如何返回Keras中验证丢失的历史记录

Using Anaconda Python 2.7 Windows 10.

使用Anaconda Python 2.7 Windows 10。

I am training a language model using the Keras exmaple:

我正在使用Keras exmaple训练语言模型:

print('Build model...')
model = Sequential()
model.add(GRU(512, return_sequences=True, input_shape=(maxlen, len(chars))))
model.add(Dropout(0.2))
model.add(GRU(512, return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(len(chars)))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

def sample(a, temperature=1.0):
    # helper function to sample an index from a probability array
    a = np.log(a) / temperature
    a = np.exp(a) / np.sum(np.exp(a))
    return np.argmax(np.random.multinomial(1, a, 1))


# train the model, output generated text after each iteration
for iteration in range(1, 3):
    print()
    print('-' * 50)
    print('Iteration', iteration)
    model.fit(X, y, batch_size=128, nb_epoch=1)
    start_index = random.randint(0, len(text) - maxlen - 1)

    for diversity in [0.2, 0.5, 1.0, 1.2]:
        print()
        print('----- diversity:', diversity)

        generated = ''
        sentence = text[start_index: start_index + maxlen]
        generated += sentence
        print('----- Generating with seed: "' + sentence + '"')
        sys.stdout.write(generated)

        for i in range(400):
            x = np.zeros((1, maxlen, len(chars)))
            for t, char in enumerate(sentence):
                x[0, t, char_indices[char]] = 1.

            preds = model.predict(x, verbose=0)[0]
            next_index = sample(preds, diversity)
            next_char = indices_char[next_index]

            generated += next_char
            sentence = sentence[1:] + next_char

            sys.stdout.write(next_char)
            sys.stdout.flush()
        print()

According to Keras documentation, the model.fit method returns a History callback, which has a history attribute containing the lists of successive losses and other metrics.

根据Keras文档,model.fit方法返回历史回调,其历史属性包含连续损失和其他指标的列表。

hist = model.fit(X, y, validation_split=0.2)
print(hist.history)

After training my model, if I run print(model.history) I get the error:

训练我的模型后,如果我运行print(model.history),我会收到错误:

 AttributeError: 'Sequential' object has no attribute 'history'

How do I return my model history after training my model with the above code?

使用上述代码训练模型后,如何返回模型历史记录?

UPDATE

The issue was that:

问题在于:

The following had to first be defined:

必须首先定义以下内容:

from keras.callbacks import History 
history = History()

The callbacks option had to be called

必须调用回调选项

model.fit(X_train, Y_train, nb_epoch=5, batch_size=16, callbacks=[history])

But now if I print

但现在如果我打印

print(history.History)

it returns

{}

even though I ran an iteration.

即使我运行迭代。

7 个解决方案

#1

It's been solved.

它已经解决了。

The losses only save to the History over the epochs. I was running iterations instead of using the Keras built in epochs option.

损失只能在历史上保存到历史。我正在运行迭代而不是使用内置时代选项中的Keras。

so instead of doing 4 iterations I now have

所以我现在没有进行4次迭代

model.fit(......, nb_epoch = 4)

Now it returns the loss for each epoch run:

现在它返回每个纪元运行的损失:

print(hist.history)
{'loss': [1.4358016599558268, 1.399221191623641, 1.381293383180471, h1.3758836857303727]}

#2

Just an example started from

只是一个例子开始

history = model.fit(X, Y, validation_split=0.33, nb_epoch=150, batch_size=10, verbose=0)

You can use

您可以使用

print(history.history.keys())

to list all data in history.

列出历史记录中的所有数据。

Then, you can print the history of validation loss like this:

然后,您可以打印验证丢失的历史记录,如下所示:

print(history.history['val_loss'])

#3

The dictionary with histories of "acc", "loss", etc. is available and saved in hist.history variable.

具有“acc”,“loss”等历史的字典可用并保存在hist.history变量中。

#4

Another option is CSVLogger: https://keras.io/callbacks/#csvlogger. It creates a csv file appending the result of each epoch. Even if you interrupt training, you get to see how it evolved.

另一个选项是CSVLogger:https://keras.io/callbacks/#csvlogger。它会创建一个附加每个纪元结果的csv文件。即使你中断训练,你也会看到它是如何进化的。

#5

The following simple code works great for me:

以下简单代码对我很有用:

    seqModel =model.fit(x_train, y_train,
          batch_size      = batch_size,
          epochs          = num_epochs,
          validation_data = (x_test, y_test),
          shuffle         = True,
          verbose=0, callbacks=[TQDMNotebookCallback()]) #for visualization

Make sure you assign the fit function to an output variable. Then you can access that variable very easily

确保将拟合函数分配给输出变量。然后,您可以非常轻松地访问该变量

# visualizing losses and accuracy
train_loss = seqModel.history['loss']
val_loss   = seqModel.history['val_loss']
train_acc  = seqModel.history['acc']
val_acc    = seqModel.history['val_acc']
xc         = range(num_epochs)

plt.figure()
plt.plot(xc, train_loss)
plt.plot(xc, val_loss)

Hope this helps. source: https://keras.io/getting-started/faq/#how-can-i-record-the-training-validation-loss-accuracy-at-each-epoch

希望这可以帮助。来源:https://keras.io/getting-started/faq/#how-can-i-record-the-training-validation-loss-accuracy-at-each-epoch

#6

I have also found that you can use verbose=2 to make keras print out the Losses:

我还发现你可以使用verbose = 2来使keras打印出损失:

history = model.fit(X, Y, validation_split=0.33, nb_epoch=150, batch_size=10, verbose=2)

And that would print nice lines like this:

这会打印出这样漂亮的线条:

Epoch 1/1
 - 5s - loss: 0.6046 - acc: 0.9999 - val_loss: 0.4403 - val_acc: 0.9999

According to their documentation:

根据他们的文件:

verbose: 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch.

#7

Actually, you can also do it with the iteration method. Because sometimes we might need to use the iteration method instead of the built-in epochs method to visualize the training results after each iteration.

实际上,您也可以使用迭代方法。因为有时我们可能需要使用迭代方法而不是内置的epochs方法来在每次迭代后可视化训练结果。

history = [] #Creating a empty list for holding the loss later
for iteration in range(1, 3):
    print()
    print('-' * 50)
    print('Iteration', iteration)
    result = model.fit(X, y, batch_size=128, nb_epoch=1) #Obtaining the loss after each training
    history.append(result.history['loss']) #Now append the loss after the training to the list.
    start_index = random.randint(0, len(text) - maxlen - 1)
print(history)

This way allows you to get the loss you want while maintaining your iteration method.

这种方式可以让您在维护迭代方法的同时获得所需的损失。

#1