train() # 将模型设置为训练模式 train_loss = 0.0 # 初始化训练损失为0.0 for data, labels in train_loader: # 遍历训练数据加载器,获取每个批次的数据和标签 optimizer.zero_grad() # 将优化器的梯度置零 outputs = model(data) # 将数据输入模型,得到预测输出 loss = criterion(outputs, labels....
le_model=keras.Sequential()le_model.add(layers.Conv2D(6,kernel_size=(5,5),strides=(1,1),activation='tanh',input_shape=(32,32,1),padding="valid"))le_model.add(layers.AveragePooling2D(pool_size=(2,2),strides=(2,2),padding='valid'))le_model.add(layers.Conv2D(16,kernel_size=(5,...
kernel_size=(5, 5), strides=(1, 1), activation='tanh', input_shape=(32,32,1), padding="valid"))le_model.add(layers.AveragePooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))le_model.add(layers.Conv2D(16, kernel_size=(5, 5), strides...
model.input)[0]grads/=(K.sqrt(K.mean(K.square(grads)))+1e-5)iterate=K.function([model.input],[loss,grads])input_img_data=np.random.random((1,size,size,3))*20+128.step=1.foriinrange(40):loss_value,grads_value=iterate([input_img_data])input_img_data+=grads_value*step...
(layer_name).output loss = K.mean(layer_output[:, :, :, filter_index]) grads = K.gradients(loss, model.input)[0] grads /= (K.sqrt(K.mean(K.square(grads))) + 1e-5) iterate = K.function([model.input], [loss, grads]) input_img_data = np.random.random((1, size, size,...
model.to(device) #将模型的所有内容放入cuda中 1. 2. 3. 2.训练中将数据迁入GPU,包括inputs和target #inputs, target = data后加入.to(device) def train(epoch): running_loss = 0.0 for batch_idx, data in enumerate(train_loader, 0): #在这里data返回输入:inputs、输出target ...
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False) 先来看: 可以看到transoforms.Compose()啥?
univariate_out = univariate_conv(univariate_data) 1. 2. 输出数据shape介绍 这里univariate_out的shape是torch.Size([5, 1, 48])。 以前几个数据的卷积操作为例,看下图解释其计算过程: (2)多元预测多元 输入数据shape介绍 举例:任意生成batch_size为5,特征数为2且长为50的多元时序数据。其中univariate_data...
train_X, _ = create_dataset(data,TIME_STEPS) _ , train_Y = create_dataset(capacity_data,TIME_STEPS) print(train_X.shape,train_Y.shape) m = attention_model() m.summary() print(train_X.shape,train_Y.shape) m = attention_model() ...
my_model.summary() my_model.fit(trainX, trainY) 2.6 进行预测 prediction_test=my_model.predict(testX) prediction_train=my_model.predict(trainX) 2.7 预测效果展示 plt.plot(df_for_training.index[window_size:,], original_train, color = 'red', label = '真实值') ...