はじめに
MNISTデータを学習させて数字を書かせる。
今回は単純に数字の「5」だけを書かせる。
参考にさせて頂いたサイト
環境
Windows10 Pro 64bit
NVIDIA GeForce GTX1080
CUDA9.2
cudnn7.2.1
Python3.6.6(venv使用)
astroid==2.0.4 certifi==2018.8.24 chardet==3.0.4 colorama==0.3.9 graphviz==0.8.4 idna==2.6 isort==4.3.4 lazy-object-proxy==1.3.1 mccabe==0.6.1 mxnet-cu92==1.3.1b20180927 numpy==1.14.6 Pillow==5.2.0 pylint==2.1.1 requests==2.18.4 six==1.11.0 typed-ast==1.1.0 urllib3==1.22 wrapt==1.10.11
モデル（gan_model.py）
import mxnet as mx from mxnet.gluon import Block, nn, rnn class modelG(Block): def __init__(self, **kwargs): super(modelG, self).__init__(**kwargs) with self.name_scope(): self.dense1 = nn.Dense(1024) self.batch1 = nn.BatchNorm() self.dense2 = nn.Dense(6272) self.batch2 = nn.BatchNorm() self.tconv2d1 =nn.Conv2DTranspose(64, kernel_size=4, strides=2, padding=1, use_bias=False) self.batch3 = nn.BatchNorm() self.tconv2d2 =nn.Conv2DTranspose(1, kernel_size=4, strides=2, padding=1, use_bias=False) def forward(self, x): output = self.dense1(x) output = self.batch1(output) output = mx.nd.relu(output) output = self.dense2(output) output = self.batch2(output) output = mx.nd.relu(output) output = output.reshape(-1,128,7,7) output = self.tconv2d1(output) output = self.batch3(output) output = mx.nd.relu(output) output = self.tconv2d2(output) output = mx.nd.sigmoid(output) return output class modelD(Block): def __init__(self, **kwargs): super(modelD, self).__init__(**kwargs) with self.name_scope(): self.conv2d1 = nn.Conv2D(64, kernel_size=4, strides=2, padding=1, use_bias=False) self.lrelu1 = nn.LeakyReLU(alpha=0.2) self.conv2d2 = nn.Conv2D(128, kernel_size=4, strides=2, padding=1, use_bias=False) self.batch_1 = nn.BatchNorm() self.lrelu2 = nn.LeakyReLU(alpha=0.2) self.dense_1 = nn.Dense(1024) self.batch_2 = nn.BatchNorm() self.lrelu3 = nn.LeakyReLU(alpha=0.2) self.dense_2 = nn.Dense(1) def forward(self, x): output = self.conv2d1(x) output = self.lrelu1(output) output = self.conv2d2(output) output = self.batch_1(output) output = self.lrelu2(output) output = self.dense_1(output) output = self.batch_2(output) output = self.lrelu3(output) output = self.dense_2(output) output = mx.nd.sigmoid(output) return output
実行ファイル
import mxnet as mx from mxnet import gluon, autograd import numpy as np from PIL import Image mnist = mx.test_utils.get_mnist() x_train = mnist['train_data'] t_train = mnist['train_label'] indx = np.where(t_train==5) x_train = x_train[indx] x_train = mx.nd.array(x_train) import gan_model modelD = gan_model.modelD() modelD.initialize(mx.init.Normal(0.02), ctx=mx.gpu()) modelG = gan_model.modelG() modelG.initialize(mx.init.Normal(0.02), ctx=mx.gpu()) trainerD = gluon.Trainer(modelD.collect_params(), 'adam') trainerG = gluon.Trainer(modelG.collect_params(), 'adam') lossD = gluon.loss.SigmoidBinaryCrossEntropyLoss(from_sigmoid=True) # hyperparameters batch_size = 100 epochs = 25 loss_d = [] #ログ表示用 loss_g = [] for epoch in range(1, epochs + 1): #ランダムに並べ替えたインデックスを作成 indexs = np.random.permutation(x_train.shape[0]) cur_start = 0 while cur_start < x_train.shape[0]: cur_end = (cur_start + batch_size) if (cur_start + batch_size) < x_train.shape[0] else x_train.shape[0] data = x_train[indexs[cur_start:cur_end]].as_in_context(mx.gpu()) label = mx.nd.ones(data.shape[0], ctx=mx.gpu()) noise = mx.nd.random.uniform(0,1,shape=(data.shape[0],100), ctx=mx.gpu()) # update D with autograd.record(): output = modelD(data) D_error_real = lossD(output,label) fake_image = modelG(noise) output = modelD(fake_image.detach()) D_error_fake = lossD(output, label*0) D_error = D_error_real + D_error_fake loss_d.append(np.mean(D_error.asnumpy())) D_error.backward() #学習ステータスをデータサイズ分進める trainerD.step(data.shape[0]) # updata G noise = mx.nd.random.uniform(0,1,shape=(data.shape[0],100), ctx=mx.gpu()) with autograd.record(): fake_image = modelG(noise) output = modelD(fake_image) G_error = lossD(output,label) loss_g.append(np.mean(G_error.asnumpy())) G_error.backward() trainerG.step(data.shape[0]) cur_start = cur_end #ログを表示 ll_d = np.mean(loss_d) ll_g = np.mean(loss_g) print('%d epoch G_loss = %f D_loss = %f' %(epoch, ll_g, ll_d)) loss_d = [] loss_g = [] if (epoch % 5)==0: noise = mx.nd.random.uniform(0,1,shape=(1,100), ctx=mx.gpu()) image = modelG(noise) image = Image.fromarray(image[0][0].asnumpy()*255) image = image.convert('L') image.save('epoch%d.png'%epoch) modelD.save_parameters('modelD.params') modelG.save_parameters('modelG.params')
結果
約1分くらいで学習が終わる。
1 epoch G_loss = 0.777134 D_loss = 1.376519 2 epoch G_loss = 1.097475 D_loss = 0.958754 3 epoch G_loss = 1.474690 D_loss = 0.758916 4 epoch G_loss = 1.602817 D_loss = 0.771593 5 epoch G_loss = 1.678429 D_loss = 0.779117 6 epoch G_loss = 1.931964 D_loss = 0.788672 7 epoch G_loss = 2.244472 D_loss = 0.575185 8 epoch G_loss = 1.683377 D_loss = 0.910790 9 epoch G_loss = 1.919621 D_loss = 0.645680 10 epoch G_loss = 1.739710 D_loss = 1.013023 11 epoch G_loss = 1.705402 D_loss = 0.821467 12 epoch G_loss = 1.961140 D_loss = 0.780046 13 epoch G_loss = 1.573381 D_loss = 0.965425 14 epoch G_loss = 1.935019 D_loss = 0.806350 15 epoch G_loss = 2.063324 D_loss = 0.681775 16 epoch G_loss = 1.585250 D_loss = 0.917888 17 epoch G_loss = 1.395943 D_loss = 0.929869 18 epoch G_loss = 1.585122 D_loss = 0.944633 19 epoch G_loss = 1.609372 D_loss = 0.867665 20 epoch G_loss = 1.598825 D_loss = 0.868693 21 epoch G_loss = 1.390774 D_loss = 1.091793 22 epoch G_loss = 1.514822 D_loss = 0.900835 23 epoch G_loss = 1.540376 D_loss = 0.928998 24 epoch G_loss = 1.609747 D_loss = 0.955562 25 epoch G_loss = 1.333527 D_loss = 1.090823
5, 10, 15, 20, 25エポック終了時に書かせた図
