nn_keras.py

import pandas as pd
from keras.layers import Dense
from keras.models import Sequential
from keras.optimizers import RMSprop, Adadelta, Adam
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

dig = load_digits()
onehot_target = pd.get_dummies(dig.target)
x_train, x_val, y_train, y_val = train_test_split(dig.data, onehot_target, test_size=0.1, random_state=20)

model = Sequential()
model.add(Dense(128, input_dim=x_train.shape[1], activation='sigmoid'))
model.add(Dense(128, activation='sigmoid'))
model.add(Dense(10, activation='softmax'))

model.summary()

model.compile(optimizer=Adadelta(), loss='categorical_crossentropy', metrics=['categorical_accuracy'])
model.fit(x_train, y_train, epochs=50, batch_size=64)

scores = model.evaluate(x_val, y_val)
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))