Question: I tried tflearn Quickstart titanic tutorial successfully and made some tests further.
I was predicting a float target by 8 float inputs, and I modified some of the tutorial then 
'ValueError: Cannot feed value of shape (64,) for Tensor u'TargetsData/Y:0', which has shape '(?, 1)''

Build neural network
====================

```
net = tflearn.input_data(shape=[None, 8])
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 1, activation='relu')
net = tflearn.regression(net)

```

Define model
============

```
model = tflearn.DNN(net, tensorboard_verbose=0)

```

Start training (apply gradient descent algorithm)
=================================================

```
model.fit(data, mfe, n_epoch=100)#Err occurs

```

Could somebody kindly help me:
1. What do 'shape (64,) ' and shape '(?, 1)' stand for?
2. How can I fix this Architecture Err?
3. Could you make some recommendation of materials learning neural networks architecture?

Thanks & Regards,
Simon

Answer: 
I have never used TensorFlow but I bet this is just a broadcasting issue. Try to change the shape of your problematic array which currently has shape (64,) into (64,1), that is from a row vector to a column vector:

```
my_array.shape = (64, 1)

```

or more generally for any length:

```
my_array.shape = (-1, 1)

```

You can read about shapes and broadcasting rules on the numpy documentation pages for more details. As for recommendation for neural networks architecture learning material, this is unfortunately off-topic (too opinion based) by SO rules.
shape (64,) is the batch\_size in the network model.Here tflearn uses 64, as its default.if you had given batch\_size as 8 it would have been shape(8,)

shape (?, 1) is the output from your net.The titanic example was a classification problem and it had 2 classes,so it had 2 units in output

```
net=tflearn.reshape(net,[-1])

```

this should fix the issue . the -1 means none

make sure the reshaping is done before the estimator layer i.e tflearn.regression