CODEWITHSUNDEEP
pythontensorflowlinear-regressiontflearn
Tom Rijntjes
Tom Rijntjes

Reputation: 654

Shaping data for linear regression with TFlearn

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!

Upvotes: 1

Views: 677

Answers (2)

Gautam J
Gautam J

Reputation: 709

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')

Upvotes: 0

michael
michael

Reputation: 4547

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)

Upvotes: 1

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