Python give 예제들

예제 #1

파일: _max.py 프로젝트: vlamprinidis/nn-estimation

                           name=NAME))
        model.add(Flatten(name='FLATTEN'))
        model.add(Dense(units=10, name='FINAL_DENSE'))
        model.compile(loss=lib_tflow.loss,
                      optimizer=lib_tflow.opt,
                      metrics=['accuracy'])
        self.model = model


Model = Max()
if args.nodes > 1:
    distribute(strategy, Model, args.nodes)
else:
    Model.create()

dataset = give(DIM, args.numf, args.channels)

dataset = dataset.batch(args.batch)

if args.nodes > 1:
    dataset = strategy.experimental_distribute_dataset(dataset)

steps = ds_size // args.batch // args.nodes

the_typs = ['MaxPool']

time = lib_tflow.profile(the_typs, None, Model.model, dataset, steps,
                         args.epochs)

import numpy as np

예제 #2

파일: tflow.py 프로젝트: vlamprinidis/nn-estimation

parser.add_argument('-epochs', type=int, required=True)
args = parser.parse_args()

print('model:', args.model, 'dataset size:', args.ds, 'numf:', args.numf,
      'channels:', args.ch, 'out:', args.out)

Model = getattr(tflow_models, args.model)()
if args.nodes > 1:
    model = tflow_lib.distribute(strategy, Model, args.nodes)
else:
    model = Model.create()

ds_size = args.ds
dataset = tf_data.give(2,
                       args.numf,
                       args.ch,
                       out_size=args.out,
                       ds_size=ds_size)

dataset = dataset.batch(args.batch)

if args.nodes > 1:
    dataset = strategy.experimental_distribute_dataset(dataset)

steps = max(ds_size / args.batch / args.nodes, 1)

the_time = tflow_lib.profile(model, dataset, steps, args.epochs)

import socket
host = socket.gethostname()

예제 #3

        model = Sequential()
        model.add(Flatten(name='FLATTEN'))
        model.add(Dense(units=args.units, name='FINAL_DENSE'))
        model.compile(loss=lib_tflow.loss,
                      optimizer=lib_tflow.opt,
                      metrics=['accuracy'])
        self.model = model


Model = FinalDense()
if args.nodes > 1:
    distribute(strategy, Model, args.nodes)
else:
    Model.create()

dataset = give(1, args.numf, 1, out_size=args.units)

dataset = dataset.batch(args.batch)

if args.nodes > 1:
    dataset = strategy.experimental_distribute_dataset(dataset)

steps = ds_size // args.batch // args.nodes

the_typs = ['_FusedMatMul', 'MatMul']
the_ops = ['FINAL_DENSE']

time = lib_tflow.profile(the_typs, the_ops, Model.model, dataset, steps,
                         args.epochs)

import numpy as np

예제 #4

파일: _dense.py 프로젝트: vlamprinidis/nn-estimation

        model.add(Dense(units=args.units))
        model.add(Flatten(name='FLATTEN'))
        model.add(Dense(units=10, name='FINAL_DENSE'))
        model.compile(loss=lib_tflow.loss,
                      optimizer=lib_tflow.opt,
                      metrics=['accuracy'])
        self.model = model


Model = MyDense()
if args.nodes > 1:
    distribute(strategy, Model, args.nodes)
else:
    Model.create()

dataset = give(1, args.numf, 1)

dataset = dataset.batch(args.batch)

if args.nodes > 1:
    dataset = strategy.experimental_distribute_dataset(dataset)

steps = ds_size // args.batch // args.nodes

the_typs = ['MatMul']
the_ops = ['dense']

time = lib_tflow.profile(the_typs, the_ops, Model.model, dataset, steps,
                         args.epochs)

import numpy as np