def __init__(self, test_id, data_dir, provider, checkpoint_dir, train_range, test_range, test_freq,
save_freq, batch_size, num_epoch, image_size, image_color, learning_rate, params):
self.origin_test_range = test_range
if len(test_range) != 1:
test_range = [test_range[0]]
AutoStopTrainer.__init__(self, test_id, data_dir, provider, checkpoint_dir, train_range, test_range, test_freq,
save_freq, batch_size, num_epoch, image_size, image_color, learning_rate, False)
self.conv_params = []
self.fc_params = []
self.softmax_param = None
self.params = params
conv = True
for ld in self.params:
if ld['type'] in ['conv', 'rnorm', 'pool', 'neuron'] and conv:
self.conv_params.append(ld)
elif ld['type'] == 'fc' or (not conv and ld['type'] == 'neuron'):
self.fc_params.append(ld)
conv = False
else:
self.softmax_param = ld
self.conv_stack = FastNet.split_conv_to_stack(self.conv_params)
self.fc_stack = FastNet.split_fc_to_stack(self.fc_params)
pprint.pprint(self.conv_stack)
pprint.pprint(self.fc_stack)
self.fakefc_param = self.fc_stack[-1][0]
