from keras.models import Sequential
from keras.layers import Dense,Dropout,Activation
from keras.optimizers import SGD
from keras import utils

# generate dummy data
import numpy as np

x_train = np.random.random((1000,20))
y_train = utils.to_categorical(np.random.randint(10,size=(1000,1)),num_classes=10)
x_test = np.random.random((100,20))
y_test = utils.to_categorical(np.random.randint(10,size=(100,1)),num_classes=10)

model = Sequential()
# Dense(64) is a fully-connected layer with 64 hidden units
# in the first layer, you must specify the expected input data shape:
# here ,20-dimensional vectors
model.add(Dense(64,activation='relu',input_dim=20))
model.add(Dropout(0.5))
model.add(Dense(64,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10,activation='softmax'))

sgd = SGD(lr=0.01,decay=1e-6,momentum=0.9,nesterov=True)
model.compile(loss='categorical_crossentropy',
              optimizer=sgd,
              metrics=['accuracy'])
model.fit(x_train,y_train,
          epochs=20,
          batch_size=128)
score = model.evaluate(x_test,y_test,batch_size=128)
print(score)