def tf_logistic_reg(train_X, train_Y, test_X, test_Y):
# Start time
startTime = time.time();
enc = preprocessing.OneHotEncoder(n_values='auto');
train_Y_onehot = enc.fit_transform(np.int32((np.transpose(np.matrix(train_Y)) + 1)/2)).toarray();
test_Y_onehot = enc.fit_transform(np.int32((np.transpose(np.matrix(test_Y)) + 1)/2)).toarray();
x = tf.placeholder("float", [None, train_X.shape[1]]);
y_ = tf.placeholder("float", [None, 2]);
W = tf.Variable(tf.zeros([train_X.shape[1], 2]));
b = tf.Variable(tf.zeros([2]));
y = tf.nn.softmax(tf.matmul(x,W) + b);
cross_entropy = -tf.reduce_sum(y_*tf.log(y));
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy);
init = tf.initialize_all_variables();
sess = tf.Session();
sess.run(init);
cur_id = 0;
for i in range(1000):
batch_xs, batch_ys, cur_id = sample(train_X, train_Y_onehot, cur_id, 500);
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys});
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1));
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"));
accu_train = sess.run(accuracy, feed_dict={x: train_X, y_: train_Y_onehot});
accu_test = sess.run(accuracy, feed_dict={x: test_X, y_: test_Y_onehot});
print "Logistic Regression: train accuracy %f"%accu_train;
print "Logistic Regression: test accuracy %f"%accu_test;
# Stop time
stopTime = time.time();
print "Elapsed time (logistic regression): %f"%(stopTime - startTime);
def tf_logistic_reg(train_X, train_Y, test_X, test_Y):
# Start time
startTime = time.time()
enc = preprocessing.OneHotEncoder(n_values='auto')
train_Y_onehot = enc.fit_transform(
np.int32((np.transpose(np.matrix(train_Y)) + 1) / 2)).toarray()
test_Y_onehot = enc.fit_transform(
np.int32((np.transpose(np.matrix(test_Y)) + 1) / 2)).toarray()
x = tf.placeholder("float", [None, train_X.shape[1]])
y_ = tf.placeholder("float", [None, 2])
W = tf.Variable(tf.zeros([train_X.shape[1], 2]))
b = tf.Variable(tf.zeros([2]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(
cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
cur_id = 0
for i in range(1000):
batch_xs, batch_ys, cur_id = sample(train_X, train_Y_onehot, cur_id,
500)
sess.run(train_step, feed_dict={
x: batch_xs,
y_: batch_ys
})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
accu_train = sess.run(accuracy, feed_dict={
x: train_X,
y_: train_Y_onehot
})
accu_test = sess.run(accuracy, feed_dict={
x: test_X,
y_: test_Y_onehot
})
print "Logistic Regression: train accuracy %f" % accu_train
print "Logistic Regression: test accuracy %f" % accu_test
# Stop time
stopTime = time.time()
print "Elapsed time (logistic regression): %f" % (stopTime - startTime)
def tf_nn(train_X, train_Y, test_X, test_Y):
# Start time
startTime = time.time();
enc = preprocessing.OneHotEncoder(n_values='auto');
train_Y_onehot = enc.fit_transform(np.int32((np.transpose(np.matrix(train_Y)) + 1)/2)).toarray();
test_Y_onehot = enc.fit_transform(np.int32((np.transpose(np.matrix(test_Y)) + 1)/2)).toarray();
nr_vars = train_X.shape[1];
x = tf.placeholder("float", [None, nr_vars]);
y_ = tf.placeholder("float", [None, 2]);
# instance of Ws and bs
W_fc1 = weight_variable([nr_vars, 1024]);
b_fc1 = bias_variable([1024]);
W_fc3 = weight_variable([1024, 257]);
b_fc3 = bias_variable([257]);
W_fc2 = weight_variable([257, 2]);
b_fc2 = bias_variable([2]);
# operations
h_fc1 = tf.nn.relu(tf.matmul(x, W_fc1) + b_fc1);
# keep_prob = tf.placeholder("float");
# h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob);
# y = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2);
h_fc2 = tf.nn.relu(tf.matmul(h_fc1, W_fc3) + b_fc3);
y = tf.nn.softmax(tf.matmul(h_fc2, W_fc2) + b_fc2);
cross_entropy = -tf.reduce_sum(y_ * tf.log(y));
train_step = tf.train.GradientDescentOptimizer(1e-3).minimize(cross_entropy);
# train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
init = tf.initialize_all_variables();
sess = tf.Session();
sess.run(init);
cur_id = 0
for i in range(20000):
print i;
batch_xs, batch_ys, cur_id = sample(train_X, train_Y_onehot, cur_id, 256);
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys});
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_,1));
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"));
# sess.run(tf.initialize_all_variables());
# cur_id = 0;
# for i in range(20000):
# batch_xs, batch_ys, cur_id = sample(train_X, train_Y_onehot, cur_id, 256);
# if i%100 == 0:
# train_accuracy = accuracy.eval(feed_dict={x: batch_xs, y_: batch_ys})
# print "step %d, training accuracy %f"%(i, train_accuracy)
# train_step.run(feed_dict={x: batch_xs, y_: batch_ys})
print "NN: train accuracy %f"%sess.run(accuracy, feed_dict={x: train_X, y_: train_Y_onehot});
print "NN: test accuracy %f"%sess.run(accuracy, feed_dict={x: test_X, y_: test_Y_onehot});
# auc = metrics.roc_auc_score(test_Y, pred_Y, average='macro', sample_weight=None);
# print "NN: area under the ROC curve %f"%auc;
# Stop time
stopTime = time.time();
print "Elapsed time (neural network): %f"%(stopTime - startTime);
def tf_nn(train_X, train_Y, test_X, test_Y):
# Start time
startTime = time.time()
enc = preprocessing.OneHotEncoder(n_values='auto')
train_Y_onehot = enc.fit_transform(
np.int32((np.transpose(np.matrix(train_Y)) + 1) / 2)).toarray()
test_Y_onehot = enc.fit_transform(
np.int32((np.transpose(np.matrix(test_Y)) + 1) / 2)).toarray()
nr_vars = train_X.shape[1]
x = tf.placeholder("float", [None, nr_vars])
y_ = tf.placeholder("float", [None, 2])
# instance of Ws and bs
W_fc1 = weight_variable([nr_vars, 1024])
b_fc1 = bias_variable([1024])
W_fc3 = weight_variable([1024, 257])
b_fc3 = bias_variable([257])
W_fc2 = weight_variable([257, 2])
b_fc2 = bias_variable([2])
# operations
h_fc1 = tf.nn.relu(tf.matmul(x, W_fc1) + b_fc1)
# keep_prob = tf.placeholder("float");
# h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob);
# y = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2);
h_fc2 = tf.nn.relu(tf.matmul(h_fc1, W_fc3) + b_fc3)
y = tf.nn.softmax(tf.matmul(h_fc2, W_fc2) + b_fc2)
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(1e-3).minimize(
cross_entropy)
# train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
cur_id = 0
for i in range(20000):
print i
batch_xs, batch_ys, cur_id = sample(train_X, train_Y_onehot, cur_id,
256)
sess.run(train_step, feed_dict={
x: batch_xs,
y_: batch_ys
})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
# sess.run(tf.initialize_all_variables());
# cur_id = 0;
# for i in range(20000):
# batch_xs, batch_ys, cur_id = sample(train_X, train_Y_onehot, cur_id, 256);
# if i%100 == 0:
# train_accuracy = accuracy.eval(feed_dict={x: batch_xs, y_: batch_ys})
# print "step %d, training accuracy %f"%(i, train_accuracy)
# train_step.run(feed_dict={x: batch_xs, y_: batch_ys})
print "NN: train accuracy %f" % sess.run(accuracy,
feed_dict={
x: train_X,
y_: train_Y_onehot
})
print "NN: test accuracy %f" % sess.run(accuracy,
feed_dict={
x: test_X,
y_: test_Y_onehot
})
# auc = metrics.roc_auc_score(test_Y, pred_Y, average='macro', sample_weight=None);
# print "NN: area under the ROC curve %f"%auc;
# Stop time
stopTime = time.time()
print "Elapsed time (neural network): %f" % (stopTime - startTime)
