I get the following error...
ValueError: Cannot feed value of shape (16,) for Tensor 'TargetsData/Y:0', which has shape '(?, 16)'
I understand that this has to do with the shape of my Y variable which in this case is the variable labels, but I'm not sure how to change the shape to make my model work.
Basically, I have a CSV file which I saved into a variable using pandas...
data = pd.read_csv('Speed Dating Data.csv')
After some preprocessing, I decided to extract my target class as so...
# Target label used for training
labels = np.array(data["age"], dtype=np.float32)
Next I removed this column from my data variable...
# Data for training minus the target label.
data = np.array(data.drop("age", axis=1), dtype=np.float32)
Then I decided to setup my model...
net = tflearn.input_data(shape=[None, 32])
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 16, activation='softmax')
net = tflearn.regression(net)
# Define model.
model = tflearn.DNN(net)
model.fit(data, labels, n_epoch=10, batch_size=16, show_metric=True)
If I run this, I get the error above. Since my labels seems to be (16,) but I need it to be (?, 16), I tried the following code...
labels = labels[np.newaxis, :]
But this gives yet another error. I think I am unsure as to what form my target class, labels, is supposed to be. How can I fix this?
Reshape your label according to follows,
label= np.reshape(label,(-1,16)) # since you have 16 classes
which reshape the label to (?,16).
Hope this helps.
Updated according to your Requirement. And added comments to changes.
labels = np.array(data["age"], dtype=np.float32)
label= np.reshape(label,(-1,1)) #reshape to [6605,1]
data = np.array(data.drop("age", axis=1), dtype=np.float32)
net = tflearn.input_data(shape=[None, 32])
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 1, activation='softmax') #Since this is a regression problem only one output
net = tflearn.regression(net)
# Define model.
model = tflearn.DNN(net)
model.fit(data, labels, n_epoch=10, batch_size=16, show_metric=True)