model.py

# 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 ray
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import time


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.")


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.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)

    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)]

    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})

    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)

    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})

    def set_weights(self, variable_names, weights):
        self.variables.set_weights(dict(zip(variable_names, weights)))

    def get_weights(self):
        weights = self.variables.get_weights()
        return list(weights.keys()), list(weights.values())


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


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)