def build_optimizer(self, model):
self._skip_gpu = False
return build_yellowfin(model, base_learning_rate=0.1)
def build_optimizer(self, model):
self._skip_gpu = False
return build_yellowfin(model, base_learning_rate=0.1)
def Train_Model():
current_folder = os.path.join(os.path.expanduser('~'), 'Development',
'MLResearch', 'datasets', 'blood-cells')
print(current_folder)
data_folder = os.path.join(current_folder, 'dataset2-master')
root_folder = current_folder
db_missing = False
if not os.path.exists(data_folder):
os.makedirs(data_folder)
print("Your data folder was not found!! This was generated: {}".format(
data_folder))
# Look for existing database: lmdb
if os.path.exists(os.path.join(data_folder, 'blood_cells_train_lmdb')):
print("lmdb train db found!")
else:
db_missing = True
if os.path.exists(os.path.join(data_folder, 'blood_cells_test_lmdb')):
print("lmdb test db found!")
else:
db_missing = True
arg_scope = {
"order": "NCHW",
"use_cudnn": False,
}
train_model = model_helper.ModelHelper(name="inceptionv4_train",
arg_scope=arg_scope)
reader = train_model.CreateDB(
"reader",
db=os.path.join(data_folder, 'blood_cells_train_lmdb'),
db_type='lmdb',
)
data, label = AddImageInput(train_model, reader, 64, 299, 'lmdb', False)
softmax, loss = inceptionv4.create_Inceptionv4(train_model, data, 4, label)
accuracy = train_model.Accuracy([softmax, label], "accuracy")
train_model.AddGradientOperators(["loss"])
optimizer.build_yellowfin(
train_model,
base_learning_rate=.001,
# allow_lr_injection= True,
)
train_model.Print('accuracy', [], to_file=1)
train_model.Print('loss', [], to_file=1)
train_model.Print('label', [], to_file=1)
train_model.Print('softmax', [], to_file=1)
workspace.RunNetOnce(train_model.param_init_net)
workspace.CreateNet(train_model.net, overwrite=True)
# pyplot.figure()
# d = workspace.FetchBlob('data')
# # _ = visualize.NCHW.ShowMultiple(data)
# pyplot.show()
total_iters = 500
accuracy = np.zeros(total_iters)
loss = np.zeros(total_iters)
for i in range(total_iters):
workspace.RunNet(train_model.net)
accuracy[i] = workspace.blobs['accuracy']
loss[i] = workspace.blobs['loss']
pyplot.plot(loss, 'b')
pyplot.plot(accuracy, 'r')
pyplot.legend(('Loss', 'Accuracy'), loc='upper right')
