# This file is adapted from https://github.com/ray-project/ray/blob
# /master/examples/parameter_server/model.py
#
# Copyright 2018 Analytics Zoo Authors.
#
# 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.
# ==============================================================================
# Most of the tensorflow code is adapted from Tensorflow's tutorial on using
# CNNs to train MNIST
# https://www.tensorflow.org/get_started/mnist/pros#build-a-multilayer-convolutional-network. # noqa: E501
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import ray
import ray.experimental.tf_utils
[docs]def download_mnist_retry(seed=0, max_num_retries=20):
for _ in range(max_num_retries):
try:
return input_data.read_data_sets(
"MNIST_data", one_hot=True, seed=seed)
except tf.errors.AlreadyExistsError:
time.sleep(1)
raise Exception("Failed to download MNIST.")
[docs]class SimpleCNN(object):
def __init__(self, learning_rate=1e-4):
with tf.Graph().as_default():
# Create the model
self.x = tf.placeholder(tf.float32, [None, 784])
# Define loss and optimizer
self.y_ = tf.placeholder(tf.float32, [None, 10])
# Build the graph for the deep net
self.y_conv, self.keep_prob = deepnn(self.x)
with tf.name_scope("loss"):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
labels=self.y_, logits=self.y_conv)
self.cross_entropy = tf.reduce_mean(cross_entropy)
with tf.name_scope("adam_optimizer"):
self.optimizer = tf.train.AdamOptimizer(learning_rate)
self.train_step = self.optimizer.minimize(self.cross_entropy)
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(
tf.argmax(self.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(
config=tf.ConfigProto(
intra_op_parallelism_threads=1,
inter_op_parallelism_threads=1))
self.sess.run(tf.global_variables_initializer())
# Helper values.
self.variables = ray.experimental.tf_utils.TensorFlowVariables(
self.cross_entropy, self.sess)
self.grads = self.optimizer.compute_gradients(self.cross_entropy)
self.grads_placeholder = [(tf.placeholder(
"float", shape=grad[1].get_shape()), grad[1])
for grad in self.grads]
self.apply_grads_placeholder = self.optimizer.apply_gradients(
self.grads_placeholder)
[docs] def compute_update(self, x, y):
# TODO(rkn): Computing the weights before and after the training step
# and taking the diff is awful.
weights = self.get_weights()[1]
self.sess.run(
self.train_step,
feed_dict={
self.x: x,
self.y_: y,
self.keep_prob: 0.5
})
new_weights = self.get_weights()[1]
return [x - y for x, y in zip(new_weights, weights)]
[docs] def compute_gradients(self, x, y):
return self.sess.run(
[grad[0] for grad in self.grads],
feed_dict={
self.x: x,
self.y_: y,
self.keep_prob: 0.5
})
[docs] def apply_gradients(self, gradients):
feed_dict = {}
for i in range(len(self.grads_placeholder)):
feed_dict[self.grads_placeholder[i][0]] = gradients[i]
self.sess.run(self.apply_grads_placeholder, feed_dict=feed_dict)
[docs] def compute_accuracy(self, x, y):
return self.sess.run(
self.accuracy,
feed_dict={
self.x: x,
self.y_: y,
self.keep_prob: 1.0
})
[docs] def set_weights(self, variable_names, weights):
self.variables.set_weights(dict(zip(variable_names, weights)))
[docs] def get_weights(self):
weights = self.variables.get_weights()
return list(weights.keys()), list(weights.values())
[docs]def deepnn(x):
"""deepnn 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 = tf.nn.relu(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 = tf.nn.relu(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 = tf.nn.relu(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
[docs]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")
[docs]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")
[docs]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)
[docs]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)