ktl014

# Importing data from csv file
csv_filename = './tweets.csv'
dataset = tf.contrib.data.make_csv_dataset(csv_filename, batch_size=32)
dataset = dataset.shuffle(buffer_size=100)
iter = dataset.make_one_shot_iterator()
next = iter.get_next()
features, labels = next['text'], next['sentiment']

with tf.Session() as sess:
	sess.run([features, labels])

ktl014

from sklearn.kernel_approximation import RBFSampler
from sklearn.decomposition import PCA

kernel_svm = svm.SVC(gamma=.2)
linear_svm = svm.LinearSVC()
feature_map_fourier = RBFSampler(gamma=.2, random_state=SEED)
feature_map_nystroem = Nystroem(gamma=.2, random_state=SEED)
fourier_approx_svm = pipeline.Pipeline([("feature_map", feature_map_fourier),
                                        ("svm", svm.LinearSVC())])

ktl014

# AlexNet
name: "AlexNet"
layer {
  name: "train-data"
  type: "Data"
  top: "data"
  top: "label"
  transform_param {
    mirror: false
    crop_size: 227