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#!/usr/bin/env python
#
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A deep MNIST classifier using convolutional layers.
See extensive documentation at
https://www.tensorflow.org/get_started/mnist/pros
"""
# Disable linter warnings to maintain consistency with tutorial.
# pylint: disable=invalid-name
# pylint: disable=g-bad-import-order
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import time
import ray
from ray.tune import grid_search, run_experiments, register_trainable, \
Trainable, sample_from
from ray.tune.schedulers import HyperBandScheduler
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import numpy as np
activation_fn = None # e.g. tf.nn.relu
def setupCNN(x):
"""setupCNN builds the graph for a deep net for classifying digits.
Args:
x: an input tensor with the dimensions (N_examples, 784), where 784 is
the number of pixels in a standard MNIST image.
Returns:
A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with
values equal to the logits of classifying the digit into one of 10
classes (the digits 0-9). keep_prob is a scalar placeholder for the
probability of dropout.
"""
# Reshape to use within a convolutional neural net.
# Last dimension is for "features" - there is only one here, since images
# are grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
with tf.name_scope('reshape'):
x_image = tf.reshape(x, [-1, 28, 28, 1])
# First convolutional layer - maps one grayscale image to 32 feature maps.
with tf.name_scope('conv1'):
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = activation_fn(conv2d(x_image, W_conv1) + b_conv1)
# Pooling layer - downsamples by 2X.
with tf.name_scope('pool1'):
h_pool1 = max_pool_2x2(h_conv1)
# Second convolutional layer -- maps 32 feature maps to 64.
with tf.name_scope('conv2'):
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = activation_fn(conv2d(h_pool1, W_conv2) + b_conv2)
# Second pooling layer.
with tf.name_scope('pool2'):
h_pool2 = max_pool_2x2(h_conv2)
# Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
# is down to 7x7x64 feature maps -- maps this to 1024 features.
with tf.name_scope('fc1'):
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
h_fc1 = activation_fn(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
# Dropout - controls the complexity of the model, prevents co-adaptation of
# features.
with tf.name_scope('dropout'):
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
# Map the 1024 features to 10 classes, one for each digit
with tf.name_scope('fc2'):
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
return y_conv, keep_prob
def conv2d(x, W):
"""conv2d returns a 2d convolution layer with full stride."""
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
"""max_pool_2x2 downsamples a feature map by 2X."""
return tf.nn.max_pool(
x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
def weight_variable(shape):
"""weight_variable generates a weight variable of a given shape."""
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
"""bias_variable generates a bias variable of a given shape."""
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
class TrainMNIST(Trainable):
"""Example MNIST trainable."""
def _setup(self, config):
global activation_fn
self.timestep = 0
# Import data
for _ in range(10):
try:
self.mnist = input_data.read_data_sets(
"/tmp/mnist_ray_demo", one_hot=True)
break
except Exception as e:
print("Error loading data, retrying", e)
time.sleep(5)
assert self.mnist
self.x = tf.placeholder(tf.float32, [None, 784])
self.y_ = tf.placeholder(tf.float32, [None, 10])
activation_fn = getattr(tf.nn, config['activation'])
# Build the graph for the deep net
y_conv, self.keep_prob = setupCNN(self.x)
with tf.name_scope('loss'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
labels=self.y_, logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
with tf.name_scope('adam_optimizer'):
train_step = tf.train.AdamOptimizer(
config['learning_rate']).minimize(cross_entropy)
self.train_step = train_step
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(
tf.argmax(y_conv, 1), tf.argmax(self.y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
self.accuracy = tf.reduce_mean(correct_prediction)
self.sess = tf.Session()
self.sess.run(tf.global_variables_initializer())
self.iterations = 0
self.saver = tf.train.Saver()
def _train(self):
for i in range(10):
batch = self.mnist.train.next_batch(50)
self.sess.run(
self.train_step,
feed_dict={
self.x: batch[0],
self.y_: batch[1],
self.keep_prob: 0.5
})
batch = self.mnist.train.next_batch(50)
train_accuracy = self.sess.run(
self.accuracy,
feed_dict={
self.x: batch[0],
self.y_: batch[1],
self.keep_prob: 1.0
})
self.iterations += 1
return {"mean_accuracy": train_accuracy}
def _save(self, checkpoint_dir):
return self.saver.save(
self.sess, checkpoint_dir + "/save", global_step=self.iterations)
def _restore(self, path):
return self.saver.restore(self.sess, path)
# !!! Example of using the ray.tune Python API !!!
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--smoke-test', action='store_true', help='Finish quickly for testing')
args, _ = parser.parse_known_args()
register_trainable("my_class", TrainMNIST)
mnist_spec = {
'run': 'my_class',
'stop': {
'mean_accuracy': 0.99,
'time_total_s': 600,
},
'config': {
'learning_rate': sample_from(
lambda spec: 10**np.random.uniform(-5, -3)),
'activation': grid_search(['relu', 'elu', 'tanh']),
},
"num_samples": 10,
}
if args.smoke_test:
mnist_spec['stop']['training_iteration'] = 2
mnist_spec['num_samples'] = 2
ray.init()
hyperband = HyperBandScheduler(
time_attr="training_iteration", reward_attr="mean_accuracy", max_t=10)
run_experiments({'mnist_hyperband_test': mnist_spec}, scheduler=hyperband)