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.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.

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:

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

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

UPDATE

The issue was that:

The following had to first be defined:

from keras.callbacks import History  history = History() 

The callbacks option had to be called

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

But now if I print

print(history.History) 

it returns

{} 

even though I ran an iteration.

Answer 1

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, 1.3758836857303727]} 

Answer 2

Just an example started from

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

You can use

print(history.history.keys()) 

to list all data in history.

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

print(history.history['val_loss'])