#
# Copyright 2018 Analytics Zoo Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from zoo.common.nncontext import *
from zoo.pipeline.api.autograd import *
from zoo.pipeline.api.keras.layers import *
from zoo.pipeline.api.keras.models import *
from optparse import OptionParser
[docs]def mean_absolute_error(y_true, y_pred):
result = mean(abs(y_true - y_pred), axis=1)
return result
[docs]def add_one_func(x):
return x + 1.0
if __name__ == "__main__":
parser = OptionParser()
parser.add_option("--nb_epoch", dest="nb_epoch", default="500")
(options, args) = parser.parse_args(sys.argv)
sc = init_nncontext("custom example")
data_len = 1000
X_ = np.random.uniform(0, 1, (1000, 2))
Y_ = ((2 * X_).sum(1) + 0.4).reshape([data_len, 1])
a = Input(shape=(2,))
b = Dense(1)(a)
c = Lambda(function=add_one_func)(b)
model = Model(input=a, output=c)
model.compile(optimizer=SGD(learningrate=1e-2),
loss=mean_absolute_error)
model.set_tensorboard('./log', 'customized layer and loss')
model.fit(x=X_,
y=Y_,
batch_size=32,
nb_epoch=int(options.nb_epoch),
distributed=False)
model.save_graph_topology('./log')
w = model.get_weights()
print(w)
pred = model.predict_local(X_)
print("finished...")
sc.stop()