# Sequential_Model
from keras.models import Sequential
from keras.layers import Dense,Activation,Dropout
from keras.optimizers import SGD
import numpy as np
import keras

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

# multi-classify
model = Sequential()
model.add(Dense(units=64, input_dim=20, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(units=64, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(units=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, batch_size=128, epochs=20)
score = model.evaluate(x_test, y_test, batch_size=128)
print(score)



