I was trying to plot train and test learning curve in keras, however, the following code produces KeyError: 'val_acc error
.
The official document <https://keras.io/callbacks/>
states that in order to use 'val_acc'
I need to enable validation and accuracy monitoring which I dont understand and dont know how to use in my code.
Any help would be much appreciated. Thanks.
seed = 7
np.random.seed(seed)
dataframe = pandas.read_csv("iris.csv", header=None)
dataset = dataframe.values
X = dataset[:,0:4].astype(float)
Y = dataset[:,4]
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
dummy_y = np_utils.to_categorical(encoded_Y)
kfold = StratifiedKFold(y=Y, n_folds=10, shuffle=True, random_state=seed)
cvscores = []
for i, (train, test) in enumerate(kfold):
model = Sequential()
model.add(Dense(12, input_dim=4, init='uniform', activation='relu'))
model.add(Dense(3, init='uniform', activation='sigmoid'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
history=model.fit(X[train], dummy_y[train], nb_epoch=200, batch_size=5, verbose=0)
scores = model.evaluate(X[test], dummy_y[test], verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
cvscores.append(scores[1] * 100)
print( "%.2f%% (+/- %.2f%%)" % (np.mean(cvscores), np.std(cvscores)))
print(history.history.keys())
# summarize history for accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
Looks like in Keras + Tensorflow 2.0 val_acc
was renamed to val_accuracy