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sgd_mse.py
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# ==============================================================================
# Copyright 2014 Intel Corporation
#
# 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.
# ==============================================================================
# daal4py SGD (Stochastic Gradient Descent) example for shared memory systems
# using Mean Squared Error objective function
from pathlib import Path
import numpy as np
from readcsv import pd_read_csv
import daal4py as d4p
def main(readcsv=pd_read_csv):
data_path = Path(__file__).parent / "data" / "batch"
infile = data_path / "mse.csv"
# Read the data, let's have 3 independent variables
data = readcsv(infile, range(3))
dep_data = readcsv(infile, range(3, 4))
nVectors = data.shape[0]
# configure a MSE object
mse_algo = d4p.optimization_solver_mse(nVectors)
mse_algo.setup(data, dep_data)
# configure a SGD object
lrs = np.array([[1.0]], dtype=np.double)
niters = 1000
sgd_algo = d4p.optimization_solver_sgd(
mse_algo,
learningRateSequence=lrs,
accuracyThreshold=0.0000001,
nIterations=niters,
)
# finally do the computation
inp = np.array([[8], [2], [1], [4]], dtype=np.double)
res = sgd_algo.compute(inp)
# The SGD result provides minimum and nIterations
assert res.minimum.shape == inp.shape and res.nIterations[0][0] <= niters
return res
if __name__ == "__main__":
res = main()
print("\nMinimum:\n", res.minimum)
print("\nNumber of iterations performed:\n", res.nIterations[0][0])
print("All looks good!")