hunk.code_window:     @override(TFPolicyGraph)
       def optimizer(self):
           return tf.train.AdamOptimizer(
               learning_rate=self.config["lr"],
               epsilon=self.config["adam_epsilon"])
       @override(TFPolicyGraph)
       def gradients(self, optimizer, loss):
           if self.config["grad_norm_clipping"] is not None:
  
hunk.labels: keep keep keep replace replace keep keep keep keep
hunk.after_edit:             learning_rate=self.cur_lr, epsilon=self.config["adam_epsilon"])
  
hunk.file_path: python/ray/rllib/agents/dqn/dqn_policy_graph.py
hunk.type: replace
file: #!/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 import tune
  from ray.tune import grid_search, 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):
          prefix = self.saver.save(
              self.sess, checkpoint_dir + "/save", global_step=self.iterations)
          return {"prefix": prefix}
  
      def _restore(self, ckpt_data):
          prefix = ckpt_data["prefix"]
          return self.saver.restore(self.sess, prefix)
  
  
  # !!! 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()
      mnist_spec = {
          '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'] = 20
          mnist_spec['num_samples'] = 2
  
      ray.init()
      hyperband = HyperBandScheduler(
          time_attr="training_iteration", reward_attr="mean_accuracy", max_t=10)
  
      tune.run(
          TrainMNIST,
          name='mnist_hyperband_test',
          scheduler=hyperband,
          **mnist_spec)
  