Merge pull request #151 from amplab/hyper

Hyper-parameter Optimization code

examples/hyperopt/driver.py

@@ -0,0 +1,39 @@
+import numpy as np
+import ray
+import ray.services as services
+import os
+
+import functions
+
+num_workers = 3
+samples = 50
+epochs = 100
+
+worker_dir = os.path.dirname(os.path.abspath(__file__))
+worker_path = os.path.join(worker_dir, "worker.py")
+services.start_singlenode_cluster(return_drivers=False, num_objstores=1, num_workers_per_objstore=num_workers, worker_path=worker_path)
+
+best_params = None
+best_accuracy = 0
+
+results = []
+
+for i in range(samples):
+  learning_rate = 10 ** np.random.uniform(-6, 1)
+  batch_size = np.random.randint(30, 100)
+  dropout = np.random.uniform(0, 1)
+  stddev = 10 ** np.random.uniform(-3, 1)
+  randparams = {"learning_rate": learning_rate, "batch_size": batch_size, "dropout": dropout, "stddev": stddev}
+  results.append((randparams, functions.train_cnn(randparams, epochs)))
+
+for i in range(samples):
+  params, ref = results[i]
+  accuracy = ray.get(ref)
+  print "With hyperparameters {}, we achieve an accuracy of {:.4}%.".format(params, 100 * accuracy)
+  if accuracy > best_accuracy:
+    best_params = params
+    best_accuracy = accuracy
+    print "Best parameters are now {}.".format(params)
+
+print "Best parameters over {} samples was {}, with an accuracy of {:.4}%.".format(samples, best_params, 100 * best_accuracy)
+services.cleanup()

examples/hyperopt/functions.py

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+# Most of the tensorflow code is adapted from Tensorflow's tutorial on using CNNs to train MNIST
+# https://www.tensorflow.org/versions/r0.9/tutorials/mnist/pros/index.html#build-a-multilayer-convolutional-network
+import tensorflow as tf
+from tensorflow.examples.tutorials.mnist import input_data
+import numpy as np
+import ray
+
+mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
+
+def weight(shape, stddev):
+  initial = tf.truncated_normal(shape, stddev=stddev)
+  return tf.Variable(initial)
+
+def bias(shape):
+  initial = tf.constant(0.1, shape=shape)
+  return tf.Variable(initial)
+
+def conv2d(x, W):
+  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")
+
+def max_pool_2x2(x):
+  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
+
+@ray.remote([dict, int], [float])
+def train_cnn(params, epochs):
+  learning_rate = params["learning_rate"]
+  batch_size = params["batch_size"]
+  keep = 1 - params["dropout"]
+  stddev = params["stddev"]
+  x = tf.placeholder(tf.float32, shape=[None, 784])
+  y = tf.placeholder(tf.float32, shape=[None, 10])
+  keep_prob = tf.placeholder(tf.float32)
+  train_step, accuracy = cnn_setup(x, y, keep_prob, learning_rate, stddev)
+  with tf.Session() as sess:
+    sess.run(tf.initialize_all_variables())
+    for i in range(1, epochs):
+      batch = mnist.train.next_batch(batch_size)
+      sess.run(train_step, feed_dict={x: batch[0], y: batch[1], keep_prob: keep})
+      if i % 100 == 0: # checks if accuracy is low enough to stop early every set number of epochs
+        train_ac = accuracy.eval(feed_dict={x: batch[0], y: batch[1], keep_prob: 1.0})
+        if train_ac < 0.25: # Accuracy threshold is on a application to application basis. 
+          totalacc = accuracy.eval(feed_dict={x: mnist.validation.images, y: mnist.validation.labels, keep_prob: 1.0})
+          return totalacc
+    totalacc = accuracy.eval(feed_dict={x: mnist.validation.images, y: mnist.validation.labels, keep_prob: 1.0})
+  return totalacc
+  
+def cnn_setup(x, y, keep_prob, lr, stddev):
+  first_hidden = 32
+  second_hidden = 64
+  fc_hidden = 1024
+  W_conv1 = weight([5, 5, 1, first_hidden], stddev)
+  B_conv1 = bias([first_hidden])
+  x_image = tf.reshape(x, [-1, 28, 28, 1])
+  h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + B_conv1)
+  h_pool1 = max_pool_2x2(h_conv1)
+  W_conv2 = weight([5, 5, first_hidden, second_hidden], stddev)
+  b_conv2 = bias([second_hidden])
+  h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
+  h_pool2 = max_pool_2x2(h_conv2)
+  W_fc1 = weight([7 * 7 * second_hidden, fc_hidden], stddev)
+  b_fc1 = bias([fc_hidden])
+  h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * second_hidden])
+  h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
+  h_fc1_drop= tf.nn.dropout(h_fc1, keep_prob)
+  W_fc2 = weight([fc_hidden, 10], stddev)
+  b_fc2 = bias([10])
+  y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
+  cross_entropy = tf.reduce_mean(-tf.reduce_sum(y * tf.log(y_conv), reduction_indices=[1]))
+  correct_pred = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y, 1))
+  return tf.train.AdamOptimizer(lr).minimize(cross_entropy), tf.reduce_mean(tf.cast(correct_pred, tf.float32))

examples/hyperopt/worker.py

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+import argparse
+import ray
+import ray.worker as worker
+
+import functions
+
+parser = argparse.ArgumentParser(description="Parse addresses for the worker to connect to.")
+parser.add_argument("--scheduler-address", default="127.0.0.1:10001", type=str, help="the scheduler's address")
+parser.add_argument("--objstore-address", default="127.0.0.1:20001", type=str, help="the objstore's address")
+parser.add_argument("--worker-address", default="127.0.0.1:40001", type=str, help="the worker's address")
+
+if __name__ == '__main__':
+  args = parser.parse_args()
+  ray.connect(args.scheduler_address, args.objstore_address, args.worker_address)
+  ray.register_module(functions)
+  worker.main_loop()