Building an SVM with Tensorflow

Question

I currently have two numpy arrays:

• X - (157, 128) - 157 sets of 128 features
• Y - (157) - classifications of the feature sets

Answer 1

Here's an SVM usage example which does not throw an error:

import numpy
import tensorflow as tf

X = numpy.zeros([157, 128])
Y = numpy.zeros([157], dtype=numpy.int32)
example_id = numpy.array(['%d' % i for i in range(len(Y))])

x_column_name = 'x'
example_id_column_name = 'example_id'

train_input_fn = tf.estimator.inputs.numpy_input_fn(
    x={x_column_name: X, example_id_column_name: example_id},
    y=Y,
    num_epochs=None,
    shuffle=True)

svm = tf.contrib.learn.SVM(
    example_id_column=example_id_column_name,
    feature_columns=(tf.contrib.layers.real_valued_column(
        column_name=x_column_name, dimension=128),),
    l2_regularization=0.1)

svm.fit(input_fn=train_input_fn, steps=10)

Examples passed to the SVM Estimator need string IDs. You can probably substitute back infer_real_valued_columns_from_input, but you would need to pass it a dictionary so it picks up the right name for the column. In this case it's conceptually simpler to just construct the feature column yourself.