尝试在 Jupiter 和 Google Colab 上训练模型。尝试实现梯度计算时出错

Question

我得到的错误是：

ZipFile requires mode 'r', 'w', 'x', or 'a'

或

ZipFile.__init__() got multiple values for argument 'mode'

或

类型错误：'ZipFile' 对象不可调用

我的代码：

@tf.function
def train_step(batch):
    # Record all of our operations 
    with tf.GradientTape() as tape:     
        # Get anchor and positive/negative image
        X = batch[:2]
        # Get label
        y = batch[2]
        
        # Forward pass 
        yhat = siamese_model(X, training=True)
        # Calculate loss
        loss = binary_cross_loss(y, yhat)
    print(loss)
        
    # Calculate gradients
    grad = tape.gradient(loss, [siamese_model.trainable_variables])
    
    # Calculate updated weights and apply to siamese model
    opt.apply_gradients(zipfile.ZipFile(grad, [siamese_model.trainable_variables], mode 
    = "w"))
    
    # Return loss
    return loss

def train(data, EPOCHS):
# Loop through epochs
for epoch in range(1, EPOCHS+1):
    print('\n Epoch {}/{}'.format(epoch, EPOCHS))
    progbar = tf.keras.utils.Progbar(len(data))
    
    # Creating a metric object 
    r = Recall()
    p = Precision()
    
    # Loop through each batch
    for idx, batch in enumerate(data):
        # Run train step here
        loss = train_step(batch)
        yhat = siamese_model.predict(batch[:2])
        r.update_state(batch[2], yhat)
        p.update_state(batch[2], yhat) 
        progbar.update(idx+1)
    print(loss.numpy(), r.result().numpy(), p.result().numpy())
    
    # Save checkpoints
    if epoch % 10 == 0: 
        checkpoint.save(file_prefix=checkpoint_prefix)

EPOCHS = 50
train(train_data, EPOCHS)

错误表明问题出在 train 调用 train_step(batch) 函数时。

我也尝试过不使用 mode = "w"，当括号打开时仅使用 'zip' 而没有 siamese_model.trainable_variables 的括号，我已经尝试过 import zipfile 或 import zip file from ZipFile 但没有区别。

Answer 1

您似乎混淆了 built-in zip function with the zipfile 模块。第一个是组合多个可迭代对象，第二个是创建和读取压缩档案。

您可能想使用这个：

    # Calculate updated weights and apply to siamese model
    opt.apply_gradients(zip(grad, [siamese_model.trainable_variables]))

尝试在 Jupiter 和 Google Colab 上训练模型。尝试实现梯度计算时出错

Trying to train a model on Jupiter and Google Colab. getting errors when trying to implement gradient calculations

python

gradient-descent

tensorflow

siamese-network