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com.intel.analytics.bigdl.models.lenet.LeNet5.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.lenet
import com.intel.analytics.bigdl._
import com.intel.analytics.bigdl.mkl.Memory
import com.intel.analytics.bigdl.numeric.NumericFloat
import com.intel.analytics.bigdl.nn._
import com.intel.analytics.bigdl.nn.mkldnn.DnnGraph
object LeNet5 {
def apply(classNum: Int): Module[Float] = {
val model = Sequential()
model.add(Reshape(Array(1, 28, 28)))
.add(SpatialConvolution(1, 6, 5, 5).setName("conv1_5x5"))
.add(Tanh())
.add(SpatialMaxPooling(2, 2, 2, 2))
.add(SpatialConvolution(6, 12, 5, 5).setName("conv2_5x5"))
.add(Tanh())
.add(SpatialMaxPooling(2, 2, 2, 2))
.add(Reshape(Array(12 * 4 * 4)))
.add(Linear(12 * 4 * 4, 100).setName("fc1"))
.add(Tanh())
.add(Linear(100, classNum).setName("fc2"))
.add(LogSoftMax())
}
def graph(classNum: Int): Module[Float] = {
val input = Reshape(Array(1, 28, 28)).inputs()
val conv1 = SpatialConvolution(1, 6, 5, 5).setName("conv1_5x5").inputs(input)
val tanh1 = Tanh().inputs(conv1)
val pool1 = SpatialMaxPooling(2, 2, 2, 2).inputs(tanh1)
val conv2 = SpatialConvolution(6, 12, 5, 5).setName("conv2_5x5").inputs(pool1)
val tanh2 = Tanh().inputs(conv2)
val pool2 = SpatialMaxPooling(2, 2, 2, 2).inputs(tanh2)
val reshape = Reshape(Array(12 * 4 * 4)).inputs(pool2)
val fc1 = Linear(12 * 4 * 4, 100).setName("fc1").inputs(reshape)
val tanh3 = Tanh().inputs(fc1)
val fc2 = Linear(100, classNum).setName("fc2").inputs(tanh3)
val output = LogSoftMax().inputs(fc2)
Graph(input, output)
}
def keras(classNum: Int): nn.keras.Sequential[Float] = {
import com.intel.analytics.bigdl.nn.keras._
import com.intel.analytics.bigdl.utils.Shape
val model = Sequential()
model.add(Reshape(Array(1, 28, 28), inputShape = Shape(28, 28, 1)))
model.add(Convolution2D(6, 5, 5, activation = "tanh").setName("conv1_5x5"))
model.add(MaxPooling2D())
model.add(Convolution2D(12, 5, 5, activation = "tanh").setName("conv2_5x5"))
model.add(MaxPooling2D())
model.add(Flatten())
model.add(Dense(100, activation = "tanh").setName("fc1"))
model.add(Dense(classNum, activation = "softmax").setName("fc2"))
}
def kerasGraph(classNum: Int): nn.keras.Model[Float] = {
import com.intel.analytics.bigdl.nn.keras._
import com.intel.analytics.bigdl.utils.Shape
val input = Input(inputShape = Shape(28, 28, 1))
val reshape = Reshape(Array(1, 28, 28)).inputs(input)
val conv1 = Convolution2D(6, 5, 5, activation = "tanh").setName("conv1_5x5").inputs(reshape)
val pool1 = MaxPooling2D().inputs(conv1)
val conv2 = Convolution2D(12, 5, 5, activation = "tanh").setName("conv2_5x5").inputs(pool1)
val pool2 = MaxPooling2D().inputs(conv2)
val flatten = Flatten().inputs(pool2)
val fc1 = Dense(100, activation = "tanh").setName("fc1").inputs(flatten)
val fc2 = Dense(classNum, activation = "softmax").setName("fc2").inputs(fc1)
Model(input, fc2)
}
def dnn(batchSize: Int, classNum: Int): mkldnn.Sequential = {
val inputShape = Array(batchSize, 1, 28, 28)
val outputShape = Array(batchSize, 10)
val model = mkldnn.Sequential()
.add(mkldnn.Input(inputShape, Memory.Format.nchw))
.add(mkldnn.SpatialConvolution(1, 20, 5, 5).setName("conv1"))
.add(mkldnn.SpatialBatchNormalization(20).setName("bn1"))
.add(mkldnn.MaxPooling(2, 2, 2, 2).setName("pool1"))
.add(mkldnn.SpatialConvolution(20, 50, 5, 5).setName("conv2"))
.add(mkldnn.MaxPooling(2, 2, 2, 2).setName("pool2"))
.add(mkldnn.Linear(50 * 4 * 4, 500).setName("ip1"))
.add(mkldnn.ReLU().setName("relu1"))
.add(mkldnn.Linear(500, 10).setName("ip2"))
.add(mkldnn.ReorderMemory(mkldnn.HeapData(outputShape, Memory.Format.nc)))
model
}
def dnnGraph(batchSize: Int, classNum: Int): mkldnn.DnnGraph = {
val inputShape = Array(batchSize, 1, 28, 28)
val outputShape = Array(batchSize, 10)
val input = mkldnn.Input(inputShape, Memory.Format.nchw).inputs()
val conv1 = mkldnn.SpatialConvolution(1, 20, 5, 5).setName("conv1").inputs(input)
val bn1 = mkldnn.SpatialBatchNormalization(20).setName("bn1").inputs(conv1)
val pool1 = mkldnn.MaxPooling(2, 2, 2, 2).setName("pool1").inputs(bn1)
val conv2 = mkldnn.SpatialConvolution(20, 50, 5, 5).setName("conv2").inputs(pool1)
val pool2 = mkldnn.MaxPooling(2, 2, 2, 2).setName("pool2").inputs(conv2)
val ip1 = mkldnn.Linear(50 * 4 * 4, 500).setName("ip1").inputs(pool2)
val relu1 = mkldnn.ReLU().setName("relu1").inputs(ip1)
val ip2 = mkldnn.Linear(500, 10).setName("ip2").inputs(relu1)
val output = mkldnn.ReorderMemory(mkldnn.HeapData(outputShape, Memory.Format.nc)).inputs(ip2)
DnnGraph(Array(input), Array(output))
}
}
