def __init__(self):
QWidget.__init__(self)
self.layout = QVBoxLayout()
SET_HYPERPARAMETER("diffLatentSpace", latentSpace)
self.data = np.load("./npz/diffs.npz")["arr_1"]
self.codes = np.load(codeNpz)["arr_1"]
self.maxs = np.max(self.codes, axis=0)
self.mins = np.min(self.codes, axis=0)
self.model = model.emptyModel(restore + "_visual",
inputsShape=list(self.data.shape[1:]),
use="diff",
log=False)
self.model.restore(restore)
self.setLayout(self.layout)
self.setWindowTitle("JTEKT encoder visualization")
self.sliders = []
for idx in range(latentSpace):
self.sliders.append(
Slider(self, idx, self.maxs[idx], self.mins[idx]))
self.render = Render(self)
self.update()
import numpy as np
import model
from model import SET_HYPERPARAMETER
SET_HYPERPARAMETER("diffLatentSpace", 6)
data = np.load("./npz/diffs.npz")["arr_1"]
model = model.emptyModel("DIFF_17jan_ls6_g",
inputsShape=list(data.shape[1:]),
use="diff",
log=False)
model.restore("DIFF_17jan_ls6_f")
code = [[0, 0, 0, 0, 0, 0]]
result = model.generate(code, data[0:1])
print(result)
print(result.shape)
import model
import numpy as np
from model import SET_HYPERPARAMETER
SET_HYPERPARAMETER("contrast", 50.0)
SET_HYPERPARAMETER("diffLatentSpace", 30)
data = np.load("./npz/diffsWithNames.npz")
goods = data["arr_0"]
bads = data["arr_1"]
model = model.emptyModel("generateSmoothDiff",
inputsShape=list(goods[0].shape),
use="diff",
log=False)
model.restore("28jan_ls30")
trainDiffs = model.reproduce(goods)
testDiffs = model.reproduce(bads)
np.savez("./npz/smoothDiffsWithNames_ls30.npz", trainDiffs, testDiffs,
data["arr_2"], data["arr_3"])
import model
import numpy as np
from model import SET_HYPERPARAMETER
SET_HYPERPARAMETER("contrast", 300.0)
SET_HYPERPARAMETER("learningRate", 0.0005)
SET_HYPERPARAMETER("diffLatentSpace", 5)
SET_HYPERPARAMETER("normalize", "individual")
files = np.load("./npz/diffsWithNames.npz")
goods = files["arr_0"]
bads = files["arr_1"]
data = np.concatenate([bads, goods])
model = model.emptyModel("5feb-ls5-inorm_f",
inputsShape=list(data.shape[1:]),
use="diff")
model.restore("5feb-ls5-inorm_e")
model.train(epoch=100, dataset=data)
model.save()
import model
import numpy as np
from model import SET_HYPERPARAMETER
SET_HYPERPARAMETER("contrast", 300.0)
SET_HYPERPARAMETER("diffLatentSpace", 12)
SET_HYPERPARAMETER("normalize", "individual")
data = np.load("./npz/diffsWithNames.npz")
goods = data["arr_0"]
bads = data["arr_1"]
model = model.emptyModel("generateEncode",
use="diff",
log=False,
inputsShape=list(goods[0].shape))
model.restore("5feb-ls12-inorm_d")
testEncoded = model.encode(bads)
trainEncoded = model.encode(goods)
np.savez("./npz/codesWithNames_inorm.npz", trainEncoded, testEncoded,
data["arr_2"], data["arr_3"])
import model
import numpy as np
from model import SET_HYPERPARAMETER
SET_HYPERPARAMETER("latentSpace", 1)
data = np.load("./npz/dataWithNames.npz")
goods = data["arr_0"]
bads = data["arr_1"]
model = model.emptyModel("generateDiff",
inputsShape=list(goods[0].shape),
log=False,
use="jtekt")
model.restore("4feb-ls1")
trainDiffs = model.getDiff(goods)
testDiffs = model.getDiff(bads)
np.savez("./npz/diffsWithNames.npz", trainDiffs, testDiffs, data["arr_2"],
data["arr_3"])
import model
import numpy as np
from model import SET_HYPERPARAMETER
SET_HYPERPARAMETER("learningRate", 0.001)
SET_HYPERPARAMETER("latentSpace", 1)
goods = np.load("./npz/dataWithNames.npz")["arr_0"]
bads = np.load("./npz/dataWithNames.npz")["arr_1"]
data = np.concatenate([bads, goods])
model = model.emptyModel("4feb-ls1", inputsShape=list(data.shape[1:]), use="jtekt")
#model.restore("16jan-ls3")
model.train(epoch=20, dataset=data)
model.save()