I'm trying to expand the tflearn example for linear regression by increasing the number of columns to 21.
from trafficdata import X,Y
import tflearn
print(X.shape) #(1054, 21)
print(Y.shape) #(1054,)
# Linear Regression graph
input_ = tflearn.input_data(shape=[None,21])
linear = tflearn.single_unit(input_)
regression = tflearn.regression(linear, optimizer='sgd', loss='mean_square',
metric='R2', learning_rate=0.01)
m = tflearn.DNN(regression)
m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False)
print("\nRegression result:")
print("Y = " + str(m.get_weights(linear.W)) +
"*X + " + str(m.get_weights(linear.b)))
However, tflearn complains:
Traceback (most recent call last):
File "linearregression.py", line 16, in <module>
m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False)
File "/usr/local/lib/python3.5/dist-packages/tflearn/models/dnn.py", line 216, in fit
callbacks=callbacks)
File "/usr/local/lib/python3.5/dist-packages/tflearn/helpers/trainer.py", line 339, in fit
show_metric)
File "/usr/local/lib/python3.5/dist-packages/tflearn/helpers/trainer.py", line 818, in _train
feed_batch)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 789, in run
run_metadata_ptr)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 975, in _run
% (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (64,) for Tensor 'TargetsData/Y:0', which has shape '(21,)'
I found the shape (64, ) comes from the default batch size of tflearn.regression().
Do I need to transform the labels (Y)? In what way?
Thanks!
I tried to do the same. I made these changes to get it to work
# linear = tflearn.single_unit(input_)
linear = tflearn.fully_connected(input_, 1, activation='linear')
My guess is that with features >1 you cannot use tflearn.single_unit()
. You can add additional fully_connected layers, but the last one must have only 1 neuron because Y.shape=(?,1)
You have 21 features. Therefore, you cannot use linear = tflearn.single_unit(input_)
Instead try this: linear = tflearn.fully_connected(input_, 21, activation='linear')
The error you get is because your labels, i.e., Y has a shape of (1054,). You have to first preprocess it.
Try using the code given below before # linear regression graph
:
Y = np.expand_dims(Y,-1)
Now before regression = tflearn.regression(linear, optimizer='sgd', loss='mean_square',metric='R2', learning_rate=0.01)
, type the below code:
linear = tflearn.fully_connected(linear, 1, activation='linear')
来源:https://stackoverflow.com/questions/44763935/shaping-data-for-linear-regression-with-tflearn