h = F.dropout(F.relu(model.fl4(h)), train=False)
y = model.fl5(h)
return y
cnn = Classifier(model, gpu=-1)
cnn.set_forward(forward)
mnist = fetch_mldata('MNIST original', data_home='.')
perm = numpy.random.permutation(len(mnist.data))
mnist.data = mnist.data.astype(numpy.float32).reshape(70000, 1, 28, 28) / 255
mnist.target = mnist.target.astype(numpy.int32)
train_data = mnist.data[perm][:60000]
train_label = mnist.target[perm][:60000]
test_data = mnist.data[perm][60000:]
test_label = mnist.target[perm][60000:]
for epoch in range(15):
print('epoch : %d' % (epoch + 1))
err, acc = cnn.train(train_data, train_label, batchsize=200)
print(acc, err)
perm = numpy.random.permutation(len(test_data))
terr, tacc = cnn.test(test_data[perm][:100], test_label[perm][:100])
print(tacc, terr)
with open('cnn.log', mode='a') as f:
f.write("%d %f %f\n" % (epoch + 1, err, terr))
cnn.save_param('cnn.param.npy')
h = F.max_pooling_2d(F.relu(h), 2)
h = model.bn2(model.conv2(h))
if layer == 2:
return h
h = F.max_pooling_2d(F.relu(h), 2)
h = model.conv3(h)
if layer == 3:
return h
return None
cnn.set_output(output)
imager = V.Visualizer(cnn)
imager.plot_filters('conv1')
imager.write_filters('conv1',
path='./filter',
identifier='filter_',
type='bmp')
imager.plot_output(numpy.array(arr).astype(numpy.float32)[:3], layer=1)
imager.write_output(numpy.array(arr).astype(numpy.float32),
layer=2,
path='./outputs',
identifier='l1_',
type='bmp')
imager.write_activation(arr[0], 2)
cnn.save_param()
h = F.dropout(F.relu(model.fl4(h)), train=False)
y = model.fl5(h)
return y
cnn = Classifier(model, gpu=-1)
cnn.set_forward(forward)
mnist = fetch_mldata('MNIST original', data_home='.')
perm = numpy.random.permutation(len(mnist.data))
mnist.data = mnist.data.astype(numpy.float32).reshape(70000, 1, 28, 28) / 255
mnist.target = mnist.target.astype(numpy.int32)
train_data = mnist.data[perm][:60000]
train_label = mnist.target[perm][:60000]
test_data = mnist.data[perm][60000:]
test_label = mnist.target[perm][60000:]
for epoch in range(15):
print('epoch : %d' % (epoch + 1))
err, acc = cnn.train(train_data, train_label, batchsize=200)
print(acc, err)
perm = numpy.random.permutation(len(test_data))
terr, tacc = cnn.test(test_data[perm][:100], test_label[perm][:100])
print(tacc, terr)
with open('cnn.log', mode='a') as f:
f.write("%d %f %f\n" % (epoch + 1, err, terr))
cnn.save_param('cnn.param.npy')
def output(self, x, layer):
h = model.bn1(model.conv1(x))
if layer == 1:
return h
h = F.max_pooling_2d(F.relu(h), 2)
h = model.bn2(model.conv2(h))
if layer == 2:
return h
h = F.max_pooling_2d(F.relu(h), 2)
h = model.conv3(h)
if layer == 3:
return h
return None
cnn.set_output(output)
imager = V.Visualizer(cnn)
imager.plot_filters('conv1')
imager.write_filters('conv1', path='./filter', identifier='filter_', type='bmp')
imager.plot_output(numpy.array(arr).astype(numpy.float32)[:3], layer=1)
imager.write_output(numpy.array(arr).astype(numpy.float32), layer=2, path='./outputs', identifier='l1_', type='bmp')
imager.write_activation(arr[0], 2)
cnn.save_param()
