#
# 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 *
[docs]def mean_absolute_error(y_true, y_pred):
result = mean(abs(y_true - y_pred), axis=1)
return result
if __name__ == "__main__":
sc = init_nncontext("customloss example")
data_len = 1000
X_ = np.random.uniform(0, 1, (1000, 2))
Y_ = ((2 * X_).sum(1) + 0.4).reshape([data_len, 1])
model = Sequential()
model.add(Dense(1, input_shape=(2,)))
model.compile(optimizer=SGD(learningrate=1e-2),
loss=mean_absolute_error,
metrics=None)
model.fit(x=X_,
y=Y_,
batch_size=32,
nb_epoch=500,
validation_data=None,
distributed=False)
w = model.get_weights()
print(w)
pred = model.predict_local(X_)
print("finished...")
sc.stop()