def main():
try:
echos_tune = 10
batchNum = 2000
batchSize = 2000
ImagePatchWidth = 40
ImagePatchStep = 1
labelMode = "NUM"
label_mutiplier = 1.0
hidden_units = [200, 400, 200]
steps = 200
optimizer = "Adagrad" # "SGD", "Adam", "Adagrad"
learning_rate = 0.1
clip_gradients = 5.0
config = None
verbose = 1
dropout = None
filename = "detector_3_no_5_angle_2.jpg"
image_files, bubble_num, bubble_regions = getinfo()
benchmark_tune = np.zeros((echos_tune, 2))
# generate data:
# ds.gaussianDatatoFile(ImagePatchWidth, ImagePatchStep, labelMode)
classifier, trainDataset = dl.train(
batchNum=0,
batchSize=1,
ImagePatchWidth=ImagePatchWidth,
ImagePatchStep=ImagePatchStep,
labelMode=labelMode,
hidden_units=hidden_units,
steps=steps,
optimizer=optimizer,
learning_rate=learning_rate,
clip_gradients=clip_gradients,
config=config,
verbose=verbose,
dropout=dropout,
)
PROGRESS = progress.progress(0, echos_tune)
for i in range(echos_tune):
PROGRESS.setCurrentIteration(i + 1)
PROGRESS.setInfo(prefix_info="Batch Number Tune ...", suffix_info=str(i + 1) + "/" + str(echos_tune))
classifier = dl.continuetrain(
classifier=classifier, trainDataset=trainDataset, batchNum=batchNum, batchSize=batchSize
)
result, accuracy = dl.testall(
classifier,
ImagePatchWidth=ImagePatchWidth,
ImagePatchStep=ImagePatchStep,
label_mutiplier=label_mutiplier,
labelMode=labelMode,
image_show=0, # show labeling result of each image
save_image=False, # save each labeled image
save_result=False,
)
slope, intercept, r_value, p_value, std_err = linregress(bubble_num, result.T)
benchmark_tune[i, 0] = r_value
benchmark_tune[i, 1] = std_err
PROGRESS.printProgress()
print "\n"
filename = gv.__DIR__ + gv.dp__tuningPar_dir + "batchnumberresult.npy"
np.save(filename, np.array(benchmark_tune))
fig, ax = plt.subplots(1)
ax.plot(np.arange(echos_tune), benchmark_tune[:, 0], color="r", label="r_value")
ax.plot(np.arange(echos_tune), benchmark_tune[:, 1], color="b", label="std_err")
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels, loc="upper left")
plt.show()
except KeyboardInterrupt:
print "Shutdown requested... exiting"
except Exception:
traceback.print_exc(file=sys.stdout)
sys.exit(0)
def main():
try:
batchNum = 2000
batchSize = 20000
ImagePatchWidth = 40
ImagePatchStep = 1
labelMode = 'PRO'
hidden_units = [200, 400, 200]
steps = 1
optimizer = 'Adagrad' #"SGD", "Adam", "Adagrad"
learning_rate = 0.14
clip_gradients = 5.0
config = None
verbose = 1
dropout = None
filename = 'detector_3_no_5_angle_2.jpg'
classifier =dl.train(
batchNum = batchNum,
batchSize = batchSize,
ImagePatchWidth = ImagePatchWidth,
ImagePatchStep = ImagePatchStep,
labelMode = labelMode,
hidden_units = hidden_units,
steps = steps,
optimizer = optimizer,
learning_rate = learning_rate,
clip_gradients = clip_gradients,
config = config,
verbose = verbose,
dropout = dropout)
dl.test(
classifier,
filename,
ImagePatchWidth = ImagePatchWidth,
ImagePatchStep = ImagePatchStep,
labelMode = labelMode,
plot_show = 1,
save_image = True)
#"""
"""
dl.performance(
ImagePatchWidth = ImagePatchWidth,
ImagePatchStep = ImagePatchStep,
trainBatchNum = batchNum,
trainBatchSize = batchSize,
hidden_units = hidden_units,
trainSteps = steps,
optimizer = optimizer,
learning_rate = learning_rate,
label_mode = labelMode,
clip_gradients = clip_gradients,
config = config,
verbose = verbose,
dropout = dropout,
plot_show = 1,
save_result = True)
#"""
except KeyboardInterrupt:
print "Shutdown requested... exiting"
except Exception:
traceback.print_exc(file=sys.stdout)
sys.exit(0)
