我正在使用 Jupyter 笔记本尝试生成一长串 TF Learn DNN 对象以进行一些蛮力试错测试（我知道这不是最有效的方法，只是试图展示一个示例）。数据遵循泰坦尼克号快速入门教程。
我有一个函数，给定一堆参数，应该返回一个 tflearn.DNN() 对象：
def make_fully_connected(input_shape, output_shape, activation, layers, nodes, dropout, optimizer, loss):
tflearn.init_graph()
net = tflearn.input_data(shape=[None, input_shape])
for l in range(layers):
net = tflearn.fully_connected(net, nodes)
if (dropout != 0) and (l%2==1):
net = tflearn.dropout(net, dropout)
net = tflearn.fully_connected(net, output_shape, activation=activation)
net = tflearn.regression(net, optimizer=optimizer, loss=loss)
return tflearn.DNN(net)
然后我使用该函数生成一个特定的模型：
model = make_fully_connected(6, 2, 'softmax', 2, 32, 0, 'adam', 'categorical_crossentropy')
model.fit(data, labels, n_epoch=10, batch_size=16, show_metric=True)
score = model.evaluate(data, labels)
但我收到一条可爱的错误消息，将我带入 TF Learn 代码，我很快就迷路了：
IndexError Traceback (most recent call last)
<ipython-input-15-79e1d2acc8bf> in <module>()
1 model = make_fully_connected(6, 2, 'softmax', 2, 32, 0, 'adam', 'categorical_crossentropy')
----> 2 model.fit(data, labels, n_epoch=10, batch_size=16, show_metric=True)
3 score = model.evaluate(data, labels)
4 print('| Score: %.4f' % score, end='')
/usr/local/lib/python3.5/dist-packages/tflearn/models/dnn.py in fit(self, X_inputs, Y_targets, n_epoch, validation_set, show_metric, batch_size, shuffle, snapshot_epoch, snapshot_step, excl_trainops, validation_batch_size, run_id, callbacks)
181 # TODO: check memory impact for large data and multiple optimizers
182 feed_dict = feed_dict_builder(X_inputs, Y_targets, self.inputs,
--> 183 self.targets)
184 feed_dicts = [feed_dict for i in self.train_ops]
185 val_feed_dicts = None
/usr/local/lib/python3.5/dist-packages/tflearn/utils.py in feed_dict_builder(X, Y, net_inputs, net_targets)
287 X = [X]
288 for i, x in enumerate(X):
--> 289 feed_dict[net_inputs[i]] = x
290 else:
291 # If a dict is provided
IndexError: list index out of range
从函数返回模型是否超出了 TF Learn 的范围？或者还有其他障碍？