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]