def on_train_begin(self, logs={}):
self.train_loss = []
self.auc = []
self.logits = []
self.val_loss = []
# save json representation
model_json = self.model.to_json()
with open("/home/ahmed/output/" + RUN + "_json.json",
"w") as json_file:
json_file.write(model_json)
dataFrameTrain, dataFrameValidate, dataFrameTest = manageDataFrames()
#
x_val, y_val, zeros, ones = getXandY(dataFrameValidate, imgSize)
print("validation data:", x_val.shape, y_val.shape, zeros, ones)
self.dataFrameValidate = dataFrameValidate
self.y_val = y_val
# lets do featurewiseCenterAndStd - its still a cube at this point
# x_val_cs = centerAndStandardizeValTest(x_val,mean,std)
x_val_cs = centerAndNormalize(x_val)
# x_val_cs = x_val
if fork:
# lets get the 3 orientations
self.x_val_a, self.x_val_s, self.x_val_c = krs.splitValTest(
x_val_cs, finalSize, imgSize, count, mode, fork, skip)
print("final val data:", self.x_val_a.shape, self.x_val_s.shape,
self.x_val_c.shape)
else:
# lets get one only
self.x_val = krs.splitValTest(x_val_cs, finalSize, imgSize, count,
mode, fork, skip)
print("final val data:", x_val.shape)
return
#
#
dataFrameTest, dataFrameValidate, dataFrameTest = funcs.manageDataFrames()
x_test = funcs.getX(dataFrameTest, imgSize)
print("test data:", x_test.shape)
# center and standardize
# x_test_cs = funcs.centerAndStandardizeValTest(x_test,mean,std)
x_test_cs = funcs.centerAndNormalize(x_test)
if fork:
# lets get the 3 orientations
x_test_a, x_test_s, x_test_c = krs.splitValTest(x_test_cs, finalSize,
imgSize, count, mode, fork,
skip)
print("final val data:", x_test_a.shape, x_test_s.shape, x_test_c.shape)
else:
x_test = krs.splitValTest(x_test_cs, finalSize, imgSize, count, mode, fork,
skip)
print("final val data:", x_test.shape)
#
#
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