Instantly share code, notes, and snippets.

What would you like to do?
An example of deep learning on the digits dataset using Keras
import numpy
import pandas
from sklearn.datasets import load_digits
from sklearn import preprocessing
from sklearn.cross_validation import KFold
from sklearn.svm import SVC
from sklearn.metrics import zero_one_loss
from keras.models import Sequential
from keras.layers.core import Dense, Activation
dataset = load_digits()
X = dataset["data"]
y = dataset["target"]
y_indicators = pandas.get_dummies(y).values
# Center each feature and scale the variance to be unitary
X = preprocessing.scale(X)
svc = SVC(gamma=0.001)
# Set up variables
svc_error = 0
ann_error = 0
n_folds = 10
for train_inds, test_inds in KFold(X.shape[0], n_folds=n_folds):
X_train, X_test = X[train_inds], X[test_inds]
y_train, y_test = y[train_inds], y[test_inds]
y_train_indicators, y_test_indicators = y_indicators[train_inds, :], y_indicators[test_inds, :]
# Use the SVM, y_train)
y_pred = svc.predict(X_test)
svc_error += zero_one_loss(y_test, y_pred)
# Use deep learner
ann = Sequential()
ann.add(Dense(output_dim=256, input_dim=X.shape[1], init="glorot_uniform"))
ann.add(Dense(output_dim=10, init="glorot_uniform"))
ann.compile(loss='categorical_crossentropy', optimizer='sgd'), y_train_indicators, nb_epoch=50, batch_size=32)
y_pred = ann.predict(X_test)
# The predicted class is the output response with the largest value
y_pred = numpy.argmax(y_pred, 1)
ann_error += zero_one_loss(y_test, y_pred)
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment