型保存参数 #每125steps保存一次模型参数,最多保留15个文件 config_ck = CheckpointConfig(save_checkpoint_steps=125, keep_checkpoint_max=15) # 应用模型保存参数 ...
net_opt = nn.Momentum(net.trainable_params(), 0.01, 0.9) model = Model(net, net_loss, net_opt, metrics={"Accuracy": nn.Accuracy()}) config_checkpoint = CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=10) checkpoint_callback = ModelCheckpoint(prefix="checkpoint_lenet", di...
params: 25 Trainable params: 25 Non-trainable params: 0 --- Input size (MB): 0.00 Forward/backward pass size (MB): 0.00 Params size (MB): 0.00 Estimated Total Size (MB): 0.00 --- {'total_params': 25, 'trainable_params': 25} In [ ] x = paddle...
Trainable params: 121,930 Non-trainable params: 0 设置compile model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy']) 训练模型并保存 model.save("1.h5")history = model.fit(train_images, train_labels, epochs=10,validation_data=(...
Trainable params:3,491,603 Non-trainable params:0 ___ deftrain_model(model, train_gen, valid_gen, epochs): train_steps_per_epoch = train_gen.n // train_gen.batch_size val_steps = valid_gen.n // valid_gen.batch_size earlystopping = tf.keras.c...
params: 11,490,314 Trainable params: 11,474,954 Non-trainable params: 15,360 --- Input size (MB): 0.57 Forward/backward pass size (MB): 122.56 Params size (MB): 43.83 Estimated Total Size (MB): 166.97 --- {'total_params': 11490314, 'trainable_...