model.add(Dense(512, input_shape=(784,)))
model.add(Activation('relu'))
model.add(Dropout({{uniform(0, 1)}}))
model.add(Dense({{choice([256, 512, 1024])}}))
model.add(Activation('relu'))
model.add(Dropout({{uniform(0, 1)}}))
model.add(Dense(10))
model.add(Activation('softmax'))
rms = RMSprop()
model.compile(loss='categorical_crossentropy', optimizer=rms, metrics=["accuracy"])
model.fit(X_train, Y_train,
batch_size={{choice([64, 128])}},
nb_epoch=1,
verbose=2,
validation_data=(X_test, Y_test))
score, acc = model.evaluate(X_test, Y_test, verbose=0)
print('Test accuracy:', acc)
return {'loss': -acc, 'status': STATUS_OK, 'model': model.to_yaml(), 'weights': pickle.dumps(model.get_weights())}
# Create Spark context
conf = SparkConf().setAppName('Elephas_Hyperparameter_Optimization').setMaster('local[8]')
sc = SparkContext(conf=conf)
# Define hyper-parameter model and run optimization.
hyperparam_model = HyperParamModel(sc,num_workers=4)
best_model = hyperparam_model.minimize(model=model, data=data, max_evals=5)
print("Best model", best_model.to_yaml())
def test_hyper_param_model(spark_context):
hyperparam_model = HyperParamModel(spark_context)
hyperparam_model.minimize(model=model, data=data, max_evals=1)
model.add(Activation('softmax'))
rms = RMSprop()
model.compile(loss='categorical_crossentropy', optimizer=rms)
model.fit(X_train,
Y_train,
batch_size={{choice([64, 128])}},
nb_epoch=1,
show_accuracy=True,
verbose=2,
validation_data=(X_test, Y_test))
acc = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)
print('Test accuracy:', acc)
return {
'loss': -acc,
'status': STATUS_OK,
'model': model.to_yaml(),
'weights': pickle.dumps(model.get_weights())
}
# Create Spark context
conf = SparkConf().setAppName('Elephas_Hyperparameter_Optimization').setMaster(
'local[8]')
sc = SparkContext(conf=conf)
# Define hyper-parameter model and run optimization.
hyperparam_model = HyperParamModel(sc)
hyperparam_model.minimize(model=model, data=data, max_evals=5)
evl.output_info()
if i > 5 and evl.dev_metric < 0.4:
break
if i > 10 and evl.dev_metric < 0.5:
break
if i > 15 and evl.dev_metric < 0.6:
break
best_dev_kappa = evl.best_dev
best_test_kappa = evl.best_test
print('Test kappa:', best_dev_kappa)
return {
'loss': 1 - best_dev_kappa,
'status': STATUS_OK,
'model': model.to_yaml(),
'weights': pk.dumps(model.get_weights())
}
# Create Spark context
conf = SparkConf().setAppName('Elephas_Hyperparameter_Optimization').setMaster(
'local[8]')
sc = SparkContext(conf=conf)
# Define hyper-parameter model and run optimization.
hyperparam_model = HyperParamModel(sc, num_workers=2)
best_model = hyperparam_model.minimize(model=model, data=data, max_evals=100)
best_model_yaml = best_model.to_yaml()
print(best_model_yaml)