How to return history of validation loss in Keras

Question

Using Anaconda Python 2.7 Windows 10.

I am training a language model using the Keras exmaple:

print(\'Build model...\')
model = Sequential()
model.ad         


        

Answer 1

For plotting the loss directly the following works:

model_ = model.fit(X, Y, epochs= ..., verbose=1 )
plt.plot(list(model_.history.values())[0],'k-o')

Answer 2

Those who got still error like me:

Convert model.fit_generator() to model.fit()

Answer 3

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

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:

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

According to their documentation:

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

Answer 4

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.

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.

Answer 5

It's been solved.

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

so instead of doing 4 iterations I now have

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

Now it returns the loss for each epoch run:

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

Answer 6

The following simple code works great for me:

    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

# 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