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com.intel.analytics.bigdl.models.vgg.VggForCifar10.scala Maven / Gradle / Ivy
/*
* Copyright 2016 The BigDL 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.
*/
package com.intel.analytics.bigdl.models.vgg
import com.intel.analytics.bigdl._
import com.intel.analytics.bigdl.nn._
import com.intel.analytics.bigdl.numeric.NumericFloat
object VggForCifar10 {
def apply(classNum: Int): Module[Float] = {
val vggBnDo = Sequential[Float]()
def convBNReLU(nInputPlane: Int, nOutPutPlane: Int)
: Sequential[Float] = {
vggBnDo.add(SpatialConvolution(nInputPlane, nOutPutPlane, 3, 3, 1, 1, 1, 1))
vggBnDo.add(SpatialBatchNormalization(nOutPutPlane, 1e-3))
vggBnDo.add(ReLU(true))
vggBnDo
}
convBNReLU(3, 64).add(Dropout((0.3)))
convBNReLU(64, 64)
vggBnDo.add(SpatialMaxPooling(2, 2, 2, 2).ceil())
convBNReLU(64, 128).add(Dropout(0.4))
convBNReLU(128, 128)
vggBnDo.add(SpatialMaxPooling(2, 2, 2, 2).ceil())
convBNReLU(128, 256).add(Dropout(0.4))
convBNReLU(256, 256).add(Dropout(0.4))
convBNReLU(256, 256)
vggBnDo.add(SpatialMaxPooling(2, 2, 2, 2).ceil())
convBNReLU(256, 512).add(Dropout(0.4))
convBNReLU(512, 512).add(Dropout(0.4))
convBNReLU(512, 512)
vggBnDo.add(SpatialMaxPooling(2, 2, 2, 2).ceil())
convBNReLU(512, 512).add(Dropout(0.4))
convBNReLU(512, 512).add(Dropout(0.4))
convBNReLU(512, 512)
vggBnDo.add(SpatialMaxPooling(2, 2, 2, 2).ceil())
vggBnDo.add(View(512))
val classifier = Sequential[Float]()
classifier.add(Dropout(0.5))
classifier.add(Linear(512, 512))
classifier.add(BatchNormalization(512))
classifier.add(ReLU(true))
classifier.add(Dropout(0.5))
classifier.add(Linear(512, classNum))
classifier.add(LogSoftMax())
vggBnDo.add(classifier)
vggBnDo
}
}
object Vgg_16 {
def apply(classNum: Int): Module[Float] = {
val model = Sequential()
model.add(SpatialConvolution(3, 64, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(64, 64, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(SpatialConvolution(64, 128, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(128, 128, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(SpatialConvolution(128, 256, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(SpatialConvolution(256, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(View(512 * 7 * 7))
model.add(Linear(512 * 7 * 7, 4096))
model.add(Threshold(0, 1e-6))
model.add(Dropout(0.5))
model.add(Linear(4096, 4096))
model.add(Threshold(0, 1e-6))
model.add(Dropout(0.5))
model.add(Linear(4096, classNum))
model.add(LogSoftMax())
model
}
}
object Vgg_19 {
def apply(classNum: Int): Module[Float] = {
val model = Sequential()
model.add(SpatialConvolution(3, 64, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(64, 64, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(SpatialConvolution(64, 128, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(128, 128, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(SpatialConvolution(128, 256, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(SpatialConvolution(256, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(View(512 * 7 * 7))
model.add(Linear(512 * 7 * 7, 4096))
model.add(Threshold(0, 1e-6))
model.add(Dropout(0.5))
model.add(Linear(4096, 4096))
model.add(Threshold(0, 1e-6))
model.add(Dropout(0.5))
model.add(Linear(4096, classNum))
model.add(LogSoftMax())
model
}
}
