Apply minor fixes to README

PiperOrigin-RevId: 279101132

curl/README.md

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+# Continual Unsupervised Representation Learning (CURL)
+
+This repository contains code to accompany the NeurIPS 2019 submission on
+Continual Unsupervised Representation Learning (CURL).
+
+The experiments in the paper can be reproduced by running one of the three
+different training scripts:
+
+
+`train_sup.py`: to run the supervised continual learning benchmark
+
+`train_unsup.py`: to run the unsupervised i.i.d learning benchmark
+
+`train_main.py`: to run all other experiments in the paper (with details in the
+file on what to change)
+
+In each of these cases, the cluster accuracy / purity and k-NN error are logged
+to the terminal, and other quantities can be accessed from training.py
+(e.g. the confusion matrix can be found in `results['test_confusion']`).
+
+We recommend running these scripts in a Python
+[virtual environment](https://docs.python.org/3/tutorial/venv.html):
+
+(Assuming python3-dev is installed in your system)
+
+```console
+python3 -m venv .curl_venv
+source .curl_venv/bin/activate
+pip install wheel
+pip install -r requirements.txt
+
+PYTHONPATH=`pwd`/..:$PYTHONPATH python3 train_main.py --dataset='mnist'
+
+Run `deactivate` to exit the virtual environment.
+```

curl/layers.py

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+################################################################################
+# Copyright 2019 DeepMind Technologies Limited
+#
+#     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
+#
+#         https://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.
+################################################################################
+"""Custom layers for CURL."""
+
+from absl import logging
+import sonnet as snt
+import tensorflow as tf
+
+tfc = tf.compat.v1
+
+
+class ResidualStack(snt.AbstractModule):
+  """A stack of ResNet V2 blocks."""
+
+  def __init__(self,
+               num_hiddens,
+               num_residual_layers,
+               num_residual_hiddens,
+               filter_size=3,
+               initializers=None,
+               data_format='NHWC',
+               activation=tf.nn.relu,
+               name='residual_stack'):
+    """Instantiate a ResidualStack."""
+    super(ResidualStack, self).__init__(name=name)
+    self._num_hiddens = num_hiddens
+    self._num_residual_layers = num_residual_layers
+    self._num_residual_hiddens = num_residual_hiddens
+    self._filter_size = filter_size
+    self._initializers = initializers
+    self._data_format = data_format
+    self._activation = activation
+
+  def _build(self, h):
+    for i in range(self._num_residual_layers):
+      h_i = self._activation(h)
+
+      h_i = snt.Conv2D(
+          output_channels=self._num_residual_hiddens,
+          kernel_shape=(self._filter_size, self._filter_size),
+          stride=(1, 1),
+          initializers=self._initializers,
+          data_format=self._data_format,
+          name='res_nxn_%d' % i)(
+              h_i)
+      h_i = self._activation(h_i)
+
+      h_i = snt.Conv2D(
+          output_channels=self._num_hiddens,
+          kernel_shape=(1, 1),
+          stride=(1, 1),
+          initializers=self._initializers,
+          data_format=self._data_format,
+          name='res_1x1_%d' % i)(
+              h_i)
+      h += h_i
+    return self._activation(h)
+
+
+class SharedConvModule(snt.AbstractModule):
+  """Convolutional decoder."""
+
+  def __init__(self,
+               filters,
+               kernel_size,
+               activation,
+               strides,
+               name='shared_conv_encoder'):
+    super(SharedConvModule, self).__init__(name=name)
+
+    self._filters = filters
+    self._kernel_size = kernel_size
+    self._activation = activation
+    self.strides = strides
+    assert len(strides) == len(filters) - 1
+    self.conv_shapes = None
+
+  def _build(self, x, is_training=True):
+    with tf.control_dependencies([tfc.assert_rank(x, 4)]):
+
+      self.conv_shapes = [x.shape.as_list()]  # Needed by deconv module
+      conv = x
+    for i, (filter_i,
+            stride_i) in enumerate(zip(self._filters, self.strides), 1):
+      conv = tf.layers.Conv2D(
+          filters=filter_i,
+          kernel_size=self._kernel_size,
+          padding='same',
+          activation=self._activation,
+          strides=stride_i,
+          name='enc_conv_%d' % i)(
+              conv)
+      self.conv_shapes.append(conv.shape.as_list())
+    conv_flat = snt.BatchFlatten()(conv)
+
+    enc_mlp = snt.nets.MLP(
+        name='enc_mlp',
+        output_sizes=[self._filters[-1]],
+        activation=self._activation,
+        activate_final=True)
+    h = enc_mlp(conv_flat)
+
+    logging.info('Shared conv module layer shapes:')
+    logging.info('\n'.join([str(el) for el in self.conv_shapes]))
+    logging.info(h.shape.as_list())
+
+    return h