from __future__ import print_function

import random

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout

import keras.optimizers
import runai.ga.keras

NUM_CLASSES = 10
BATCH_SIZE = 10
STEPS = 5

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, NUM_CLASSES)
y_test = keras.utils.to_categorical(y_test, NUM_CLASSES)

model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(NUM_CLASSES, activation='softmax'))

model.summary()

if random.choice([True, False]):
    optimizer = keras.optimizers.Adam()
    optimizer = runai.ga.keras.optimizers.Optimizer(optimizer, STEPS)
else:
    optimizer = runai.ga.keras.optimizers.Adam(STEPS)

model.compile(loss='categorical_crossentropy',
              optimizer=optimizer,
              metrics=['accuracy'])

history = model.fit(x_train, y_train,
                    batch_size=BATCH_SIZE // STEPS,
                    epochs=1,
                    verbose=1,
                    validation_data=(x_test, y_test))

score = model.evaluate(x_test, y_test, verbose=0)

print('Test loss:', score[0])
print('Test accuracy:', score[1])

from __future__ import print_function

import random

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout

import keras.optimizers
import runai.ga.keras

NUM_CLASSES = 10
BATCH_SIZE = 10
STEPS = 5

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, NUM_CLASSES)
y_test = keras.utils.to_categorical(y_test, NUM_CLASSES)

model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(NUM_CLASSES, activation='softmax'))

model.summary()

if random.choice([True, False]):
    optimizer = keras.optimizers.Adam()
    optimizer = runai.ga.keras.optimizers.Optimizer(optimizer, STEPS)
else:
    optimizer = runai.ga.keras.optimizers.Adam(STEPS)

model.compile(loss='categorical_crossentropy',
              optimizer=optimizer,
              metrics=['accuracy'])

history = model.fit(x_train, y_train,
                    batch_size=BATCH_SIZE // STEPS,
                    epochs=1,
                    verbose=1,
                    validation_data=(x_test, y_test))

score = model.evaluate(x_test, y_test, verbose=0)

print('Test loss:', score[0])
print('Test accuracy:', score[1])

# the data, split between train and test sets # convert class vectors to binary class matrices