Merge branch 'master' of github.com:tensorflow/models

im2txt/README.md

@@ -145,7 +145,8 @@ available space for storing the downloaded and processed data.
 MSCOCO_DIR="${HOME}/im2txt/data/mscoco"
 
 # Build the preprocessing script.
-bazel build im2txt/download_and_preprocess_mscoco
+cd tensorflow-models/im2txt
+bazel build //im2txt:download_and_preprocess_mscoco
 
 # Run the preprocessing script.
 bazel-bin/im2txt/download_and_preprocess_mscoco "${MSCOCO_DIR}"
@@ -211,7 +212,8 @@ INCEPTION_CHECKPOINT="${HOME}/im2txt/data/inception_v3.ckpt"
 MODEL_DIR="${HOME}/im2txt/model"
 
 # Build the model.
-bazel build -c opt im2txt/...
+cd tensorflow-models/im2txt
+bazel build -c opt //im2txt/...
 
 # Run the training script.
 bazel-bin/im2txt/train \
@@ -304,7 +306,8 @@ VOCAB_FILE="${HOME}/im2txt/data/mscoco/word_counts.txt"
 IMAGE_FILE="${HOME}/im2txt/data/mscoco/raw-data/val2014/COCO_val2014_000000224477.jpg"
 
 # Build the inference binary.
-bazel build -c opt im2txt/run_inference
+cd tensorflow-models/im2txt
+bazel build -c opt //im2txt:run_inference
 
 # Ignore GPU devices (only necessary if your GPU is currently memory
 # constrained, for example, by running the training script).

inception/README.md

@@ -86,7 +86,8 @@ you will not need to interact with the script again.
 DATA_DIR=$HOME/imagenet-data
 
 # build the preprocessing script.
-bazel build inception/download_and_preprocess_imagenet
+cd tensorflow-models/inception
+bazel build //inception:download_and_preprocess_imagenet
 
 # run it
 bazel-bin/inception/download_and_preprocess_imagenet "${DATA_DIR}"
@@ -153,7 +154,8 @@ To train this model, you simply need to specify the following:
 ```shell
 # Build the model. Note that we need to make sure the TensorFlow is ready to
 # use before this as this command will not build TensorFlow.
-bazel build inception/imagenet_train
+cd tensorflow-models/inception
+bazel build //inception:imagenet_train
 
 # run it
 bazel-bin/inception/imagenet_train --num_gpus=1 --batch_size=32 --train_dir=/tmp/imagenet_train --data_dir=/tmp/imagenet_data
@@ -189,7 +191,8 @@ GPU cards.
 ```shell
 # Build the model. Note that we need to make sure the TensorFlow is ready to
 # use before this as this command will not build TensorFlow.
-bazel build inception/imagenet_train
+cd tensorflow-models/inception
+bazel build //inception:imagenet_train
 
 # run it
 bazel-bin/inception/imagenet_train --num_gpus=2 --batch_size=64 --train_dir=/tmp/imagenet_train
@@ -288,7 +291,8 @@ running. Several things to note here:
 ```shell
 # Build the model. Note that we need to make sure the TensorFlow is ready to
 # use before this as this command will not build TensorFlow.
-bazel build inception/imagenet_distributed_train
+cd tensorflow-models/inception
+bazel build //inception:imagenet_distributed_train
 
 # To start worker 0, go to the worker0 host and run the following (Note that
 # task_id should be in the range [0, num_worker_tasks):
@@ -395,7 +399,8 @@ Briefly, one can evaluate the model by running:
 ```shell
 # Build the model. Note that we need to make sure the TensorFlow is ready to
 # use before this as this command will not build TensorFlow.
-bazel build inception/imagenet_eval
+cd tensorflow-models/inception
+bazel build //inception:imagenet_eval
 
 # run it
 bazel-bin/inception/imagenet_eval --checkpoint_dir=/tmp/imagenet_train --eval_dir=/tmp/imagenet_eval
@@ -450,7 +455,8 @@ but feel free to edit accordingly.
 FLOWERS_DATA_DIR=/tmp/flowers-data/
 
 # build the preprocessing script.
-bazel build inception/download_and_preprocess_flowers
+cd tensorflow-models/inception
+bazel build //inception:download_and_preprocess_flowers
 
 # run it
 bazel-bin/inception/download_and_preprocess_flowers "${FLOWERS_DATA_DIR}"
@@ -530,7 +536,8 @@ the flowers data set with the following command.
 ```shell
 # Build the model. Note that we need to make sure the TensorFlow is ready to
 # use before this as this command will not build TensorFlow.
-bazel build inception/flowers_train
+cd tensorflow-models/inception
+bazel build //inception:flowers_train
 
 # Path to the downloaded Inception-v3 model.
 MODEL_PATH="${INCEPTION_MODEL_DIR}/inception-v3/model.ckpt-157585"
@@ -566,7 +573,8 @@ fine-tuned model, you will need to run `flowers_eval`:
 ```shell
 # Build the model. Note that we need to make sure the TensorFlow is ready to
 # use before this as this command will not build TensorFlow.
-bazel build inception/flowers_eval
+cd tensorflow-models/inception
+bazel build //inception:flowers_eval
 
 # Directory where we saved the fine-tuned checkpoint and events files.
 TRAIN_DIR=/tmp/flowers_train/
@@ -654,7 +662,8 @@ To run `build_image_data.py`, you can run the following command line:
 OUTPUT_DIRECTORY=$HOME/my-custom-data/
 
 # build the preprocessing script.
-bazel build inception/build_image_data
+cd tensorflow-models/inception
+bazel build //inception:build_image_data
 
 # convert the data.
 bazel-bin/inception/build_image_data \

inception/inception/data/download_imagenet.sh

@@ -40,7 +40,6 @@ fi
 
 OUTDIR="${1:-./imagenet-data}"
 SYNSETS_FILE="${2:-./synsets.txt}"
-SYNSETS_FILE="${PWD}/${SYNSETS_FILE}"
 
 echo "Saving downloaded files to $OUTDIR"
 mkdir -p "${OUTDIR}"

inception/inception/data/preprocess_imagenet_validation_data.py

@@ -76,7 +76,7 @@ if __name__ == '__main__':
     basename = 'ILSVRC2012_val_000%.5d.JPEG' % (i + 1)
     original_filename = os.path.join(data_dir, basename)
     if not os.path.exists(original_filename):
-      print('Failed to find: ' % original_filename)
+      print('Failed to find: %s' % original_filename)
       sys.exit(-1)
     new_filename = os.path.join(data_dir, labels[i], basename)
     os.rename(original_filename, new_filename)

inception/inception/imagenet_eval.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""A binary to evaluate Inception on the flowers data set.
+"""A binary to evaluate Inception on the ImageNet data set.
 
 Note that using the supplied pre-trained inception checkpoint, the eval should
 achieve:

inception/inception/slim/ops_test.py

@@ -21,8 +21,6 @@ from __future__ import print_function
 import numpy as np
 import tensorflow as tf
 
-from tensorflow.python.ops import control_flow_ops
-
 from inception.slim import ops
 from inception.slim import scopes
 from inception.slim import variables
@@ -420,7 +418,7 @@ class DropoutTest(tf.test.TestCase):
     with self.test_session():
       images = tf.random_uniform((5, height, width, 3), seed=1)
       output = ops.dropout(images)
-      self.assertEquals(output.op.name, 'Dropout/dropout/mul_1')
+      self.assertEquals(output.op.name, 'Dropout/dropout/mul')
       output.get_shape().assert_is_compatible_with(images.get_shape())
 
   def testCreateDropoutNoTraining(self):
@@ -601,8 +599,7 @@ class BatchNormTest(tf.test.TestCase):
       output = ops.batch_norm(images, decay=0.1)
       update_ops = tf.get_collection(ops.UPDATE_OPS_COLLECTION)
       with tf.control_dependencies(update_ops):
-        barrier = tf.no_op(name='gradient_barrier')
-        output = control_flow_ops.with_dependencies([barrier], output)
+        output = tf.identity(output)
       # Initialize all variables
       sess.run(tf.global_variables_initializer())
       moving_mean = variables.get_variables('BatchNorm/moving_mean')[0]
@@ -631,8 +628,7 @@ class BatchNormTest(tf.test.TestCase):
       output = ops.batch_norm(images, decay=0.1, is_training=False)
       update_ops = tf.get_collection(ops.UPDATE_OPS_COLLECTION)
       with tf.control_dependencies(update_ops):
-        barrier = tf.no_op(name='gradient_barrier')
-        output = control_flow_ops.with_dependencies([barrier], output)
+        output = tf.identity(output)
       # Initialize all variables
       sess.run(tf.global_variables_initializer())
       moving_mean = variables.get_variables('BatchNorm/moving_mean')[0]
@@ -665,8 +661,7 @@ class BatchNormTest(tf.test.TestCase):
       output = ops.batch_norm(images, decay=0.1, is_training=False)
       update_ops = tf.get_collection(ops.UPDATE_OPS_COLLECTION)
       with tf.control_dependencies(update_ops):
-        barrier = tf.no_op(name='gradient_barrier')
-        output = control_flow_ops.with_dependencies([barrier], output)
+        output = tf.identity(output)
       # Initialize all variables
       sess.run(tf.global_variables_initializer())
       moving_mean = variables.get_variables('BatchNorm/moving_mean')[0]

lfads/README.md

+# LFADS - Latent Factor Analysis via Dynamical Systems
+
+This code implements the model from the paper "[LFADS - Latent Factor Analysis via Dynamical Systems](http://biorxiv.org/content/early/2017/06/20/152884)". It is a sequential variational auto-encoder designed specifically for investigating neuroscience data, but can be applied widely to any time series data. In an unsupervised setting, LFADS is able to decompose time series data into various factors, such as an initial condition, a generative dynamical system, control inputs to that generator, and a low dimensional description of the observed data, called the factors. Additionally, the observation model is a loss on a probability distribution, so when LFADS processes a dataset, a denoised version of the dataset is also created. For example, if the dataset is raw spike counts, then under the negative log-likeihood loss under a Poisson distribution, the denoised data would be the inferred Poisson rates.
+
+
+## Prerequisites
+
+The code is written in Python 2.7.6. You will also need:
+
+* **TensorFlow** version 1.1 ([install](http://tflearn.org/installation/)) -
+  there is an incompatibility with LFADS and TF v1.2, which we are in the
+  process of resolving
+* **NumPy, SciPy, Matplotlib** ([install SciPy stack](https://www.scipy.org/install.html), contains all of them)
+* **h5py** ([install](https://pypi.python.org/pypi/h5py))
+
+
+## Getting started
+
+Before starting, run the following:
+
+<pre>
+$ export PYTHONPATH=$PYTHONPATH:/<b>path/to/your/directory</b>/lfads/
+</pre>
+
+where "path/to/your/directory" is replaced with the path to the LFADS repository (you can get this path by using the `pwd` command). This allows the nested directories to access modules from their parent directory.
+
+## Generate synthetic data
+
+In order to generate the synthetic datasets first, from the top-level lfads directory, run:
+
+```sh
+$ cd synth_data
+$ ./run_generate_synth_data.sh
+$ cd ..
+```
+
+These synthetic datasets are provided 1. to gain insight into how the LFADS algorithm operates, and 2. to give reasonable starting points for analyses you might be interested for your own data.
+
+## Train an LFADS model
+
+Now that we have our example datasets, we can train some models! To spin up an LFADS model on the synthetic data, run any of the following commands. For the examples that are in the paper, the important hyperparameters are roughly replicated. Most hyperparameters are insensitive to small changes or won't ever be changed unless you want a very fine level of control. In the first example, all hyperparameter flags are enumerated for easy copy-pasting, but for the rest of the examples only the most important flags (~the first 8) are specified for brevity. For a full list of flags, their descriptions, and their default values, refer to the top of `run_lfads.py`.  Please see Table 1 in the Online Methods of the associated paper for definitions of the most important hyperparameters.
+
+```sh
+# Run LFADS on chaotic rnn data with no input pulses (g = 1.5)
+$ python run_lfads.py --kind=train \
+--data_dir=/tmp/rnn_synth_data_v1.0/ \
+--data_filename_stem=chaotic_rnn_no_inputs \
+--lfads_save_dir=/tmp/lfads_chaotic_rnn_no_inputs \
+--co_dim=0 \
+--factors_dim=20 \
+--ext_input_dim=0 \
+--controller_input_lag=1 \
+--output_dist=poisson \
+--do_causal_controller=false \
+--batch_size=128 \
+--learning_rate_init=0.01 \
+--learning_rate_stop=1e-05 \
+--learning_rate_decay_factor=0.95 \
+--learning_rate_n_to_compare=6 \
+--do_reset_learning_rate=false \
+--keep_prob=0.95 \
+--con_dim=128 \
+--gen_dim=200 \
+--ci_enc_dim=128 \
+--ic_dim=64 \
+--ic_enc_dim=128 \
+--ic_prior_var_min=0.1 \
+--gen_cell_input_weight_scale=1.0 \
+--cell_weight_scale=1.0 \
+--do_feed_factors_to_controller=true \
+--kl_start_step=0 \
+--kl_increase_steps=2000 \
+--kl_ic_weight=1.0 \
+--l2_con_scale=0.0 \
+--l2_gen_scale=2000.0 \
+--l2_start_step=0 \
+--l2_increase_steps=2000 \
+--ic_prior_var_scale=0.1 \
+--ic_post_var_min=0.0001 \
+--kl_co_weight=1.0 \
+--prior_ar_nvar=0.1 \
+--cell_clip_value=5.0 \
+--max_ckpt_to_keep_lve=5 \
+--do_train_prior_ar_atau=true \
+--co_prior_var_scale=0.1 \
+--csv_log=fitlog \
+--feedback_factors_or_rates=factors \
+--do_train_prior_ar_nvar=true \
+--max_grad_norm=200.0 \
+--device=gpu:0 \
+--num_steps_for_gen_ic=100000000 \
+--ps_nexamples_to_process=100000000 \
+--checkpoint_name=lfads_vae \
+--temporal_spike_jitter_width=0 \
+--checkpoint_pb_load_name=checkpoint \
+--inject_ext_input_to_gen=false \
+--co_mean_corr_scale=0.0 \
+--gen_cell_rec_weight_scale=1.0 \
+--max_ckpt_to_keep=5 \
+--output_filename_stem="" \
+--ic_prior_var_max=0.1 \
+--prior_ar_atau=10.0 \
+--do_train_io_only=false
+
+# Run LFADS on chaotic rnn data with input pulses (g = 2.5)
+$ python run_lfads.py --kind=train \
+--data_dir=/tmp/rnn_synth_data_v1.0/ \
+--data_filename_stem=chaotic_rnn_inputs_g2p5 \
+--lfads_save_dir=/tmp/lfads_chaotic_rnn_inputs_g2p5 \
+--co_dim=1 \
+--factors_dim=20
+
+# Run LFADS on multi-session RNN data
+$ python run_lfads.py --kind=train \
+--data_dir=/tmp/rnn_synth_data_v1.0/ \
+--data_filename_stem=chaotic_rnn_multisession \
+--lfads_save_dir=/tmp/lfads_chaotic_rnn_multisession \
+--factors_dim=10
+
+# Run LFADS on integration to bound model data
+$ python run_lfads.py --kind=train \
+--data_dir=/tmp/rnn_synth_data_v1.0/ \
+--data_filename_stem=itb_rnn \
+--lfads_save_dir=/tmp/lfads_itb_rnn \
+--co_dim=1 \
+--factors_dim=20 \
+--controller_input_lag=0
+
+# Run LFADS on chaotic RNN data with labels
+$ python run_lfads.py --kind=train \
+--data_dir=/tmp/rnn_synth_data_v1.0/ \
+--data_filename_stem=chaotic_rnns_labeled \
+--lfads_save_dir=/tmp/lfads_chaotic_rnns_labeled \
+--co_dim=0 \
+--factors_dim=20 \
+--controller_input_lag=0 \
+--ext_input_dim=1
+
+```
+
+**Tip**: If you are running LFADS on GPU and would like to run more than one model concurrently, set the `--allow_gpu_growth=True` flag on each job, otherwise one model will take up the entire GPU for performance purposes.  Also, one needs to install the TensorFlow libraries with GPU support.
+
+
+## Visualize a training model
+
+To visualize training curves and various other metrics while training and LFADS model, run the following command on your model directory. To launch a tensorboard on the chaotic RNN data with input pulses, for example:
+
+```sh
+tensorboard --logdir=/tmp/lfads_chaotic_rnn_inputs_g2p5
+```
+
+## Evaluate a trained model
+
+Once your model is finished training, there are multiple ways you can evaluate
+it. Below are some sample commands to evaluate an LFADS model trained on the
+chaotic rnn data with input pulses (g = 2.5). The key differences here are
+setting the `--kind` flag to the appropriate mode, as well as the
+`--checkpoint_pb_load_name` flag to `checkpoint_lve` and the `--batch_size` flag
+(if you'd like to make it larger or smaller). All other flags should be the
+same as used in training, so that the same model architecture is built.
+
+```sh
+# Take samples from posterior then average (denoising operation)
+$ python run_lfads.py --kind=posterior_sample_and_average \
+--data_dir=/tmp/rnn_synth_data_v1.0/ \
+--data_filename_stem=chaotic_rnn_inputs_g2p5 \
+--lfads_save_dir=/tmp/lfads_chaotic_rnn_inputs_g2p5 \
+--co_dim=1 \
+--factors_dim=20 \
+--batch_size=1024 \
+--checkpoint_pb_load_name=checkpoint_lve
+
+# Sample from prior (generation of completely new samples)
+$ python run_lfads.py --kind=prior_sample \
+--data_dir=/tmp/rnn_synth_data_v1.0/ \
+--data_filename_stem=chaotic_rnn_inputs_g2p5 \
+--lfads_save_dir=/tmp/lfads_chaotic_rnn_inputs_g2p5 \
+--co_dim=1 \
+--factors_dim=20 \
+--batch_size=50 \
+--checkpoint_pb_load_name=checkpoint_lve
+
+# Write down model parameters
+$ python run_lfads.py --kind=write_model_params \
+--data_dir=/tmp/rnn_synth_data_v1.0/ \
+--data_filename_stem=chaotic_rnn_inputs_g2p5 \
+--lfads_save_dir=/tmp/lfads_chaotic_rnn_inputs_g2p5 \
+--co_dim=1 \
+--factors_dim=20 \
+--checkpoint_pb_load_name=checkpoint_lve
+```
+
+## Contact
+
+File any issues with the [issue tracker](https://github.com/tensorflow/models/issues). For any questions or problems, this code is maintained by [@sussillo](https://github.com/sussillo) and [@jazcollins](https://github.com/jazcollins).
+

lfads/distributions.py

+# Copyright 2017 Google Inc. 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.
+#
+# ==============================================================================
+import numpy as np
+import tensorflow as tf
+from utils import linear, log_sum_exp
+
+class Poisson(object):
+  """Poisson distributon
+
+  Computes the log probability under the model.
+
+  """
+  def __init__(self, log_rates):
+    """ Create Poisson distributions with log_rates parameters.
+
+    Args:
+      log_rates: a tensor-like list of log rates underlying the Poisson dist.
+    """
+    self.logr = log_rates
+
+  def logp(self, bin_counts):
+    """Compute the log probability for the counts in the bin, under the model.
+
+    Args:
+      bin_counts: array-like integer counts
+
+    Returns:
+      The log-probability under the Poisson models for each element of
+      bin_counts.
+    """
+    k = tf.to_float(bin_counts)
+    # log poisson(k, r) = log(r^k * e^(-r) / k!) = k log(r) - r - log k!
+    # log poisson(k, r=exp(x)) = k * x - exp(x) - lgamma(k + 1)
+    return k * self.logr - tf.exp(self.logr) - tf.lgamma(k + 1)
+
+
+def diag_gaussian_log_likelihood(z, mu=0.0, logvar=0.0):
+  """Log-likelihood under a Gaussian distribution with diagonal covariance.
+    Returns the log-likelihood for each dimension.  One should sum the
+    results for the log-likelihood under the full multidimensional model.
+
+  Args:
+    z: The value to compute the log-likelihood.
+    mu: The mean of the Gaussian
+    logvar: The log variance of the Gaussian.
+
+  Returns:
+    The log-likelihood under the Gaussian model.
+  """
+
+  return -0.5 * (logvar + np.log(2*np.pi) + \
+                 tf.square((z-mu)/tf.exp(0.5*logvar)))
+
+
+def gaussian_pos_log_likelihood(unused_mean, logvar, noise):
+  """Gaussian log-likelihood function for a posterior in VAE
+
+  Note: This function is specialized for a posterior distribution, that has the
+  form of z = mean + sigma * noise.
+
+  Args:
+    unused_mean: ignore
+    logvar: The log variance of the distribution
+    noise: The noise used in the sampling of the posterior.
+
+  Returns:
+    The log-likelihood under the Gaussian model.
+  """
+  # ln N(z; mean, sigma) = - ln(sigma) - 0.5 ln 2pi - noise^2 / 2
+  return - 0.5 * (logvar + np.log(2 * np.pi) + tf.square(noise))
+
+
+class Gaussian(object):
+  """Base class for Gaussian distribution classes."""
+  pass
+
+
+class DiagonalGaussian(Gaussian):
+  """Diagonal Gaussian with different constant mean and variances in each
+  dimension.
+  """
+
+  def __init__(self, batch_size, z_size, mean, logvar):
+    """Create a diagonal gaussian distribution.
+
+    Args:
+      batch_size: The size of the batch, i.e. 0th dim in 2D tensor of samples.
+      z_size: The dimension of the distribution, i.e. 1st dim in 2D tensor.
+      mean: The N-D mean of the distribution.
+      logvar: The N-D log variance of the diagonal distribution.
+    """
+    size__xz = [None, z_size]
+    self.mean = mean            # bxn already
+    self.logvar = logvar        # bxn already
+    self.noise = noise = tf.random_normal(tf.shape(logvar))
+    self.sample = mean + tf.exp(0.5 * logvar) * noise
+    mean.set_shape(size__xz)
+    logvar.set_shape(size__xz)
+    self.sample.set_shape(size__xz)
+
+  def logp(self, z=None):
+    """Compute the log-likelihood under the distribution.
+
+    Args:
+      z (optional): value to compute likelihood for, if None, use sample.
+
+    Returns:
+      The likelihood of z under the model.
+    """
+    if z is None:
+      z = self.sample
+
+    # This is needed to make sure that the gradients are simple.
+    # The value of the function shouldn't change.
+    if z == self.sample:
+      return gaussian_pos_log_likelihood(self.mean, self.logvar, self.noise)
+
+    return diag_gaussian_log_likelihood(z, self.mean, self.logvar)
+
+
+class LearnableDiagonalGaussian(Gaussian):
+  """Diagonal Gaussian whose mean and variance are learned parameters."""
+
+  def __init__(self, batch_size, z_size, name, mean_init=0.0,
+               var_init=1.0, var_min=0.0, var_max=1000000.0):
+    """Create a learnable diagonal gaussian distribution.
+
+    Args:
+      batch_size: The size of the batch, i.e. 0th dim in 2D tensor of samples.
+      z_size: The dimension of the distribution, i.e. 1st dim in 2D tensor.
+      name: prefix name for the mean and log TF variables.
+      mean_init (optional): The N-D mean initialization of the distribution.
+      var_init (optional): The N-D variance initialization of the diagonal
+        distribution.
+      var_min (optional): The minimum value the learned variance can take in any
+        dimension.
+      var_max (optional): The maximum value the learned variance can take in any
+        dimension.
+    """
+
+    size_1xn = [1, z_size]
+    size__xn = [None, z_size]
+    size_bx1 = tf.stack([batch_size, 1])
+    assert var_init > 0.0, "Problems"
+    assert var_max >= var_min, "Problems"
+    assert var_init >= var_min, "Problems"
+    assert var_max >= var_init, "Problems"
+
+
+    z_mean_1xn = tf.get_variable(name=name+"/mean", shape=size_1xn,
+                                 initializer=tf.constant_initializer(mean_init))
+    self.mean_bxn = mean_bxn = tf.tile(z_mean_1xn, size_bx1)
+    mean_bxn.set_shape(size__xn) # tile loses shape
+
+    log_var_init = np.log(var_init)
+    if var_max > var_min:
+      var_is_trainable = True
+    else:
+      var_is_trainable = False
+
+    z_logvar_1xn = \
+        tf.get_variable(name=(name+"/logvar"), shape=size_1xn,
+                        initializer=tf.constant_initializer(log_var_init),
+                        trainable=var_is_trainable)
+
+    if var_is_trainable:
+      z_logit_var_1xn = tf.exp(z_logvar_1xn)
+      z_var_1xn = tf.nn.sigmoid(z_logit_var_1xn)*(var_max-var_min) + var_min
+      z_logvar_1xn = tf.log(z_var_1xn)
+
+    logvar_bxn = tf.tile(z_logvar_1xn, size_bx1)
+    self.logvar_bxn = logvar_bxn
+    self.noise_bxn = noise_bxn = tf.random_normal(tf.shape(logvar_bxn))
+    self.sample_bxn = mean_bxn + tf.exp(0.5 * logvar_bxn) * noise_bxn
+
+  def logp(self, z=None):
+    """Compute the log-likelihood under the distribution.
+
+    Args:
+      z (optional): value to compute likelihood for, if None, use sample.
+
+    Returns:
+      The likelihood of z under the model.
+    """
+    if z is None:
+      z = self.sample
+
+    # This is needed to make sure that the gradients are simple.
+    # The value of the function shouldn't change.
+    if z == self.sample_bxn:
+      return gaussian_pos_log_likelihood(self.mean_bxn, self.logvar_bxn,
+                                         self.noise_bxn)
+
+    return diag_gaussian_log_likelihood(z, self.mean_bxn, self.logvar_bxn)
+
+  @property
+  def mean(self):
+    return self.mean_bxn
+
+  @property
+  def logvar(self):
+    return self.logvar_bxn
+
+  @property
+  def sample(self):
+    return self.sample_bxn
+
+
+class DiagonalGaussianFromInput(Gaussian):
+  """Diagonal Gaussian whose mean and variance are conditioned on other
+  variables.
+
+  Note: the parameters to convert from input to the learned mean and log
+  variance are held in this class.
+  """
+
+  def __init__(self, x_bxu, z_size, name, var_min=0.0):
+    """Create an input dependent diagonal Gaussian distribution.
+
+    Args:
+      x: The input tensor from which the mean and variance are computed,
+        via a linear transformation of x.  I.e.
+          mu = Wx + b, log(var) = Mx + c
+      z_size: The size of the distribution.
+      name:  The name to prefix to learned variables.
+      var_min (optional): Minimal variance allowed.  This is an additional
+        way to control the amount of information getting through the stochastic
+        layer.
+    """
+    size_bxn = tf.stack([tf.shape(x_bxu)[0], z_size])
+    self.mean_bxn = mean_bxn = linear(x_bxu, z_size, name=(name+"/mean"))
+    logvar_bxn = linear(x_bxu, z_size, name=(name+"/logvar"))
+    if var_min > 0.0:
+      logvar_bxn = tf.log(tf.exp(logvar_bxn) + var_min)
+    self.logvar_bxn = logvar_bxn
+
+    self.noise_bxn = noise_bxn = tf.random_normal(size_bxn)
+    self.noise_bxn.set_shape([None, z_size])
+    self.sample_bxn = mean_bxn + tf.exp(0.5 * logvar_bxn) * noise_bxn
+
+  def logp(self, z=None):
+    """Compute the log-likelihood under the distribution.
+
+    Args:
+      z (optional): value to compute likelihood for, if None, use sample.
+
+    Returns:
+      The likelihood of z under the model.
+    """
+
+    if z is None:
+      z = self.sample
+
+    # This is needed to make sure that the gradients are simple.
+    # The value of the function shouldn't change.
+    if z == self.sample_bxn:
+      return gaussian_pos_log_likelihood(self.mean_bxn,
+                                         self.logvar_bxn, self.noise_bxn)
+
+    return diag_gaussian_log_likelihood(z, self.mean_bxn, self.logvar_bxn)
+
+  @property
+  def mean(self):
+    return self.mean_bxn
+
+  @property
+  def logvar(self):
+    return self.logvar_bxn
+
+  @property
+  def sample(self):
+    return self.sample_bxn
+
+
+class GaussianProcess:
+  """Base class for Gaussian processes."""
+  pass
+
+
+class LearnableAutoRegressive1Prior(GaussianProcess):
+  """AR(1) model where autocorrelation and process variance are learned
+  parameters.  Assumed zero mean.
+
+  """
+
+  def __init__(self, batch_size, z_size,
+               autocorrelation_taus, noise_variances,
+               do_train_prior_ar_atau, do_train_prior_ar_nvar,
+               num_steps, name):
+    """Create a learnable autoregressive (1) process.
+
+    Args:
+      batch_size: The size of the batch, i.e. 0th dim in 2D tensor of samples.
+      z_size: The dimension of the distribution, i.e. 1st dim in 2D tensor.
+      autocorrelation_taus: The auto correlation time constant of the AR(1)
+      process.
+        A value of 0 is uncorrelated gaussian noise.
+      noise_variances: The variance of the additive noise, *not* the process
+        variance.
+      do_train_prior_ar_atau: Train or leave as constant, the autocorrelation?
+      do_train_prior_ar_nvar: Train or leave as constant, the noise variance?
+      num_steps: Number of steps to run the process.
+      name: The name to prefix to learned TF variables.
+    """
+
+    # Note the use of the plural in all of these quantities.  This is intended
+    # to mark that even though a sample z_t from the posterior is thought of a
+    # single sample of a multidimensional gaussian, the prior is actually
+    # thought of as U AR(1) processes, where U is the dimension of the inferred
+    # input.
+    size_bx1 = tf.stack([batch_size, 1])
+    size__xu = [None, z_size]
+    # process variance, the variance at time t over all instantiations of AR(1)
+    # with these parameters.
+    log_evar_inits_1xu = tf.expand_dims(tf.log(noise_variances), 0)
+    self.logevars_1xu = logevars_1xu = \
+        tf.Variable(log_evar_inits_1xu, name=name+"/logevars", dtype=tf.float32,
+                    trainable=do_train_prior_ar_nvar)
+    self.logevars_bxu = logevars_bxu = tf.tile(logevars_1xu, size_bx1)
+    logevars_bxu.set_shape(size__xu) # tile loses shape
+
+    # \tau, which is the autocorrelation time constant of the AR(1) process
+    log_atau_inits_1xu = tf.expand_dims(tf.log(autocorrelation_taus), 0)
+    self.logataus_1xu = logataus_1xu = \
+        tf.Variable(log_atau_inits_1xu, name=name+"/logatau", dtype=tf.float32,
+                    trainable=do_train_prior_ar_atau)
+
+    # phi in x_t = \mu + phi x_tm1 + \eps
+    # phi = exp(-1/tau)
+    # phi = exp(-1/exp(logtau))
+    # phi = exp(-exp(-logtau))
+    phis_1xu = tf.exp(-tf.exp(-logataus_1xu))
+    self.phis_bxu = phis_bxu = tf.tile(phis_1xu, size_bx1)
+    phis_bxu.set_shape(size__xu)
+
+    # process noise
+    # pvar = evar / (1- phi^2)
+    # logpvar = log ( exp(logevar) / (1 - phi^2) )
+    # logpvar = logevar - log(1-phi^2)
+    # logpvar = logevar - (log(1-phi) + log(1+phi))
+    self.logpvars_1xu = \
+        logevars_1xu - tf.log(1.0-phis_1xu) - tf.log(1.0+phis_1xu)
+    self.logpvars_bxu = logpvars_bxu = tf.tile(self.logpvars_1xu, size_bx1)
+    logpvars_bxu.set_shape(size__xu)
+
+    # process mean (zero but included in for completeness)
+    self.pmeans_bxu = pmeans_bxu = tf.zeros_like(phis_bxu)
+
+    # For sampling from the prior during de-novo generation.
+    self.means_t = means_t = [None] * num_steps
+    self.logvars_t = logvars_t = [None] * num_steps
+    self.samples_t = samples_t = [None] * num_steps
+    self.gaussians_t = gaussians_t = [None] * num_steps
+    sample_bxu = tf.zeros_like(phis_bxu)
+    for t in range(num_steps):
+      # process variance used here to make process completely stationary
+      if t == 0:
+        logvar_pt_bxu = self.logpvars_bxu
+      else:
+        logvar_pt_bxu = self.logevars_bxu
+
+      z_mean_pt_bxu = pmeans_bxu + phis_bxu * sample_bxu
+      gaussians_t[t] = DiagonalGaussian(batch_size, z_size,
+                                        mean=z_mean_pt_bxu,
+                                        logvar=logvar_pt_bxu)
+      sample_bxu = gaussians_t[t].sample
+      samples_t[t] = sample_bxu
+      logvars_t[t] = logvar_pt_bxu
+      means_t[t] = z_mean_pt_bxu
+
+  def logp_t(self, z_t_bxu, z_tm1_bxu=None):
+    """Compute the log-likelihood under the distribution for a given time t,
+    not the whole sequence.
+
+    Args:
+      z_t_bxu: sample to compute likelihood for at time t.
+      z_tm1_bxu (optional): sample condition probability of z_t upon.
+
+    Returns:
+      The likelihood of p_t under the model at time t. i.e.
+        p(z_t|z_tm1) = N(z_tm1 * phis, eps^2)
+
+    """
+    if z_tm1_bxu is None:
+      return diag_gaussian_log_likelihood(z_t_bxu, self.pmeans_bxu,
+                                          self.logpvars_bxu)
+    else:
+      means_t_bxu = self.pmeans_bxu + self.phis_bxu * z_tm1_bxu
+      logp_tgtm1_bxu = diag_gaussian_log_likelihood(z_t_bxu,
+                                                    means_t_bxu,
+                                                    self.logevars_bxu)
+      return logp_tgtm1_bxu
+
+
+class KLCost_GaussianGaussian(object):
+  """log p(x|z) + KL(q||p) terms for Gaussian posterior and Gaussian prior. See
+  eqn 10 and Appendix B in VAE for latter term,
+  http://arxiv.org/abs/1312.6114
+
+  The log p(x|z) term is the reconstruction error under the model.
+  The KL term represents the penalty for passing information from the encoder
+  to the decoder.
+  To sample KL(q||p), we simply sample
+        ln q - ln p
+  by drawing samples from q and averaging.
+  """
+
+  def __init__(self, zs, prior_zs):
+    """Create a lower bound in three parts, normalized reconstruction
+    cost, normalized KL divergence cost, and their sum.
+
+    E_q[ln p(z_i | z_{i+1}) / q(z_i | x)
+       \int q(z) ln p(z) dz = - 0.5 ln(2pi) - 0.5 \sum (ln(sigma_p^2) + \
+          sigma_q^2 / sigma_p^2 + (mean_p - mean_q)^2 / sigma_p^2)
+
+       \int q(z) ln q(z) dz = - 0.5 ln(2pi) - 0.5 \sum (ln(sigma_q^2) + 1)
+
+    Args:
+      zs: posterior z ~ q(z|x)
+      prior_zs: prior zs
+    """
+    # L = -KL + log p(x|z), to maximize bound on likelihood
+    # -L = KL - log p(x|z), to minimize bound on NLL
+    # so 'KL cost' is postive KL divergence
+    kl_b = 0.0
+    for z, prior_z in zip(zs, prior_zs):
+      assert isinstance(z, Gaussian)
+      assert isinstance(prior_z, Gaussian)
+      # ln(2pi) terms cancel
+      kl_b += 0.5 * tf.reduce_sum(
+          prior_z.logvar - z.logvar
+          + tf.exp(z.logvar - prior_z.logvar)
+          + tf.square((z.mean - prior_z.mean) / tf.exp(0.5 * prior_z.logvar))
+          - 1.0, [1])
+
+    self.kl_cost_b = kl_b
+    self.kl_cost = tf.reduce_mean(kl_b)
+
+
+class KLCost_GaussianGaussianProcessSampled(object):
+  """ log p(x|z) + KL(q||p) terms for Gaussian posterior and Gaussian process
+  prior via sampling.
+
+  The log p(x|z) term is the reconstruction error under the model.
+  The KL term represents the penalty for passing information from the encoder
+  to the decoder.
+  To sample KL(q||p), we simply sample
+        ln q - ln p
+  by drawing samples from q and averaging.
+  """
+
+  def __init__(self, post_zs, prior_z_process):
+    """Create a lower bound in three parts, normalized reconstruction
+    cost, normalized KL divergence cost, and their sum.
+
+    Args:
+      post_zs: posterior z ~ q(z|x)
+      prior_z_process: prior AR(1) process
+    """
+    assert len(post_zs) > 1, "GP is for time, need more than 1 time step."
+    assert isinstance(prior_z_process, GaussianProcess), "Must use GP."
+
+    # L = -KL + log p(x|z), to maximize bound on likelihood
+    # -L = KL - log p(x|z), to minimize bound on NLL
+    # so 'KL cost' is postive KL divergence
+    z0_bxu = post_zs[0].sample
+    logq_bxu = post_zs[0].logp(z0_bxu)
+    logp_bxu = prior_z_process.logp_t(z0_bxu)
+    z_tm1_bxu = z0_bxu
+    for z_t in post_zs[1:]:
+      # posterior is independent in time, prior is not
+      z_t_bxu = z_t.sample
+      logq_bxu += z_t.logp(z_t_bxu)
+      logp_bxu += prior_z_process.logp_t(z_t_bxu, z_tm1_bxu)
+      z_tm1 = z_t_bxu
+
+    kl_bxu = logq_bxu - logp_bxu
+    kl_b = tf.reduce_sum(kl_bxu, [1])
+    self.kl_cost_b = kl_b
+    self.kl_cost = tf.reduce_mean(kl_b)

lfads/lfads.py

+# Copyright 2017 Google Inc. 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.
+#
+# ==============================================================================
+"""
+LFADS - Latent Factor Analysis via Dynamical Systems.
+
+LFADS is an unsupervised method to decompose time series data into
+various factors, such as an initial condition, a generative
+dynamical system, control inputs to that generator, and a low
+dimensional description of the observed data, called the factors.
+Additionally, the observations have a noise model (in this case
+Poisson), so a denoised version of the observations is also created
+(e.g. underlying rates of a Poisson distribution given the observed
+event counts).
+
+The main data structure being passed around is a dataset.  This is a dictionary
+of data dictionaries.
+
+DATASET: The top level dictionary is simply name (string -> dictionary).
+The nested dictionary is the DATA DICTIONARY, which has the following keys:
+  'train_data' and 'valid_data', whose values are the corresponding training
+    and validation data with shape
+    ExTxD, E - # examples, T - # time steps, D - # dimensions in data.
+  The data dictionary also has a few more keys:
+    'train_ext_input' and 'valid_ext_input', if there are know external inputs
+      to the system being modeled, these take on dimensions:
+      ExTxI, E - # examples, T - # time steps, I = # dimensions in input.
+   'alignment_matrix_cxf' - If you are using multiple days data, it's possible
+     that one can align the channels (see manuscript).  If so each dataset will
+     contain this matrix, which will be used for both the input adapter and the
+     output adapter for each dataset. These matrices, if provided, must be of
+     size [data_dim x factors] where data_dim is the number of neurons recorded
+     on that day, and factors is chosen and set through the '--factors' flag.
+
+  If one runs LFADS on data where the true rates are known for some trials,
+  (say simulated, testing data, as in the example shipped with the paper), then
+  one can add three more fields for plotting purposes.  These are 'train_truth'
+  and 'valid_truth', and 'conversion_factor'.  These have the same dimensions as
+  'train_data', and 'valid_data' but represent the underlying rates of the
+  observations.  Finally, if one needs to convert scale for plotting the true
+  underlying firing rates, there is the 'conversion_factor' key.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+import numpy as np
+import os
+import tensorflow as tf
+from distributions import LearnableDiagonalGaussian, DiagonalGaussianFromInput
+from distributions import diag_gaussian_log_likelihood
+from distributions import KLCost_GaussianGaussian, Poisson
+from distributions import LearnableAutoRegressive1Prior
+from distributions import KLCost_GaussianGaussianProcessSampled
+
+from utils import init_linear, linear, list_t_bxn_to_tensor_bxtxn, write_data
+from utils import log_sum_exp, flatten
+from plot_lfads import plot_lfads
+
+
+class GRU(object):
+  """Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078).
+
+  """
+  def __init__(self, num_units, forget_bias=1.0, weight_scale=1.0,
+               clip_value=np.inf, collections=None):
+    """Create a GRU object.
+
+    Args:
+      num_units: Number of units in the GRU
+      forget_bias (optional): Hack to help learning.
+      weight_scale (optional): weights are scaled by ws/sqrt(#inputs), with
+       ws being the weight scale.
+      clip_value (optional): if the recurrent values grow above this value,
+        clip them.
+      collections (optional): List of additonal collections variables should
+        belong to.
+    """
+    self._num_units = num_units
+    self._forget_bias = forget_bias
+    self._weight_scale = weight_scale
+    self._clip_value = clip_value
+    self._collections = collections
+
+  @property
+  def state_size(self):
+    return self._num_units
+
+  @property
+  def output_size(self):
+    return self._num_units
+
+  @property
+  def state_multiplier(self):
+    return 1
+
+  def output_from_state(self, state):
+    """Return the output portion of the state."""
+    return state
+
+  def __call__(self, inputs, state, scope=None):
+    """Gated recurrent unit (GRU) function.
+
+    Args:
+      inputs: A 2D batch x input_dim tensor of inputs.
+      state: The previous state from the last time step.
+      scope (optional): TF variable scope for defined GRU variables.
+
+    Returns:
+      A tuple (state, state), where state is the newly computed state at time t.
+      It is returned twice to respect an interface that works for LSTMs.
+    """
+
+    x = inputs
+    h = state
+    if inputs is not None:
+      xh = tf.concat(axis=1, values=[x, h])
+    else:
+      xh = h
+
+    with tf.variable_scope(scope or type(self).__name__):  # "GRU"
+      with tf.variable_scope("Gates"):  # Reset gate and update gate.
+        # We start with bias of 1.0 to not reset and not update.
+        r, u = tf.split(axis=1, num_or_size_splits=2, value=linear(xh,
+                                     2 * self._num_units,
+                                     alpha=self._weight_scale,
+                                     name="xh_2_ru",
+                                     collections=self._collections))
+        r, u = tf.sigmoid(r), tf.sigmoid(u + self._forget_bias)
+      with tf.variable_scope("Candidate"):
+        xrh = tf.concat(axis=1, values=[x, r * h])
+        c = tf.tanh(linear(xrh, self._num_units, name="xrh_2_c",
+                           collections=self._collections))
+      new_h = u * h + (1 - u) * c
+      new_h = tf.clip_by_value(new_h, -self._clip_value, self._clip_value)
+
+    return new_h, new_h
+
+
+class GenGRU(object):
+  """Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078).
+
+  This version is specialized for the generator, but isn't as fast, so
+  we have two.  Note this allows for l2 regularization on the recurrent
+  weights, but also implicitly rescales the inputs via the 1/sqrt(input)
+  scaling in the linear helper routine to be large magnitude, if there are
+  fewer inputs than recurrent state.
+
+  """
+  def __init__(self, num_units, forget_bias=1.0,
+               input_weight_scale=1.0, rec_weight_scale=1.0, clip_value=np.inf,
+               input_collections=None, recurrent_collections=None):
+    """Create a GRU object.
+
+    Args:
+      num_units: Number of units in the GRU
+      forget_bias (optional): Hack to help learning.
+      input_weight_scale (optional): weights are scaled ws/sqrt(#inputs), with
+        ws being the weight scale.
+      rec_weight_scale (optional): weights are scaled ws/sqrt(#inputs),
+        with ws being the weight scale.
+      clip_value (optional): if the recurrent values grow above this value,
+        clip them.
+      input_collections (optional): List of additonal collections variables
+        that input->rec weights should belong to.
+      recurrent_collections (optional): List of additonal collections variables
+        that rec->rec weights should belong to.
+    """
+    self._num_units = num_units
+    self._forget_bias = forget_bias
+    self._input_weight_scale = input_weight_scale
+    self._rec_weight_scale = rec_weight_scale
+    self._clip_value = clip_value
+    self._input_collections = input_collections
+    self._rec_collections = recurrent_collections
+
+  @property
+  def state_size(self):
+    return self._num_units
+
+  @property
+  def output_size(self):
+    return self._num_units
+
+  @property
+  def state_multiplier(self):
+    return 1
+
+  def output_from_state(self, state):
+    """Return the output portion of the state."""
+    return state
+
+  def __call__(self, inputs, state, scope=None):
+    """Gated recurrent unit (GRU) function.
+
+    Args:
+      inputs: A 2D batch x input_dim tensor of inputs.
+      state: The previous state from the last time step.
+      scope (optional): TF variable scope for defined GRU variables.
+
+    Returns:
+      A tuple (state, state), where state is the newly computed state at time t.
+      It is returned twice to respect an interface that works for LSTMs.
+    """
+
+    x = inputs
+    h = state
+    with tf.variable_scope(scope or type(self).__name__):  # "GRU"
+      with tf.variable_scope("Gates"):  # Reset gate and update gate.
+        # We start with bias of 1.0 to not reset and not update.
+        r_x = u_x = 0.0
+        if x is not None:
+          r_x, u_x = tf.split(axis=1, num_or_size_splits=2, value=linear(x,
+                                           2 * self._num_units,
+                                           alpha=self._input_weight_scale,
+                                           do_bias=False,
+                                           name="x_2_ru",
+                                           normalized=False,
+                                           collections=self._input_collections))
+
+        r_h, u_h = tf.split(axis=1, num_or_size_splits=2, value=linear(h,
+                                         2 * self._num_units,
+                                         do_bias=True,
+                                         alpha=self._rec_weight_scale,
+                                         name="h_2_ru",
+                                         collections=self._rec_collections))
+        r = r_x + r_h
+        u = u_x + u_h
+        r, u = tf.sigmoid(r), tf.sigmoid(u + self._forget_bias)
+
+      with tf.variable_scope("Candidate"):
+        c_x = 0.0
+        if x is not None:
+          c_x = linear(x, self._num_units, name="x_2_c", do_bias=False,
+                       alpha=self._input_weight_scale,
+                       normalized=False,
+                       collections=self._input_collections)
+        c_rh = linear(r*h, self._num_units, name="rh_2_c", do_bias=True,
+                     alpha=self._rec_weight_scale,
+                     collections=self._rec_collections)
+        c = tf.tanh(c_x + c_rh)
+
+      new_h = u * h + (1 - u) * c
+      new_h = tf.clip_by_value(new_h, -self._clip_value, self._clip_value)
+
+    return new_h, new_h
+
+
+class LFADS(object):
+  """LFADS - Latent Factor Analysis via Dynamical Systems.
+
+  LFADS is an unsupervised method to decompose time series data into
+  various factors, such as an initial condition, a generative
+  dynamical system, inferred inputs to that generator, and a low
+  dimensional description of the observed data, called the factors.
+  Additoinally, the observations have a noise model (in this case
+  Poisson), so a denoised version of the observations is also created
+  (e.g. underlying rates of a Poisson distribution given the observed
+  event counts).
+  """
+
+  def __init__(self, hps, kind="train", datasets=None):
+    """Create an LFADS model.
+
+       train - a model for training, sampling of posteriors is used
+       posterior_sample_and_average - sample from the posterior, this is used 
+         for evaluating the expected value of the outputs of LFADS, given a
+         specific input, by averaging over multiple samples from the approx
+         posterior.  Also used for the lower bound on the negative
+         log-likelihood using IWAE error (Importance Weighed Auto-encoder).
+         This is the denoising operation.
+       prior_sample - a model for generation - sampling from priors is used
+
+    Args:
+      hps: The dictionary of hyper parameters.
+      kind: the type of model to build (see above).
+      datasets: a dictionary of named data_dictionaries, see top of lfads.py
+    """
+    print("Building graph...")
+    all_kinds = ['train', 'posterior_sample_and_average', 'prior_sample']
+    assert kind in all_kinds, 'Wrong kind'
+    if hps.feedback_factors_or_rates == "rates":
+      assert len(hps.dataset_names) == 1, \
+      "Multiple datasets not supported for rate feedback."
+    num_steps = hps.num_steps
+    ic_dim = hps.ic_dim
+    co_dim = hps.co_dim
+    ext_input_dim = hps.ext_input_dim
+    cell_class = GRU
+    gen_cell_class = GenGRU
+
+    def makelambda(v):          # Used with tf.case
+      return lambda: v
+
+    # Define the data placeholder, and deal with all parts of the graph
+    # that are dataset dependent.
+    self.dataName = tf.placeholder(tf.string, shape=())
+    # The batch_size to be inferred from data, as normal.
+    # Additionally, the data_dim will be inferred as well, allowing for a
+    # single placeholder for all datasets, regardless of data dimension.
+    if hps.output_dist == 'poisson':
+      # Enforce correct dtype
+      assert np.issubdtype(
+          datasets[hps.dataset_names[0]]['train_data'].dtype, int), \
+          "Data dtype must be int for poisson output distribution"
+      data_dtype = tf.int32
+    elif hps.output_dist == 'gaussian':
+      assert np.issubdtype(
+          datasets[hps.dataset_names[0]]['train_data'].dtype, float), \
+          "Data dtype must be float for gaussian output dsitribution"
+      data_dtype = tf.float32
+    else:
+      assert False, "NIY"
+    self.dataset_ph = dataset_ph = tf.placeholder(data_dtype,
+                                                  [None, num_steps, None],
+                                                  name="data")
+    self.train_step = tf.get_variable("global_step", [], tf.int64,
+                                      tf.zeros_initializer(),
+                                      trainable=False)
+    self.hps = hps
+    ndatasets = hps.ndatasets
+    factors_dim = hps.factors_dim
+    self.preds = preds = [None] * ndatasets
+    self.fns_in_fac_Ws = fns_in_fac_Ws = [None] * ndatasets
+    self.fns_in_fatcor_bs = fns_in_fac_bs = [None] * ndatasets
+    self.fns_out_fac_Ws = fns_out_fac_Ws = [None] * ndatasets
+    self.fns_out_fac_bs = fns_out_fac_bs = [None] * ndatasets
+    self.datasetNames = dataset_names = hps.dataset_names
+    self.ext_inputs = ext_inputs = None
+
+    if len(dataset_names) == 1:  # single session
+      if 'alignment_matrix_cxf' in datasets[dataset_names[0]].keys():
+        used_in_factors_dim = factors_dim
+        in_identity_if_poss = False
+      else:
+        used_in_factors_dim = hps.dataset_dims[dataset_names[0]]
+        in_identity_if_poss = True
+    else:  # multisession
+      used_in_factors_dim = factors_dim
+      in_identity_if_poss = False
+
+    for d, name in enumerate(dataset_names):
+      data_dim = hps.dataset_dims[name]
+      in_mat_cxf = None
+      if datasets and 'alignment_matrix_cxf' in datasets[name].keys():
+        dataset = datasets[name]
+        print("Using alignment matrix provided for dataset:", name)
+        in_mat_cxf = dataset['alignment_matrix_cxf'].astype(np.float32)
+        if in_mat_cxf.shape != (data_dim, factors_dim):
+          raise ValueError("""Alignment matrix must have dimensions %d x %d 
+          (data_dim x factors_dim), but currently has %d x %d."""%
+                           (data_dim, factors_dim, in_mat_cxf.shape[0],
+                            in_mat_cxf.shape[1]))
+
+      in_fac_lin = init_linear(data_dim, used_in_factors_dim, do_bias=True,
+                               mat_init_value=in_mat_cxf,
+                               identity_if_possible=in_identity_if_poss,
+                               normalized=False, name="x_2_infac_"+name,
+                               collections=['IO_transformations'])
+      in_fac_W, in_fac_b = in_fac_lin
+      fns_in_fac_Ws[d] = makelambda(in_fac_W)
+      fns_in_fac_bs[d] = makelambda(in_fac_b)
+
+    with tf.variable_scope("glm"):
+      out_identity_if_poss = False
+      if len(dataset_names) == 1 and \
+          factors_dim == hps.dataset_dims[dataset_names[0]]:
+        out_identity_if_poss = True
+      for d, name in enumerate(dataset_names):
+        data_dim = hps.dataset_dims[name]
+        in_mat_cxf = None
+        if datasets and 'alignment_matrix_cxf' in datasets[name].keys():
+          dataset = datasets[name]
+          in_mat_cxf = dataset['alignment_matrix_cxf'].astype(np.float32)
+
+        out_mat_cxf = None
+        if in_mat_cxf is not None:
+          out_mat_cxf = in_mat_cxf.T
+
+        if hps.output_dist == 'poisson':
+          out_fac_lin = init_linear(factors_dim, data_dim, do_bias=True,
+                                    mat_init_value=out_mat_cxf,
+                                    identity_if_possible=out_identity_if_poss,
+                                    normalized=False,
+                                    name="fac_2_logrates_"+name,
+                                    collections=['IO_transformations'])
+          out_fac_W, out_fac_b = out_fac_lin
+
+        elif hps.output_dist == 'gaussian':
+          out_fac_lin_mean = \
+              init_linear(factors_dim, data_dim, do_bias=True,
+                          mat_init_value=out_mat_cxf,
+                          normalized=False,
+                          name="fac_2_means_"+name,
+                          collections=['IO_transformations'])
+          out_fac_lin_logvar = \
+              init_linear(factors_dim, data_dim, do_bias=True,
+                          mat_init_value=out_mat_cxf,
+                          normalized=False,
+                          name="fac_2_logvars_"+name,
+                          collections=['IO_transformations'])
+          out_fac_W_mean, out_fac_b_mean = out_fac_lin_mean
+          out_fac_W_logvar, out_fac_b_logvar = out_fac_lin_logvar
+          out_fac_W = tf.concat(
+              axis=1, values=[out_fac_W_mean, out_fac_W_logvar])
+          out_fac_b = tf.concat(
+              axis=1, values=[out_fac_b_mean, out_fac_b_logvar])
+        else:
+          assert False, "NIY"
+
+        preds[d] = tf.equal(tf.constant(name), self.dataName)
+        data_dim = hps.dataset_dims[name]
+        fns_out_fac_Ws[d] = makelambda(out_fac_W)
+        fns_out_fac_bs[d] =  makelambda(out_fac_b)
+
+    pf_pairs_in_fac_Ws = zip(preds, fns_in_fac_Ws)
+    pf_pairs_in_fac_bs = zip(preds, fns_in_fac_bs)
+    pf_pairs_out_fac_Ws = zip(preds, fns_out_fac_Ws)
+    pf_pairs_out_fac_bs = zip(preds, fns_out_fac_bs)
+
+    case_default = lambda: tf.constant([-8675309.0])
+    this_in_fac_W = tf.case(pf_pairs_in_fac_Ws, case_default, exclusive=True)
+    this_in_fac_b = tf.case(pf_pairs_in_fac_bs, case_default, exclusive=True)
+    this_out_fac_W = tf.case(pf_pairs_out_fac_Ws, case_default, exclusive=True)
+    this_out_fac_b = tf.case(pf_pairs_out_fac_bs, case_default, exclusive=True)
+
+    # External inputs (not changing by dataset, by definition).
+    if hps.ext_input_dim > 0:
+      self.ext_input = tf.placeholder(tf.float32,
+                                      [None, num_steps, ext_input_dim],
+                                      name="ext_input")
+    else:
+      self.ext_input = None
+    ext_input_bxtxi = self.ext_input
+
+    self.keep_prob = keep_prob = tf.placeholder(tf.float32, [], "keep_prob")
+    self.batch_size = batch_size = int(hps.batch_size)
+    self.learning_rate = tf.Variable(float(hps.learning_rate_init),
+                                     trainable=False, name="learning_rate")
+    self.learning_rate_decay_op = self.learning_rate.assign(
+        self.learning_rate * hps.learning_rate_decay_factor)
+
+    # Dropout the data.
+    dataset_do_bxtxd = tf.nn.dropout(tf.to_float(dataset_ph), keep_prob)
+    if hps.ext_input_dim > 0:
+      ext_input_do_bxtxi = tf.nn.dropout(ext_input_bxtxi, keep_prob)
+    else:
+      ext_input_do_bxtxi = None
+
+    # ENCODERS
+    def encode_data(dataset_bxtxd, enc_cell, name, forward_or_reverse,
+                num_steps_to_encode):
+      """Encode data for LFADS
+      Args:
+        dataset_bxtxd - the data to encode, as a 3 tensor, with dims
+          time x batch x data dims.
+        enc_cell: encoder cell
+        name: name of encoder
+        forward_or_reverse: string, encode in forward or reverse direction
+        num_steps_to_encode: number of steps to  encode, 0:num_steps_to_encode
+      Returns:
+        encoded data as a list with num_steps_to_encode items, in order
+      """
+      if forward_or_reverse == "forward":
+        dstr = "_fwd"
+        time_fwd_or_rev = range(num_steps_to_encode)
+      else:
+        dstr = "_rev"
+        time_fwd_or_rev = reversed(range(num_steps_to_encode))
+
+      with tf.variable_scope(name+"_enc"+dstr, reuse=False):
+        enc_state = tf.tile(
+            tf.Variable(tf.zeros([1, enc_cell.state_size]),
+                        name=name+"_enc_t0"+dstr), tf.stack([batch_size, 1]))
+        enc_state.set_shape([None, enc_cell.state_size]) # tile loses shape
+
+      enc_outs = [None] * num_steps_to_encode
+      for i, t in enumerate(time_fwd_or_rev):
+        with tf.variable_scope(name+"_enc"+dstr, reuse=True if i > 0 else None):
+          dataset_t_bxd = dataset_bxtxd[:,t,:]
+          in_fac_t_bxf = tf.matmul(dataset_t_bxd, this_in_fac_W) + this_in_fac_b
+          in_fac_t_bxf.set_shape([None, used_in_factors_dim])
+          if ext_input_dim > 0 and not hps.inject_ext_input_to_gen:
+            ext_input_t_bxi = ext_input_do_bxtxi[:,t,:]
+            enc_input_t_bxfpe = tf.concat(
+                axis=1, values=[in_fac_t_bxf, ext_input_t_bxi])
+          else:
+            enc_input_t_bxfpe = in_fac_t_bxf
+          enc_out, enc_state = enc_cell(enc_input_t_bxfpe, enc_state)
+          enc_outs[t] = enc_out
+
+      return enc_outs
+
+    # Encode initial condition means and variances
+    # ([x_T, x_T-1, ... x_0] and [x_0, x_1, ... x_T] -> g0/c0)
+    self.ic_enc_fwd = [None] * num_steps
+    self.ic_enc_rev = [None] * num_steps
+    if ic_dim > 0:
+      enc_ic_cell = cell_class(hps.ic_enc_dim,
+                               weight_scale=hps.cell_weight_scale,
+                               clip_value=hps.cell_clip_value)
+      ic_enc_fwd = encode_data(dataset_do_bxtxd, enc_ic_cell,
+                               "ic", "forward",
+                               hps.num_steps_for_gen_ic)
+      ic_enc_rev = encode_data(dataset_do_bxtxd, enc_ic_cell,
+                               "ic", "reverse",
+                               hps.num_steps_for_gen_ic)
+      self.ic_enc_fwd = ic_enc_fwd
+      self.ic_enc_rev = ic_enc_rev
+
+    # Encoder control input means and variances, bi-directional encoding so:
+    # ([x_T, x_T-1, ..., x_0] and [x_0, x_1 ... x_T] -> u_t)
+    self.ci_enc_fwd = [None] * num_steps
+    self.ci_enc_rev = [None] * num_steps
+    if co_dim > 0:
+      enc_ci_cell = cell_class(hps.ci_enc_dim,
+                               weight_scale=hps.cell_weight_scale,
+                               clip_value=hps.cell_clip_value)
+      ci_enc_fwd = encode_data(dataset_do_bxtxd, enc_ci_cell,
+                               "ci", "forward",
+                               hps.num_steps)
+      if hps.do_causal_controller:
+        ci_enc_rev = None
+      else:
+        ci_enc_rev = encode_data(dataset_do_bxtxd, enc_ci_cell,
+                                 "ci", "reverse",
+                                 hps.num_steps)
+      self.ci_enc_fwd = ci_enc_fwd
+      self.ci_enc_rev = ci_enc_rev
+
+    # STOCHASTIC LATENT VARIABLES, priors and posteriors
+    # (initial conditions g0, and control inputs, u_t)
+    # Note that zs represent all the stochastic latent variables.
+    with tf.variable_scope("z", reuse=False):
+      self.prior_zs_g0 = None
+      self.posterior_zs_g0 = None
+      self.g0s_val = None
+      if ic_dim > 0:
+        self.prior_zs_g0 = \
+            LearnableDiagonalGaussian(batch_size, ic_dim, name="prior_g0",
+                                      mean_init=0.0,
+                                      var_min=hps.ic_prior_var_min,
+                                      var_init=hps.ic_prior_var_scale,
+                                      var_max=hps.ic_prior_var_max)
+        ic_enc = tf.concat(axis=1, values=[ic_enc_fwd[-1], ic_enc_rev[0]])
+        ic_enc = tf.nn.dropout(ic_enc, keep_prob)
+        self.posterior_zs_g0 = \
+            DiagonalGaussianFromInput(ic_enc, ic_dim, "ic_enc_2_post_g0",
+                                      var_min=hps.ic_post_var_min)
+        if kind in ["train", "posterior_sample_and_average"]:
+          zs_g0 = self.posterior_zs_g0
+        else:
+          zs_g0 = self.prior_zs_g0
+        if kind in ["train", "posterior_sample_and_average", "prior_sample"]:
+          self.g0s_val = zs_g0.sample
+        else:
+          self.g0s_val = zs_g0.mean
+
+      # Priors for controller, 'co' for controller output
+      self.prior_zs_co = prior_zs_co = [None] * num_steps
+      self.posterior_zs_co = posterior_zs_co = [None] * num_steps
+      self.zs_co = zs_co = [None] * num_steps
+      self.prior_zs_ar_con = None
+      if co_dim > 0:
+        # Controller outputs
+        autocorrelation_taus = [hps.prior_ar_atau for x in range(hps.co_dim)]
+        noise_variances = [hps.prior_ar_nvar for x in range(hps.co_dim)]
+        self.prior_zs_ar_con = prior_zs_ar_con = \
+            LearnableAutoRegressive1Prior(batch_size, hps.co_dim,
+                                          autocorrelation_taus,
+                                          noise_variances,
+                                          hps.do_train_prior_ar_atau,
+                                          hps.do_train_prior_ar_nvar,
+                                          num_steps, "u_prior_ar1")
+
+    # CONTROLLER -> GENERATOR -> RATES
+    # (u(t) -> gen(t) -> factors(t) -> rates(t) -> p(x_t|z_t) )
+    self.controller_outputs = u_t = [None] * num_steps
+    self.con_ics = con_state = None
+    self.con_states = con_states = [None] * num_steps
+    self.con_outs = con_outs = [None] * num_steps
+    self.gen_inputs = gen_inputs = [None] * num_steps
+    if co_dim > 0:
+      # gen_cell_class here for l2 penalty recurrent weights
+      # didn't split the cell_weight scale here, because I doubt it matters
+      con_cell = gen_cell_class(hps.con_dim,
+                                input_weight_scale=hps.cell_weight_scale,
+                                rec_weight_scale=hps.cell_weight_scale,
+                                clip_value=hps.cell_clip_value,
+                                recurrent_collections=['l2_con_reg'])
+      with tf.variable_scope("con", reuse=False):
+        self.con_ics = tf.tile(
+            tf.Variable(tf.zeros([1, hps.con_dim*con_cell.state_multiplier]), \
+                        name="c0"),
+            tf.stack([batch_size, 1]))
+        self.con_ics.set_shape([None, con_cell.state_size]) # tile loses shape
+        con_states[-1] = self.con_ics
+
+    gen_cell = gen_cell_class(hps.gen_dim,
+                              input_weight_scale=hps.gen_cell_input_weight_scale,
+                              rec_weight_scale=hps.gen_cell_rec_weight_scale,
+                              clip_value=hps.cell_clip_value,
+                              recurrent_collections=['l2_gen_reg'])
+    with tf.variable_scope("gen", reuse=False):
+      if ic_dim == 0:
+        self.gen_ics = tf.tile(
+              tf.Variable(tf.zeros([1, gen_cell.state_size]), name="g0"),
+              tf.stack([batch_size, 1]))
+      else:
+        self.gen_ics = linear(self.g0s_val, gen_cell.state_size,
+                              identity_if_possible=True,
+                              name="g0_2_gen_ic")
+
+      self.gen_states = gen_states = [None] * num_steps
+      self.gen_outs = gen_outs = [None] * num_steps
+      gen_states[-1] = self.gen_ics
+      gen_outs[-1] = gen_cell.output_from_state(gen_states[-1])
+      self.factors = factors = [None] * num_steps
+      factors[-1] = linear(gen_outs[-1], factors_dim, do_bias=False,
+                           normalized=True, name="gen_2_fac")
+
+    self.rates = rates = [None] * num_steps
+    # rates[-1] is collected to potentially feed back to controller
+    with tf.variable_scope("glm", reuse=False):
+      if hps.output_dist == 'poisson':
+        log_rates_t0 = tf.matmul(factors[-1], this_out_fac_W) + this_out_fac_b
+        log_rates_t0.set_shape([None, None])
+        rates[-1] = tf.exp(log_rates_t0) # rate
+        rates[-1].set_shape([None, hps.dataset_dims[hps.dataset_names[0]]])
+      elif hps.output_dist == 'gaussian':
+        mean_n_logvars = tf.matmul(factors[-1],this_out_fac_W) + this_out_fac_b
+        mean_n_logvars.set_shape([None, None])
+        means_t_bxd, logvars_t_bxd = tf.split(axis=1, num_or_size_splits=2,
+                                              value=mean_n_logvars)
+        rates[-1] = means_t_bxd
+      else:
+        assert False, "NIY"
+
+
+    # We support mulitple output distributions, for example Poisson, and also
+    # Gaussian. In these two cases respectively, there are one and two
+    # parameters (rates vs. mean and variance).  So the output_dist_params
+    # tensor will variable sizes via tf.concat and tf.split, along the 1st
+    # dimension. So in the case of gaussian, for example, it'll be
+    # batch x (D+D), where each D dims is the mean, and then variances,
+    # respectively. For a distribution with 3 parameters, it would be
+    # batch x (D+D+D).
+    self.output_dist_params = dist_params = [None] * num_steps
+    self.log_p_xgz_b = log_p_xgz_b = 0.0  # log P(x|z)
+    for t in range(num_steps):
+      # Controller
+      if co_dim > 0:
+        # Build inputs for controller
+        tlag = t - hps.controller_input_lag
+        if tlag < 0:
+          con_in_f_t = tf.zeros_like(ci_enc_fwd[0])
+        else:
+          con_in_f_t = ci_enc_fwd[tlag]
+        if hps.do_causal_controller:
+          # If controller is causal (wrt to data generation process), then it
+          # cannot see future data.  Thus, excluding ci_enc_rev[t] is obvious.
+          # Less obvious is the need to exclude factors[t-1].  This arises
+          # because information flows from g0 through factors to the controller
+          # input.  The g0 encoding is backwards, so we must necessarily exclude
+          # the factors in order to keep the controller input purely from a
+          # forward encoding (however unlikely it is that
+          # g0->factors->controller channel might actually be used in this way).
+          con_in_list_t = [con_in_f_t]
+        else:
+          tlag_rev = t + hps.controller_input_lag
+          if tlag_rev >= num_steps:
+            # better than zeros
+            con_in_r_t = tf.zeros_like(ci_enc_rev[0])
+          else:
+            con_in_r_t = ci_enc_rev[tlag_rev]
+          con_in_list_t = [con_in_f_t, con_in_r_t]
+
+        if hps.do_feed_factors_to_controller:
+          if hps.feedback_factors_or_rates == "factors":
+            con_in_list_t.append(factors[t-1])
+          elif hps.feedback_factors_or_rates == "rates":
+            con_in_list_t.append(rates[t-1])
+          else:
+            assert False, "NIY"
+
+        con_in_t = tf.concat(axis=1, values=con_in_list_t)
+        con_in_t = tf.nn.dropout(con_in_t, keep_prob)
+        with tf.variable_scope("con", reuse=True if t > 0 else None):
+          con_outs[t], con_states[t] = con_cell(con_in_t, con_states[t-1])
+          posterior_zs_co[t] = \
+            DiagonalGaussianFromInput(con_outs[t], co_dim,
+                                      name="con_to_post_co")
+        if kind == "train":
+          u_t[t] = posterior_zs_co[t].sample
+        elif kind == "posterior_sample_and_average":
+          u_t[t] = posterior_zs_co[t].sample
+        else:
+          u_t[t] = prior_zs_ar_con.samples_t[t]
+
+      # Inputs to the generator (controller output + external input)
+      if ext_input_dim > 0 and hps.inject_ext_input_to_gen:
+        ext_input_t_bxi = ext_input_do_bxtxi[:,t,:]
+        if co_dim > 0:
+          gen_inputs[t] = tf.concat(axis=1, values=[u_t[t], ext_input_t_bxi])
+        else:
+          gen_inputs[t] = ext_input_t_bxi
+      else:
+        gen_inputs[t] = u_t[t]
+
+      # Generator
+      data_t_bxd = dataset_ph[:,t,:]
+      with tf.variable_scope("gen", reuse=True if t > 0 else None):
+        gen_outs[t], gen_states[t] = gen_cell(gen_inputs[t], gen_states[t-1])
+        gen_outs[t] = tf.nn.dropout(gen_outs[t], keep_prob)
+      with tf.variable_scope("gen", reuse=True): # ic defined it above
+        factors[t] = linear(gen_outs[t], factors_dim, do_bias=False,
+                            normalized=True, name="gen_2_fac")
+      with tf.variable_scope("glm", reuse=True if t > 0 else None):
+        if hps.output_dist == 'poisson':
+          log_rates_t = tf.matmul(factors[t], this_out_fac_W) + this_out_fac_b
+          log_rates_t.set_shape([None, None])
+          rates[t] = dist_params[t] = tf.exp(log_rates_t) # rates feed back
+          rates[t].set_shape([None, hps.dataset_dims[hps.dataset_names[0]]])
+          loglikelihood_t = Poisson(log_rates_t).logp(data_t_bxd)
+
+        elif hps.output_dist == 'gaussian':
+          mean_n_logvars = tf.matmul(factors[t],this_out_fac_W) + this_out_fac_b
+          mean_n_logvars.set_shape([None, None])
+          means_t_bxd, logvars_t_bxd = tf.split(axis=1, num_or_size_splits=2,
+                                                value=mean_n_logvars)
+          rates[t] = means_t_bxd # rates feed back to controller
+          dist_params[t] = tf.concat(
+              axis=1, values=[means_t_bxd, tf.exp(logvars_t_bxd)])
+          loglikelihood_t = \
+              diag_gaussian_log_likelihood(data_t_bxd,
+                                           means_t_bxd, logvars_t_bxd)
+        else:
+          assert False, "NIY"
+
+        log_p_xgz_b += tf.reduce_sum(loglikelihood_t, [1])
+
+    # Correlation of inferred inputs cost.
+    self.corr_cost = tf.constant(0.0)
+    if hps.co_mean_corr_scale > 0.0:
+      all_sum_corr = []
+      for i in range(hps.co_dim):
+        for j in range(i+1, hps.co_dim):
+          sum_corr_ij = tf.constant(0.0)
+          for t in range(num_steps):
+            u_mean_t = posterior_zs_co[t].mean
+            sum_corr_ij += u_mean_t[:,i]*u_mean_t[:,j]
+          all_sum_corr.append(0.5 * tf.square(sum_corr_ij))
+      self.corr_cost = tf.reduce_mean(all_sum_corr) # div by batch and by n*(n-1)/2 pairs
+
+    # Variational Lower Bound on posterior, p(z|x), plus reconstruction cost.
+    # KL and reconstruction costs are normalized only by batch size, not by
+    # dimension, or by time steps.
+    kl_cost_g0_b = tf.zeros_like(batch_size, dtype=tf.float32)
+    kl_cost_co_b = tf.zeros_like(batch_size, dtype=tf.float32)
+    self.kl_cost = tf.constant(0.0) # VAE KL cost
+    self.recon_cost = tf.constant(0.0) # VAE reconstruction cost
+    self.nll_bound_vae = tf.constant(0.0)
+    self.nll_bound_iwae = tf.constant(0.0) # for eval with IWAE cost.
+    if kind in ["train", "posterior_sample_and_average"]:
+      kl_cost_g0_b = 0.0
+      kl_cost_co_b = 0.0
+      if ic_dim > 0:
+        g0_priors = [self.prior_zs_g0]
+        g0_posts = [self.posterior_zs_g0]
+        kl_cost_g0_b = KLCost_GaussianGaussian(g0_posts, g0_priors).kl_cost_b
+        kl_cost_g0_b = hps.kl_ic_weight * kl_cost_g0_b
+      if co_dim > 0:
+        kl_cost_co_b = \
+            KLCost_GaussianGaussianProcessSampled(
+                posterior_zs_co, prior_zs_ar_con).kl_cost_b
+        kl_cost_co_b = hps.kl_co_weight * kl_cost_co_b
+
+      # L = -KL + log p(x|z), to maximize bound on likelihood
+      # -L = KL - log p(x|z), to minimize bound on NLL
+      # so 'reconstruction cost' is negative log likelihood
+      self.recon_cost = - tf.reduce_mean(log_p_xgz_b)
+      self.kl_cost = tf.reduce_mean(kl_cost_g0_b + kl_cost_co_b)
+
+      lb_on_ll_b = log_p_xgz_b - kl_cost_g0_b - kl_cost_co_b
+
+      # VAE error averages outside the log
+      self.nll_bound_vae = -tf.reduce_mean(lb_on_ll_b)
+
+      # IWAE error averages inside the log
+      k = tf.cast(tf.shape(log_p_xgz_b)[0], tf.float32)
+      iwae_lb_on_ll = -tf.log(k) + log_sum_exp(lb_on_ll_b)
+      self.nll_bound_iwae = -iwae_lb_on_ll
+
+    # L2 regularization on the generator, normalized by number of parameters.
+    self.l2_cost = tf.constant(0.0)
+    if self.hps.l2_gen_scale > 0.0 or self.hps.l2_con_scale > 0.0:
+      l2_costs = []
+      l2_numels = []
+      l2_reg_var_lists = [tf.get_collection('l2_gen_reg'),
+                          tf.get_collection('l2_con_reg')]
+      l2_reg_scales = [self.hps.l2_gen_scale, self.hps.l2_con_scale]
+      for l2_reg_vars, l2_scale in zip(l2_reg_var_lists, l2_reg_scales):
+        for v in l2_reg_vars:
+          numel = tf.reduce_prod(tf.concat(axis=0, values=tf.shape(v)))
+          numel_f = tf.cast(numel, tf.float32)
+          l2_numels.append(numel_f)
+          v_l2 = tf.reduce_sum(v*v)
+          l2_costs.append(0.5 * l2_scale * v_l2)
+      self.l2_cost = tf.add_n(l2_costs) / tf.add_n(l2_numels)
+
+    # Compute the cost for training, part of the graph regardless.
+    # The KL cost can be problematic at the beginning of optimization,
+    # so we allow an exponential increase in weighting the KL from 0
+    # to 1.
+    self.kl_decay_step = tf.maximum(self.train_step - hps.kl_start_step, 0)
+    self.l2_decay_step = tf.maximum(self.train_step - hps.l2_start_step, 0)
+    kl_decay_step_f = tf.cast(self.kl_decay_step, tf.float32)
+    l2_decay_step_f = tf.cast(self.l2_decay_step, tf.float32)
+    kl_increase_steps_f = tf.cast(hps.kl_increase_steps, tf.float32)
+    l2_increase_steps_f = tf.cast(hps.l2_increase_steps, tf.float32)
+    self.kl_weight = kl_weight = \
+        tf.minimum(kl_decay_step_f / kl_increase_steps_f, 1.0)
+    self.l2_weight = l2_weight = \
+        tf.minimum(l2_decay_step_f / l2_increase_steps_f, 1.0)
+
+    self.timed_kl_cost = kl_weight * self.kl_cost
+    self.timed_l2_cost = l2_weight * self.l2_cost
+    self.weight_corr_cost = hps.co_mean_corr_scale * self.corr_cost
+    self.cost = self.recon_cost + self.timed_kl_cost + \
+        self.timed_l2_cost + self.weight_corr_cost
+
+    if kind != "train":
+      # save every so often
+      self.seso_saver = tf.train.Saver(tf.global_variables(),
+                                       max_to_keep=hps.max_ckpt_to_keep)
+      # lowest validation error
+      self.lve_saver = tf.train.Saver(tf.global_variables(),
+                                      max_to_keep=hps.max_ckpt_to_keep_lve)
+
+      return
+
+    # OPTIMIZATION
+    if not self.hps.do_train_io_only:
+      self.train_vars = tvars = \
+        tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
+                          scope=tf.get_variable_scope().name)
+    else:
+      self.train_vars = tvars = \
+        tf.get_collection('IO_transformations',
+                          scope=tf.get_variable_scope().name)
+    print("done.")
+    print("Model Variables (to be optimized): ")
+    total_params = 0
+    for i in range(len(tvars)):
+      shape = tvars[i].get_shape().as_list()
+      print("    ", i, tvars[i].name, shape)
+      total_params += np.prod(shape)
+    print("Total model parameters: ", total_params)
+
+    grads = tf.gradients(self.cost, tvars)
+    grads, grad_global_norm = tf.clip_by_global_norm(grads, hps.max_grad_norm)
+    opt = tf.train.AdamOptimizer(self.learning_rate, beta1=0.9, beta2=0.999,
+                                 epsilon=1e-01)
+    self.grads = grads
+    self.grad_global_norm = grad_global_norm
+    self.train_op = opt.apply_gradients(
+        zip(grads, tvars), global_step=self.train_step)
+
+    self.seso_saver = tf.train.Saver(tf.global_variables(),
+                                     max_to_keep=hps.max_ckpt_to_keep)
+
+    # lowest validation error
+    self.lve_saver = tf.train.Saver(tf.global_variables(),
+                                    max_to_keep=hps.max_ckpt_to_keep)
+
+    # SUMMARIES, used only during training.
+    # example summary
+    self.example_image = tf.placeholder(tf.float32, shape=[1,None,None,3],
+                                        name='image_tensor')
+    self.example_summ = tf.summary.image("LFADS example", self.example_image,
+                                         collections=["example_summaries"])
+
+    # general training summaries
+    self.lr_summ = tf.summary.scalar("Learning rate", self.learning_rate)
+    self.kl_weight_summ = tf.summary.scalar("KL weight", self.kl_weight)
+    self.l2_weight_summ = tf.summary.scalar("L2 weight", self.l2_weight)
+    self.corr_cost_summ = tf.summary.scalar("Corr cost", self.weight_corr_cost)
+    self.grad_global_norm_summ = tf.summary.scalar("Gradient global norm",
+                                                   self.grad_global_norm)
+    if hps.co_dim > 0:
+      self.atau_summ = [None] * hps.co_dim
+      self.pvar_summ = [None] * hps.co_dim
+      for c in range(hps.co_dim):
+        self.atau_summ[c] = \
+            tf.summary.scalar("AR Autocorrelation taus " + str(c),
+                              tf.exp(self.prior_zs_ar_con.logataus_1xu[0,c]))
+        self.pvar_summ[c] = \
+            tf.summary.scalar("AR Variances " + str(c),
+                              tf.exp(self.prior_zs_ar_con.logpvars_1xu[0,c]))
+
+    # cost summaries, separated into different collections for
+    # training vs validation.  We make placeholders for these, because
+    # even though the graph computes these costs on a per-batch basis,
+    # we want to report the more reliable metric of per-epoch cost.
+    kl_cost_ph = tf.placeholder(tf.float32, shape=[], name='kl_cost_ph')
+    self.kl_t_cost_summ = tf.summary.scalar("KL cost (train)", kl_cost_ph,
+                                            collections=["train_summaries"])
+    self.kl_v_cost_summ = tf.summary.scalar("KL cost (valid)", kl_cost_ph,
+                                            collections=["valid_summaries"])
+    l2_cost_ph = tf.placeholder(tf.float32, shape=[], name='l2_cost_ph')
+    self.l2_cost_summ = tf.summary.scalar("L2 cost", l2_cost_ph,
+                                          collections=["train_summaries"])
+
+    recon_cost_ph = tf.placeholder(tf.float32, shape=[], name='recon_cost_ph')
+    self.recon_t_cost_summ = tf.summary.scalar("Reconstruction cost (train)",
+                                               recon_cost_ph,
+                                               collections=["train_summaries"])
+    self.recon_v_cost_summ = tf.summary.scalar("Reconstruction cost (valid)",
+                                               recon_cost_ph,
+                                               collections=["valid_summaries"])
+
+    total_cost_ph = tf.placeholder(tf.float32, shape=[], name='total_cost_ph')
+    self.cost_t_summ = tf.summary.scalar("Total cost (train)", total_cost_ph,
+                                         collections=["train_summaries"])
+    self.cost_v_summ = tf.summary.scalar("Total cost (valid)", total_cost_ph,
+                                         collections=["valid_summaries"])
+
+    self.kl_cost_ph = kl_cost_ph
+    self.l2_cost_ph = l2_cost_ph
+    self.recon_cost_ph = recon_cost_ph
+    self.total_cost_ph = total_cost_ph
+
+    # Merged summaries, for easy coding later.
+    self.merged_examples = tf.summary.merge_all(key="example_summaries")
+    self.merged_generic = tf.summary.merge_all() # default key is 'summaries'
+    self.merged_train = tf.summary.merge_all(key="train_summaries")
+    self.merged_valid = tf.summary.merge_all(key="valid_summaries")
+
+    session = tf.get_default_session()
+    self.logfile = os.path.join(hps.lfads_save_dir, "lfads_log")
+    self.writer = tf.summary.FileWriter(self.logfile, session.graph)
+
+  def build_feed_dict(self, train_name, data_bxtxd, ext_input_bxtxi=None,
+                      keep_prob=None):
+    """Build the feed dictionary, handles cases where there is no value defined.
+
+    Args:
+      train_name: The key into the datasets, to set the tf.case statement for
+        the proper readin / readout matrices.
+      data_bxtxd: The data tensor
+      ext_input_bxtxi (optional): The external input tensor
+      keep_prob: The drop out keep probability.
+
+    Returns:
+      The feed dictionary with TF tensors as keys and data as values, for use
+      with tf.Session.run()
+
+    """
+    feed_dict = {}
+    B, T, _ = data_bxtxd.shape
+    feed_dict[self.dataName] = train_name
+    feed_dict[self.dataset_ph] = data_bxtxd
+
+    if self.ext_input is not None and ext_input_bxtxi is not None:
+      feed_dict[self.ext_input] = ext_input_bxtxi
+
+    if keep_prob is None:
+      feed_dict[self.keep_prob] = self.hps.keep_prob
+    else:
+      feed_dict[self.keep_prob] = keep_prob
+
+    return feed_dict
+
+  @staticmethod
+  def get_batch(data_extxd, ext_input_extxi=None, batch_size=None,
+                example_idxs=None):
+    """Get a batch of data, either randomly chosen, or specified directly.
+
+    Args:
+      data_extxd: The data to model, numpy tensors with shape:
+        # examples x # time steps x # dimensions
+      ext_input_extxi (optional): The external inputs, numpy tensor with shape:
+        # examples x # time steps x # external input dimensions
+      batch_size:  The size of the batch to return
+      example_idxs (optional): The example indices used to select examples.
+
+    Returns:
+      A tuple with two parts:
+        1. Batched data numpy tensor with shape:
+        batch_size x # time steps x # dimensions
+        2. Batched external input numpy tensor with shape:
+        batch_size x # time steps x # external input dims
+    """
+    assert batch_size is not None or example_idxs is not None, "Problems"
+    E, T, D = data_extxd.shape
+    if example_idxs is None:
+      example_idxs = np.random.choice(E, batch_size)
+
+    ext_input_bxtxi = None
+    if ext_input_extxi is not None:
+      ext_input_bxtxi = ext_input_extxi[example_idxs,:,:]
+
+    return data_extxd[example_idxs,:,:], ext_input_bxtxi
+
+  @staticmethod
+  def example_idxs_mod_batch_size(nexamples, batch_size):
+    """Given a number of examples, E, and a batch_size, B, generate indices
+    [0, 1, 2, ... B-1;
+    [B, B+1, ... 2*B-1;
+    ...
+    ]
+    returning those indices as a 2-dim tensor shaped like E/B x B.  Note that
+    shape is only correct if E % B == 0.  If not, then an extra row is generated
+    so that the remainder of examples is included. The extra examples are
+    explicitly to to the zero index (see randomize_example_idxs_mod_batch_size)
+    for randomized behavior.
+
+    Args:
+      nexamples: The number of examples to batch up.
+      batch_size: The size of the batch.
+    Returns:
+      2-dim tensor as described above.
+    """
+    bmrem = batch_size - (nexamples % batch_size)
+    bmrem_examples = []
+    if bmrem < batch_size:
+      #bmrem_examples = np.zeros(bmrem, dtype=np.int32)
+      ridxs = np.random.permutation(nexamples)[0:bmrem].astype(np.int32)
+      bmrem_examples = np.sort(ridxs)
+    example_idxs = range(nexamples) + list(bmrem_examples)
+    example_idxs_e_x_edivb = np.reshape(example_idxs, [-1, batch_size])
+    return example_idxs_e_x_edivb, bmrem
+
+  @staticmethod
+  def randomize_example_idxs_mod_batch_size(nexamples, batch_size):
+    """Indices 1:nexamples, randomized, in 2D form of
+    shape = (nexamples / batch_size) x batch_size.  The remainder
+    is managed by drawing randomly from 1:nexamples.
+
+    Args:
+      nexamples: number of examples to randomize
+      batch_size: number of elements in batch
+
+    Returns:
+      The randomized, properly shaped indicies.
+    """
+    assert nexamples > batch_size, "Problems"
+    bmrem = batch_size - nexamples % batch_size
+    bmrem_examples = []
+    if bmrem < batch_size:
+      bmrem_examples = np.random.choice(range(nexamples),
+                                        size=bmrem, replace=False)
+    example_idxs = range(nexamples) + list(bmrem_examples)
+    mixed_example_idxs = np.random.permutation(example_idxs)
+    example_idxs_e_x_edivb = np.reshape(mixed_example_idxs, [-1, batch_size])
+    return example_idxs_e_x_edivb, bmrem
+
+  def shuffle_spikes_in_time(self, data_bxtxd):
+    """Shuffle the spikes in the temporal dimension.  This is useful to
+    help the LFADS system avoid overfitting to individual spikes or fast
+    oscillations found in the data that are irrelevant to behavior. A
+    pure 'tabula rasa' approach would avoid this, but LFADS is sensitive
+    enough to pick up dynamics that you may not want.
+
+    Args:
+      data_bxtxd: numpy array of spike count data to be shuffled.
+    Returns:
+    S_bxtxd, a numpy array with the same dimensions and contents as
+      data_bxtxd, but shuffled appropriately.
+
+    """
+
+    B, T, N = data_bxtxd.shape
+    w = self.hps.temporal_spike_jitter_width
+
+    if w == 0:
+      return data_bxtxd
+
+    max_counts = np.max(data_bxtxd)
+    S_bxtxd = np.zeros([B,T,N])
+
+    # Intuitively, shuffle spike occurances, 0 or 1, but since we have counts,
+    # Do it over and over again up to the max count.
+    for mc in range(1,max_counts+1):
+      idxs = np.nonzero(data_bxtxd >= mc)
+
+      data_ones = np.zeros_like(data_bxtxd)
+      data_ones[data_bxtxd >= mc] = 1
+
+      nfound = len(idxs[0])
+      shuffles_incrs_in_time = np.random.randint(-w, w, size=nfound)
+
+      shuffle_tidxs = idxs[1].copy()
+      shuffle_tidxs += shuffles_incrs_in_time
+
+      # Reflect on the boundaries to not lose mass.
+      shuffle_tidxs[shuffle_tidxs < 0] = -shuffle_tidxs[shuffle_tidxs < 0]
+      shuffle_tidxs[shuffle_tidxs > T-1] = \
+          (T-1)-(shuffle_tidxs[shuffle_tidxs > T-1] -(T-1))
+
+      for iii in zip(idxs[0], shuffle_tidxs, idxs[2]):
+        S_bxtxd[iii] += 1
+
+    return S_bxtxd
+
+  def shuffle_and_flatten_datasets(self, datasets, kind='train'):
+    """Since LFADS supports multiple datasets in the same dynamical model,
+    we have to be careful to use all the data in a single training epoch.  But
+    since the datasets my have different data dimensionality, we cannot batch
+    examples from data dictionaries together.  Instead, we generate random
+    batches within each data dictionary, and then randomize these batches
+    while holding onto the dataname, so that when it's time to feed
+    the graph, the correct in/out matrices can be selected, per batch.
+
+    Args:
+      datasets: A dict of data dicts.  The dataset dict is simply a
+        name(string)-> data dictionary mapping (See top of lfads.py).
+      kind: 'train' or 'valid'
+
+    Returns:
+      A flat list, in which each element is a pair ('name', indices).
+    """
+    batch_size = self.hps.batch_size
+    ndatasets = len(datasets)
+    random_example_idxs = {}
+    epoch_idxs = {}
+    all_name_example_idx_pairs = []
+    kind_data = kind + '_data'
+    for name, data_dict in datasets.items():
+      nexamples, ntime, data_dim = data_dict[kind_data].shape
+      epoch_idxs[name] = 0
+      random_example_idxs, _ = \
+        self.randomize_example_idxs_mod_batch_size(nexamples, batch_size)
+
+      epoch_size = random_example_idxs.shape[0]
+      names = [name] * epoch_size
+      all_name_example_idx_pairs += zip(names, random_example_idxs)
+
+    np.random.shuffle(all_name_example_idx_pairs) # shuffle in place
+
+    return all_name_example_idx_pairs
+
+  def train_epoch(self, datasets, batch_size=None, do_save_ckpt=True):
+    """Train the model through the entire dataset once.
+
+    Args:
+      datasets: A dict of data dicts.  The dataset dict is simply a
+        name(string)-> data dictionary mapping (See top of lfads.py).
+      batch_size (optional):  The batch_size to use
+      do_save_ckpt (optional): Should the routine save a checkpoint on this
+        training epoch?
+
+    Returns:
+    A tuple with 6 float values:
+      (total cost of the epoch, epoch reconstruction cost,
+       epoch kl cost, KL weight used this training epoch,
+       total l2 cost on generator, and the corresponding weight).
+    """
+    ops_to_eval = [self.cost, self.recon_cost,
+                   self.kl_cost, self.kl_weight,
+                   self.l2_cost, self.l2_weight,
+                   self.train_op]
+    collected_op_values = self.run_epoch(datasets, ops_to_eval, kind="train")
+
+    total_cost = total_recon_cost = total_kl_cost = 0.0
+    # normalizing by batch done in distributions.py
+    epoch_size = len(collected_op_values)
+    for op_values in collected_op_values:
+      total_cost += op_values[0]
+      total_recon_cost += op_values[1]
+      total_kl_cost += op_values[2]
+
+    kl_weight = collected_op_values[-1][3]
+    l2_cost = collected_op_values[-1][4]
+    l2_weight = collected_op_values[-1][5]
+
+    epoch_total_cost = total_cost / epoch_size
+    epoch_recon_cost = total_recon_cost / epoch_size
+    epoch_kl_cost = total_kl_cost / epoch_size
+
+    if do_save_ckpt:
+      session = tf.get_default_session()
+      checkpoint_path = os.path.join(self.hps.lfads_save_dir,
+                                     self.hps.checkpoint_name + '.ckpt')
+      self.seso_saver.save(session, checkpoint_path,
+                           global_step=self.train_step)
+
+    return epoch_total_cost, epoch_recon_cost, epoch_kl_cost, \
+        kl_weight, l2_cost, l2_weight
+
+
+  def run_epoch(self, datasets, ops_to_eval, kind="train", batch_size=None,
+                do_collect=True, keep_prob=None):
+    """Run the model through the entire dataset once.
+
+    Args:
+      datasets: A dict of data dicts.  The dataset dict is simply a
+        name(string)-> data dictionary mapping (See top of lfads.py).
+      ops_to_eval: A list of tensorflow operations that will be evaluated in
+        the tf.session.run() call.
+      batch_size (optional):  The batch_size to use
+      do_collect (optional): Should the routine collect all session.run
+        output as a list, and return it?
+      keep_prob (optional): The dropout keep probability.
+
+    Returns:
+      A list of lists, the internal list is the return for the ops for each
+      session.run() call.  The outer list collects over the epoch.
+    """
+    hps = self.hps
+    all_name_example_idx_pairs = \
+        self.shuffle_and_flatten_datasets(datasets, kind)
+
+    kind_data = kind + '_data'
+    kind_ext_input = kind + '_ext_input'
+
+    total_cost = total_recon_cost = total_kl_cost = 0.0
+    session = tf.get_default_session()
+    epoch_size = len(all_name_example_idx_pairs)
+    evaled_ops_list = []
+    for name, example_idxs in all_name_example_idx_pairs:
+      data_dict = datasets[name]
+      data_extxd = data_dict[kind_data]
+      if hps.output_dist == 'poisson' and hps.temporal_spike_jitter_width > 0:
+        data_extxd = self.shuffle_spikes_in_time(data_extxd)
+
+      ext_input_extxi = data_dict[kind_ext_input]
+      data_bxtxd, ext_input_bxtxi = self.get_batch(data_extxd, ext_input_extxi,
+                                                   example_idxs=example_idxs)
+
+      feed_dict = self.build_feed_dict(name, data_bxtxd, ext_input_bxtxi,
+                                       keep_prob=keep_prob)
+      evaled_ops_np = session.run(ops_to_eval, feed_dict=feed_dict)
+      if do_collect:
+        evaled_ops_list.append(evaled_ops_np)
+
+    return evaled_ops_list
+
+  def summarize_all(self, datasets, summary_values):
+    """Plot and summarize stuff in tensorboard.
+
+    Note that everything done in the current function is otherwise done on
+    a single, randomly selected dataset (except for summary_values, which are
+    passed in.)
+
+    Args:
+      datasets, the dictionary of datasets used in the study.
+      summary_values:  These summary values are created from the training loop,
+      and so summarize the entire set of datasets.
+    """
+    hps = self.hps
+    tr_kl_cost = summary_values['tr_kl_cost']
+    tr_recon_cost = summary_values['tr_recon_cost']
+    tr_total_cost = summary_values['tr_total_cost']
+    kl_weight = summary_values['kl_weight']
+    l2_weight = summary_values['l2_weight']
+    l2_cost = summary_values['l2_cost']
+    has_any_valid_set = summary_values['has_any_valid_set']
+    i = summary_values['nepochs']
+
+    session = tf.get_default_session()
+    train_summ, train_step = session.run([self.merged_train,
+                                          self.train_step],
+                             feed_dict={self.l2_cost_ph:l2_cost,
+                                        self.kl_cost_ph:tr_kl_cost,
+                                        self.recon_cost_ph:tr_recon_cost,
+                                        self.total_cost_ph:tr_total_cost})
+    self.writer.add_summary(train_summ, train_step)
+    if has_any_valid_set:
+      ev_kl_cost = summary_values['ev_kl_cost']
+      ev_recon_cost = summary_values['ev_recon_cost']
+      ev_total_cost = summary_values['ev_total_cost']
+      eval_summ = session.run(self.merged_valid,
+                              feed_dict={self.kl_cost_ph:ev_kl_cost,
+                                         self.recon_cost_ph:ev_recon_cost,
+                                         self.total_cost_ph:ev_total_cost})
+      self.writer.add_summary(eval_summ, train_step)
+      print("Epoch:%d, step:%d (TRAIN, VALID): total: %.2f, %.2f\
+      recon: %.2f, %.2f,     kl: %.2f, %.2f,     l2: %.5f,\
+      kl weight: %.2f, l2 weight: %.2f" % \
+            (i, train_step, tr_total_cost, ev_total_cost,
+             tr_recon_cost, ev_recon_cost, tr_kl_cost, ev_kl_cost,
+             l2_cost, kl_weight, l2_weight))
+
+      csv_outstr = "epoch,%d, step,%d, total,%.2f,%.2f, \
+      recon,%.2f,%.2f, kl,%.2f,%.2f, l2,%.5f, \
+      klweight,%.2f, l2weight,%.2f\n"% \
+      (i, train_step, tr_total_cost, ev_total_cost,
+       tr_recon_cost, ev_recon_cost, tr_kl_cost, ev_kl_cost,
+       l2_cost, kl_weight, l2_weight)
+
+    else:
+      print("Epoch:%d, step:%d TRAIN: total: %.2f     recon: %.2f, kl: %.2f,\
+      l2: %.5f,    kl weight: %.2f, l2 weight: %.2f" % \
+            (i, train_step, tr_total_cost, tr_recon_cost, tr_kl_cost,
+             l2_cost, kl_weight, l2_weight))
+      csv_outstr = "epoch,%d, step,%d, total,%.2f, recon,%.2f, kl,%.2f, \
+      l2,%.5f, klweight,%.2f, l2weight,%.2f\n"% \
+      (i, train_step, tr_total_cost, tr_recon_cost,
+       tr_kl_cost, l2_cost, kl_weight, l2_weight)
+
+    if self.hps.csv_log:
+      csv_file = os.path.join(self.hps.lfads_save_dir, self.hps.csv_log+'.csv')
+      with open(csv_file, "a") as myfile:
+        myfile.write(csv_outstr)
+
+
+  def plot_single_example(self, datasets):
+    """Plot an image relating to a randomly chosen, specific example.  We use
+    posterior sample and average by taking one example, and filling a whole
+    batch with that example, sample from the posterior, and then average the
+    quantities.
+
+    """
+    hps = self.hps
+    all_data_names = datasets.keys()
+    data_name = np.random.permutation(all_data_names)[0]
+    data_dict = datasets[data_name]
+    has_valid_set = True if data_dict['valid_data'] is not None else False
+    cf = 1.0                  # plotting concern
+
+    # posterior sample and average here
+    E, _, _ = data_dict['train_data'].shape
+    eidx = np.random.choice(E)
+    example_idxs = eidx * np.ones(hps.batch_size, dtype=np.int32)
+
+    train_data_bxtxd, train_ext_input_bxtxi = \
+        self.get_batch(data_dict['train_data'], data_dict['train_ext_input'],
+                       example_idxs=example_idxs)
+
+    truth_train_data_bxtxd = None
+    if 'train_truth' in data_dict and data_dict['train_truth'] is not None:
+      truth_train_data_bxtxd, _ = self.get_batch(data_dict['train_truth'],
+                                                 example_idxs=example_idxs)
+      cf = data_dict['conversion_factor']
+
+    # plotter does averaging
+    train_model_values = self.eval_model_runs_batch(data_name,
+                                                    train_data_bxtxd,
+                                                    train_ext_input_bxtxi,
+                                                    do_average_batch=False)
+
+    train_step = train_model_values['train_steps']
+    feed_dict = self.build_feed_dict(data_name, train_data_bxtxd,
+                                     train_ext_input_bxtxi, keep_prob=1.0)
+
+    session = tf.get_default_session()
+    generic_summ = session.run(self.merged_generic, feed_dict=feed_dict)
+    self.writer.add_summary(generic_summ, train_step)
+
+    valid_data_bxtxd = valid_model_values = valid_ext_input_bxtxi = None
+    truth_valid_data_bxtxd = None
+    if has_valid_set:
+      E, _, _ = data_dict['valid_data'].shape
+      eidx = np.random.choice(E)
+      example_idxs = eidx * np.ones(hps.batch_size, dtype=np.int32)
+      valid_data_bxtxd, valid_ext_input_bxtxi = \
+          self.get_batch(data_dict['valid_data'],
+                         data_dict['valid_ext_input'],
+                         example_idxs=example_idxs)
+      if 'valid_truth' in data_dict and data_dict['valid_truth'] is not None:
+        truth_valid_data_bxtxd, _ = self.get_batch(data_dict['valid_truth'],
+                                                   example_idxs=example_idxs)
+      else:
+        truth_valid_data_bxtxd = None
+
+      # plotter does averaging
+      valid_model_values = self.eval_model_runs_batch(data_name,
+                                                      valid_data_bxtxd,
+                                                      valid_ext_input_bxtxi,
+                                                      do_average_batch=False)
+
+    example_image = plot_lfads(train_bxtxd=train_data_bxtxd,
+                               train_model_vals=train_model_values,
+                               train_ext_input_bxtxi=train_ext_input_bxtxi,
+                               train_truth_bxtxd=truth_train_data_bxtxd,
+                               valid_bxtxd=valid_data_bxtxd,
+                               valid_model_vals=valid_model_values,
+                               valid_ext_input_bxtxi=valid_ext_input_bxtxi,
+                               valid_truth_bxtxd=truth_valid_data_bxtxd,
+                               bidx=None, cf=cf, output_dist=hps.output_dist)
+    example_image = np.expand_dims(example_image, axis=0)
+    example_summ = session.run(self.merged_examples,
+                               feed_dict={self.example_image : example_image})
+    self.writer.add_summary(example_summ)
+
+  def train_model(self, datasets):
+    """Train the model, print per-epoch information, and save checkpoints.
+
+    Loop over training epochs. The function that actually does the
+    training is train_epoch.  This function iterates over the training
+    data, one epoch at a time.  The learning rate schedule is such
+    that it will stay the same until the cost goes up in comparison to
+    the last few values, then it will drop.
+
+    Args:
+      datasets: A dict of data dicts.  The dataset dict is simply a
+        name(string)-> data dictionary mapping (See top of lfads.py).
+    """
+    hps = self.hps
+    has_any_valid_set = False
+    for data_dict in datasets.values():
+      if data_dict['valid_data'] is not None:
+        has_any_valid_set = True
+        break
+
+    session = tf.get_default_session()
+    lr = session.run(self.learning_rate)
+    lr_stop = hps.learning_rate_stop
+    i = -1
+    train_costs = []
+    valid_costs = []
+    ev_total_cost = ev_recon_cost = ev_kl_cost = 0.0
+    lowest_ev_cost = np.Inf
+    while True:
+      i += 1
+      do_save_ckpt = True if i % 10 ==0 else False
+      tr_total_cost, tr_recon_cost, tr_kl_cost, kl_weight, l2_cost, l2_weight = \
+                self.train_epoch(datasets, do_save_ckpt=do_save_ckpt)
+
+      # Evaluate the validation cost, and potentially save.  Note that this
+      # routine will not save a validation checkpoint until the kl weight and
+      # l2 weights are equal to 1.0.
+      if has_any_valid_set:
+        ev_total_cost, ev_recon_cost, ev_kl_cost = \
+            self.eval_cost_epoch(datasets, kind='valid')
+        valid_costs.append(ev_total_cost)
+
+        # > 1 may give more consistent results, but not the actual lowest vae.
+        # == 1 gives the lowest vae seen so far.
+        n_lve = 1
+        run_avg_lve = np.mean(valid_costs[-n_lve:])
+
+        # conditions for saving checkpoints:
+        #   KL weight must have finished stepping (>=1.0), AND
+        #   L2 weight must have finished stepping OR L2 is not being used, AND
+        #   the current run has a lower LVE than previous runs AND
+        #     len(valid_costs > n_lve) (not sure what that does)
+        if kl_weight >= 1.0 and \
+          (l2_weight >= 1.0 or \
+           (self.hps.l2_gen_scale == 0.0 and self.hps.l2_con_scale == 0.0)) \
+           and (len(valid_costs) > n_lve and run_avg_lve < lowest_ev_cost):
+
+          lowest_ev_cost = run_avg_lve
+          checkpoint_path = os.path.join(self.hps.lfads_save_dir,
+                                         self.hps.checkpoint_name + '_lve.ckpt')
+          self.lve_saver.save(session, checkpoint_path,
+                              global_step=self.train_step,
+                              latest_filename='checkpoint_lve')
+
+      # Plot and summarize.
+      values = {'nepochs':i, 'has_any_valid_set': has_any_valid_set,
+                'tr_total_cost':tr_total_cost, 'ev_total_cost':ev_total_cost,
+                'tr_recon_cost':tr_recon_cost, 'ev_recon_cost':ev_recon_cost,
+                'tr_kl_cost':tr_kl_cost, 'ev_kl_cost':ev_kl_cost,
+                'l2_weight':l2_weight, 'kl_weight':kl_weight,
+                'l2_cost':l2_cost}
+      self.summarize_all(datasets, values)
+      self.plot_single_example(datasets)
+
+      # Manage learning rate.
+      train_res = tr_total_cost
+      n_lr = hps.learning_rate_n_to_compare
+      if len(train_costs) > n_lr and train_res > np.max(train_costs[-n_lr:]):
+        _ = session.run(self.learning_rate_decay_op)
+        lr = session.run(self.learning_rate)
+        print("     Decreasing learning rate to %f." % lr)
+        # Force the system to run n_lr times while at this lr.
+        train_costs.append(np.inf)
+      else:
+        train_costs.append(train_res)
+
+      if lr < lr_stop:
+        print("Stopping optimization based on learning rate criteria.")
+        break
+
+  def eval_cost_epoch(self, datasets, kind='train', ext_input_extxi=None,
+                      batch_size=None):
+    """Evaluate the cost of the epoch.
+
+    Args:
+      data_dict: The dictionary of data (training and validation) used for
+        training and evaluation of the model, respectively.
+
+    Returns:
+      a 3 tuple of costs:
+        (epoch total cost, epoch reconstruction cost, epoch KL cost)
+    """
+    ops_to_eval = [self.cost, self.recon_cost, self.kl_cost]
+    collected_op_values = self.run_epoch(datasets, ops_to_eval, kind=kind,
+                                         keep_prob=1.0)
+
+    total_cost = total_recon_cost = total_kl_cost = 0.0
+    # normalizing by batch done in distributions.py
+    epoch_size = len(collected_op_values)
+    for op_values in collected_op_values:
+      total_cost += op_values[0]
+      total_recon_cost += op_values[1]
+      total_kl_cost += op_values[2]
+
+    epoch_total_cost = total_cost / epoch_size
+    epoch_recon_cost = total_recon_cost / epoch_size
+    epoch_kl_cost = total_kl_cost / epoch_size
+
+    return epoch_total_cost, epoch_recon_cost, epoch_kl_cost
+
+  def eval_model_runs_batch(self, data_name, data_bxtxd, ext_input_bxtxi=None,
+                            do_eval_cost=False, do_average_batch=False):
+    """Returns all the goodies for the entire model, per batch.
+
+    Args:
+      data_name: The name of the data dict, to select which in/out matrices
+        to use.
+      data_bxtxd:  Numpy array training data with shape:
+        batch_size x # time steps x # dimensions
+      ext_input_bxtxi: Numpy array training external input with shape:
+        batch_size x # time steps x # external input dims
+      do_eval_cost (optional): If true, the IWAE (Importance Weighted
+         Autoencoder) log likeihood bound, instead of the VAE version.
+      do_average_batch (optional): average over the batch, useful for getting
+      good IWAE costs, and model outputs for a single data point.
+
+    Returns:
+      A dictionary with the outputs of the model decoder, namely:
+        prior g0 mean, prior g0 variance, approx. posterior mean, approx
+        posterior mean, the generator initial conditions, the control inputs (if
+        enabled), the state of the generator, the factors, and the rates.
+    """
+    session = tf.get_default_session()
+    feed_dict = self.build_feed_dict(data_name, data_bxtxd,
+                                     ext_input_bxtxi, keep_prob=1.0)
+
+    # Non-temporal signals will be batch x dim.
+    # Temporal signals are list length T with elements batch x dim.
+    tf_vals = [self.gen_ics, self.gen_states, self.factors,
+               self.output_dist_params]
+    tf_vals.append(self.cost)
+    tf_vals.append(self.nll_bound_vae)
+    tf_vals.append(self.nll_bound_iwae)
+    tf_vals.append(self.train_step) # not train_op!
+    if self.hps.ic_dim > 0:
+      tf_vals += [self.prior_zs_g0.mean, self.prior_zs_g0.logvar,
+                  self.posterior_zs_g0.mean, self.posterior_zs_g0.logvar]
+    if self.hps.co_dim > 0:
+      tf_vals.append(self.controller_outputs)
+    tf_vals_flat, fidxs = flatten(tf_vals)
+
+    np_vals_flat = session.run(tf_vals_flat, feed_dict=feed_dict)
+
+    ff = 0
+    gen_ics = [np_vals_flat[f] for f in fidxs[ff]]; ff += 1
+    gen_states = [np_vals_flat[f] for f in fidxs[ff]]; ff += 1
+    factors = [np_vals_flat[f] for f in fidxs[ff]]; ff += 1
+    out_dist_params = [np_vals_flat[f] for f in fidxs[ff]]; ff += 1
+    costs = [np_vals_flat[f] for f in fidxs[ff]]; ff += 1
+    nll_bound_vaes = [np_vals_flat[f] for f in fidxs[ff]]; ff += 1
+    nll_bound_iwaes = [np_vals_flat[f] for f in fidxs[ff]]; ff +=1
+    train_steps = [np_vals_flat[f] for f in fidxs[ff]]; ff +=1
+    if self.hps.ic_dim > 0:
+      prior_g0_mean = [np_vals_flat[f] for f in fidxs[ff]]; ff +=1
+      prior_g0_logvar = [np_vals_flat[f] for f in fidxs[ff]]; ff += 1
+      post_g0_mean = [np_vals_flat[f] for f in fidxs[ff]]; ff += 1
+      post_g0_logvar = [np_vals_flat[f] for f in fidxs[ff]]; ff += 1
+    if self.hps.co_dim > 0:
+      controller_outputs = [np_vals_flat[f] for f in fidxs[ff]]; ff += 1
+
+    # [0] are to take out the non-temporal items from lists
+    gen_ics = gen_ics[0]
+    costs = costs[0]
+    nll_bound_vaes = nll_bound_vaes[0]
+    nll_bound_iwaes = nll_bound_iwaes[0]
+    train_steps = train_steps[0]
+
+    # Convert to full tensors, not lists of tensors in time dim.
+    gen_states = list_t_bxn_to_tensor_bxtxn(gen_states)
+    factors = list_t_bxn_to_tensor_bxtxn(factors)
+    out_dist_params = list_t_bxn_to_tensor_bxtxn(out_dist_params)
+    if self.hps.ic_dim > 0:
+      prior_g0_mean = prior_g0_mean[0]
+      prior_g0_logvar = prior_g0_logvar[0]
+      post_g0_mean = post_g0_mean[0]
+      post_g0_logvar = post_g0_logvar[0]
+    if self.hps.co_dim > 0:
+      controller_outputs = list_t_bxn_to_tensor_bxtxn(controller_outputs)
+
+    if do_average_batch:
+      gen_ics = np.mean(gen_ics, axis=0)
+      gen_states = np.mean(gen_states, axis=0)
+      factors = np.mean(factors, axis=0)
+      out_dist_params = np.mean(out_dist_params, axis=0)
+      if self.hps.ic_dim > 0:
+        prior_g0_mean = np.mean(prior_g0_mean, axis=0)
+        prior_g0_logvar = np.mean(prior_g0_logvar, axis=0)
+        post_g0_mean = np.mean(post_g0_mean, axis=0)
+        post_g0_logvar = np.mean(post_g0_logvar, axis=0)
+      if self.hps.co_dim > 0:
+        controller_outputs = np.mean(controller_outputs, axis=0)
+
+    model_vals = {}
+    model_vals['gen_ics'] = gen_ics
+    model_vals['gen_states'] = gen_states
+    model_vals['factors'] = factors
+    model_vals['output_dist_params'] = out_dist_params
+    model_vals['costs'] = costs
+    model_vals['nll_bound_vaes'] = nll_bound_vaes
+    model_vals['nll_bound_iwaes'] = nll_bound_iwaes
+    model_vals['train_steps'] = train_steps
+    if self.hps.ic_dim > 0:
+      model_vals['prior_g0_mean'] = prior_g0_mean
+      model_vals['prior_g0_logvar'] = prior_g0_logvar
+      model_vals['post_g0_mean'] = post_g0_mean
+      model_vals['post_g0_logvar'] = post_g0_logvar
+    if self.hps.co_dim > 0:
+      model_vals['controller_outputs'] = controller_outputs
+
+    return model_vals
+
+  def eval_model_runs_avg_epoch(self, data_name, data_extxd,
+                                ext_input_extxi=None):
+    """Returns all the expected value for goodies for the entire model.
+
+    The expected value is taken over hidden (z) variables, namely the initial
+    conditions and the control inputs.  The expected value is approximate, and
+    accomplished via sampling (batch_size) samples for every examples.
+
+    Args:
+      data_name: The name of the data dict, to select which in/out matrices
+        to use.
+      data_extxd:  Numpy array training data with shape:
+        # examples x # time steps x # dimensions
+      ext_input_extxi (optional): Numpy array training external input with
+        shape: # examples x # time steps x # external input dims
+
+    Returns:
+      A dictionary with the averaged outputs of the model decoder, namely:
+        prior g0 mean, prior g0 variance, approx. posterior mean, approx
+        posterior mean, the generator initial conditions, the control inputs (if
+        enabled), the state of the generator, the factors, and the output
+        distribution parameters, e.g. (rates or mean and variances).
+    """
+    hps = self.hps
+    batch_size = hps.batch_size
+    E, T, D  = data_extxd.shape
+    E_to_process = hps.ps_nexamples_to_process
+    if E_to_process > E:
+      print("Setting number of posterior samples to process to : ", E)
+      E_to_process = E
+
+    if hps.ic_dim > 0:
+      prior_g0_mean = np.zeros([E_to_process, hps.ic_dim])
+      prior_g0_logvar = np.zeros([E_to_process, hps.ic_dim])
+      post_g0_mean = np.zeros([E_to_process, hps.ic_dim])
+      post_g0_logvar = np.zeros([E_to_process, hps.ic_dim])
+
+    if hps.co_dim > 0:
+      controller_outputs = np.zeros([E_to_process, T, hps.co_dim])
+    gen_ics = np.zeros([E_to_process, hps.gen_dim])
+    gen_states = np.zeros([E_to_process, T, hps.gen_dim])
+    factors = np.zeros([E_to_process, T, hps.factors_dim])
+
+    if hps.output_dist == 'poisson':
+      out_dist_params = np.zeros([E_to_process, T, D])
+    elif hps.output_dist == 'gaussian':
+      out_dist_params = np.zeros([E_to_process, T, D+D])
+    else:
+      assert False, "NIY"
+    
+    costs = np.zeros(E_to_process)
+    nll_bound_vaes = np.zeros(E_to_process)
+    nll_bound_iwaes = np.zeros(E_to_process)
+    train_steps = np.zeros(E_to_process)
+    for es_idx in range(E_to_process):
+      print("Running %d of %d." % (es_idx+1, E_to_process))
+      example_idxs = es_idx * np.ones(batch_size, dtype=np.int32)
+      data_bxtxd, ext_input_bxtxi = self.get_batch(data_extxd,
+                                                   ext_input_extxi,
+                                                   batch_size=batch_size,
+                                                   example_idxs=example_idxs)
+      model_values = self.eval_model_runs_batch(data_name, data_bxtxd,
+                                                ext_input_bxtxi,
+                                                do_eval_cost=True,
+                                                do_average_batch=True)
+
+      if self.hps.ic_dim > 0:
+        prior_g0_mean[es_idx,:] = model_values['prior_g0_mean']
+        prior_g0_logvar[es_idx,:] = model_values['prior_g0_logvar']
+        post_g0_mean[es_idx,:] = model_values['post_g0_mean']
+        post_g0_logvar[es_idx,:] = model_values['post_g0_logvar']
+      gen_ics[es_idx,:] = model_values['gen_ics']
+
+      if self.hps.co_dim > 0:
+        controller_outputs[es_idx,:,:] = model_values['controller_outputs']
+      gen_states[es_idx,:,:] = model_values['gen_states']
+      factors[es_idx,:,:] = model_values['factors']
+      out_dist_params[es_idx,:,:] = model_values['output_dist_params']
+      costs[es_idx] = model_values['costs']
+      nll_bound_vaes[es_idx] = model_values['nll_bound_vaes']
+      nll_bound_iwaes[es_idx] = model_values['nll_bound_iwaes']
+      train_steps[es_idx] = model_values['train_steps']
+      print('bound nll(vae): %.3f, bound nll(iwae): %.3f' \
+            % (nll_bound_vaes[es_idx], nll_bound_iwaes[es_idx]))
+
+    model_runs = {}
+    if self.hps.ic_dim > 0:
+      model_runs['prior_g0_mean'] = prior_g0_mean
+      model_runs['prior_g0_logvar'] = prior_g0_logvar
+      model_runs['post_g0_mean'] = post_g0_mean
+      model_runs['post_g0_logvar'] = post_g0_logvar
+    model_runs['gen_ics'] = gen_ics
+
+    if self.hps.co_dim > 0:
+      model_runs['controller_outputs'] = controller_outputs
+    model_runs['gen_states'] = gen_states
+    model_runs['factors'] = factors
+    model_runs['output_dist_params'] = out_dist_params
+    model_runs['costs'] = costs
+    model_runs['nll_bound_vaes'] = nll_bound_vaes
+    model_runs['nll_bound_iwaes'] = nll_bound_iwaes
+    model_runs['train_steps'] = train_steps
+    return model_runs
+
+  def write_model_runs(self, datasets, output_fname=None):
+    """Run the model on the data in data_dict, and save the computed values.
+
+    LFADS generates a number of outputs for each examples, and these are all
+    saved.  They are:
+      The mean and variance of the prior of g0.
+      The mean and variance of approximate posterior of g0.
+      The control inputs (if enabled)
+      The initial conditions, g0, for all examples.
+      The generator states for all time.
+      The factors for all time.
+      The output distribution parameters (e.g. rates) for all time.
+
+    Args:
+      datasets: a dictionary of named data_dictionaries, see top of lfads.py
+      output_fname: a file name stem for the output files.
+    """
+    hps = self.hps
+    kind = hps.kind
+
+    for data_name, data_dict in datasets.items():
+      data_tuple = [('train', data_dict['train_data'],
+                     data_dict['train_ext_input']),
+                    ('valid', data_dict['valid_data'],
+                     data_dict['valid_ext_input'])]
+      for data_kind, data_extxd, ext_input_extxi in data_tuple:
+        if not output_fname:
+          fname = "model_runs_" + data_name + '_' + data_kind + '_' + kind
+        else:
+          fname = output_fname + data_name + '_' + data_kind + '_' + kind
+
+        print("Writing data for %s data and kind %s." % (data_name, data_kind))
+        model_runs = self.eval_model_runs_avg_epoch(data_name, data_extxd,
+                                                    ext_input_extxi)
+        full_fname = os.path.join(hps.lfads_save_dir, fname)
+        write_data(full_fname, model_runs, compression='gzip')
+        print("Done.")
+
+  def write_model_samples(self, dataset_name, output_fname=None):
+    """Use the prior distribution to generate batch_size number of samples
+    from the model.
+
+    LFADS generates a number of outputs for each sample, and these are all
+    saved.  They are:
+      The mean and variance of the prior of g0.
+      The control inputs (if enabled)
+      The initial conditions, g0, for all examples.
+      The generator states for all time.
+      The factors for all time.
+      The output distribution parameters (e.g. rates) for all time.
+
+    Args:
+      dataset_name: The name of the dataset to grab the factors -> rates
+      alignment matrices from.
+      output_fname: The name of the file in which to save the generated
+        samples.
+    """
+    hps = self.hps
+    batch_size = hps.batch_size
+
+    print("Generating %d samples" % (batch_size))
+    tf_vals = [self.factors, self.gen_states, self.gen_ics,
+               self.cost, self.output_dist_params]
+    if hps.ic_dim > 0:
+      tf_vals += [self.prior_zs_g0.mean, self.prior_zs_g0.logvar]
+    if hps.co_dim > 0:
+      tf_vals += [self.prior_zs_ar_con.samples_t]
+    tf_vals_flat, fidxs = flatten(tf_vals)
+
+    session = tf.get_default_session()
+    feed_dict = {}
+    feed_dict[self.dataName] = dataset_name
+    feed_dict[self.keep_prob] = 1.0
+
+    np_vals_flat = session.run(tf_vals_flat, feed_dict=feed_dict)
+
+    ff = 0
+    factors = [np_vals_flat[f] for f in fidxs[ff]]; ff += 1
+    gen_states = [np_vals_flat[f] for f in fidxs[ff]]; ff += 1
+    gen_ics = [np_vals_flat[f] for f in fidxs[ff]]; ff += 1
+    costs = [np_vals_flat[f] for f in fidxs[ff]]; ff += 1
+    output_dist_params = [np_vals_flat[f] for f in fidxs[ff]]; ff += 1
+    if hps.ic_dim > 0:
+      prior_g0_mean = [np_vals_flat[f] for f in fidxs[ff]]; ff += 1
+      prior_g0_logvar = [np_vals_flat[f] for f in fidxs[ff]]; ff += 1
+    if hps.co_dim > 0:
+      prior_zs_ar_con = [np_vals_flat[f] for f in fidxs[ff]]; ff += 1
+
+    # [0] are to take out the non-temporal items from lists
+    gen_ics = gen_ics[0]
+    costs = costs[0]
+
+    # Convert to full tensors, not lists of tensors in time dim.
+    gen_states = list_t_bxn_to_tensor_bxtxn(gen_states)
+    factors = list_t_bxn_to_tensor_bxtxn(factors)
+    output_dist_params = list_t_bxn_to_tensor_bxtxn(output_dist_params)
+    if hps.ic_dim > 0:
+      prior_g0_mean = prior_g0_mean[0]
+      prior_g0_logvar = prior_g0_logvar[0]
+    if hps.co_dim > 0:
+      prior_zs_ar_con = list_t_bxn_to_tensor_bxtxn(prior_zs_ar_con)
+
+    model_vals = {}
+    model_vals['gen_ics'] = gen_ics
+    model_vals['gen_states'] = gen_states
+    model_vals['factors'] = factors
+    model_vals['output_dist_params'] = output_dist_params
+    model_vals['costs'] = costs.reshape(1)
+    if hps.ic_dim > 0:
+      model_vals['prior_g0_mean'] = prior_g0_mean
+      model_vals['prior_g0_logvar'] = prior_g0_logvar
+    if hps.co_dim > 0:
+      model_vals['prior_zs_ar_con'] = prior_zs_ar_con
+
+    full_fname = os.path.join(hps.lfads_save_dir, output_fname)
+    write_data(full_fname, model_vals, compression='gzip')
+    print("Done.")
+
+  @staticmethod
+  def eval_model_parameters(use_nested=True, include_strs=None):
+    """Evaluate and return all of the TF variables in the model.
+
+    Args:
+    use_nested (optional): For returning values, use a nested dictoinary, based
+      on variable scoping, or return all variables in a flat dictionary.
+    include_strs (optional): A list of strings to use as a filter, to reduce the
+      number of variables returned.  A variable name must contain at least one
+      string in include_strs as a sub-string in order to be returned.
+
+    Returns:
+      The parameters of the model.  This can be in a flat
+      dictionary, or a nested dictionary, where the nesting is by variable
+      scope.
+    """
+    all_tf_vars = tf.global_variables()
+    session = tf.get_default_session()
+    all_tf_vars_eval = session.run(all_tf_vars)
+    vars_dict = {}
+    strs = ["LFADS"]
+    if include_strs:
+      strs += include_strs
+
+    for i, (var, var_eval) in enumerate(zip(all_tf_vars, all_tf_vars_eval)):
+      if any(s in include_strs for s in var.name):
+        if not isinstance(var_eval, np.ndarray): # for H5PY
+          print(var.name, """ is not numpy array, saving as numpy array 
+                with value: """, var_eval, type(var_eval))
+          e = np.array(var_eval)
+          print(e, type(e))
+        else:
+          e = var_eval
+        vars_dict[var.name] = e
+
+    if not use_nested:
+      return vars_dict
+
+    var_names = vars_dict.keys()
+    nested_vars_dict = {}
+    current_dict = nested_vars_dict
+    for v, var_name in enumerate(var_names):
+      var_split_name_list = var_name.split('/')
+      split_name_list_len = len(var_split_name_list)
+      current_dict = nested_vars_dict
+      for p, part in enumerate(var_split_name_list):
+        if p < split_name_list_len - 1:
+          if part in current_dict:
+            current_dict = current_dict[part]
+          else:
+            current_dict[part] = {}
+            current_dict = current_dict[part]
+        else:
+          current_dict[part] = vars_dict[var_name]
+
+    return nested_vars_dict
+
+  @staticmethod
+  def spikify_rates(rates_bxtxd):
+    """Randomly spikify underlying rates according a Poisson distribution
+
+    Args:
+      rates_bxtxd: a numpy tensor with shape:
+
+    Returns:
+      A numpy array with the same shape as rates_bxtxd, but with the event
+      counts.
+    """
+
+    B,T,N = rates_bxtxd.shape
+    assert all([B > 0, N > 0]), "problems"
+
+    # Because the rates are changing, there is nesting
+    spikes_bxtxd = np.zeros([B,T,N], dtype=np.int32)
+    for b in range(B):
+      for t in range(T):
+        for n in range(N):
+          rate = rates_bxtxd[b,t,n]
+          count = np.random.poisson(rate)
+          spikes_bxtxd[b,t,n] = count
+
+    return spikes_bxtxd

lfads/plot_lfads.py

+# Copyright 2017 Google Inc. 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.
+#
+# ==============================================================================
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import matplotlib
+matplotlib.use('Agg')
+from matplotlib import pyplot as plt
+import numpy as np
+import tensorflow as tf
+
+def _plot_item(W, name, full_name, nspaces):
+  plt.figure()
+  if W.shape == ():
+    print(name, ": ", W)
+  elif W.shape[0] == 1:
+    plt.stem(W.T)
+    plt.title(full_name)
+  elif W.shape[1] == 1:
+    plt.stem(W)
+    plt.title(full_name)
+  else:
+    plt.imshow(np.abs(W), interpolation='nearest', cmap='jet');
+    plt.colorbar()
+    plt.title(full_name)
+
+
+def all_plot(d, full_name="", exclude="", nspaces=0):
+  """Recursively plot all the LFADS model parameters in the nested
+  dictionary."""
+  for k, v in d.iteritems():
+    this_name = full_name+"/"+k
+    if isinstance(v, dict):
+      all_plot(v, full_name=this_name, exclude=exclude, nspaces=nspaces+4)
+    else:
+      if exclude == "" or exclude not in this_name:
+        _plot_item(v, name=k, full_name=full_name+"/"+k, nspaces=nspaces+4)
+
+
+def plot_priors():
+  g0s_prior_mean_bxn = train_modelvals['prior_g0_mean']
+  g0s_prior_var_bxn = train_modelvals['prior_g0_var']
+  g0s_post_mean_bxn = train_modelvals['posterior_g0_mean']
+  g0s_post_var_bxn = train_modelvals['posterior_g0_var']
+
+  plt.figure(figsize=(10,4), tight_layout=True);
+  plt.subplot(1,2,1)
+  plt.hist(g0s_post_mean_bxn.flatten(), bins=20, color='b');
+  plt.hist(g0s_prior_mean_bxn.flatten(), bins=20, color='g');
+
+  plt.title('Histogram of Prior/Posterior Mean Values')
+  plt.subplot(1,2,2)
+  plt.hist((g0s_post_var_bxn.flatten()), bins=20, color='b');
+  plt.hist((g0s_prior_var_bxn.flatten()), bins=20, color='g');
+  plt.title('Histogram of Prior/Posterior Log Variance Values')
+
+  plt.figure(figsize=(10,10), tight_layout=True)
+  plt.subplot(2,2,1)
+  plt.imshow(g0s_prior_mean_bxn.T, interpolation='nearest', cmap='jet')
+  plt.colorbar(fraction=0.025, pad=0.04)
+  plt.title('Prior g0 means')
+
+  plt.subplot(2,2,2)
+  plt.imshow(g0s_post_mean_bxn.T, interpolation='nearest', cmap='jet')
+  plt.colorbar(fraction=0.025, pad=0.04)
+  plt.title('Posterior g0 means');
+
+  plt.subplot(2,2,3)
+  plt.imshow(g0s_prior_var_bxn.T, interpolation='nearest', cmap='jet')
+  plt.colorbar(fraction=0.025, pad=0.04)
+  plt.title('Prior g0 variance Values')
+
+  plt.subplot(2,2,4)
+  plt.imshow(g0s_post_var_bxn.T, interpolation='nearest', cmap='jet')
+  plt.colorbar(fraction=0.025, pad=0.04)
+  plt.title('Posterior g0 variance Values')
+
+  plt.figure(figsize=(10,5))
+  plt.stem(np.sort(np.log(g0s_post_mean_bxn.std(axis=0))));
+  plt.title('Log standard deviation of h0 means');
+
+
+def plot_time_series(vals_bxtxn, bidx=None, n_to_plot=np.inf, scale=1.0,
+                     color='r', title=None):
+
+  if bidx is None:
+    vals_txn = np.mean(vals_bxtxn, axis=0)
+  else:
+    vals_txn = vals_bxtxn[bidx,:,:]
+
+  T, N = vals_txn.shape
+  if n_to_plot > N:
+    n_to_plot = N
+
+  plt.plot(vals_txn[:,0:n_to_plot] + scale*np.array(range(n_to_plot)),
+           color=color, lw=1.0)
+  plt.axis('tight')
+  if title:
+    plt.title(title)
+
+
+def plot_lfads_timeseries(data_bxtxn, model_vals, ext_input_bxtxi=None,
+                          truth_bxtxn=None, bidx=None, output_dist="poisson",
+                          conversion_factor=1.0, subplot_cidx=0,
+                          col_title=None):
+
+  n_to_plot = 10
+  scale = 1.0
+  nrows = 7
+  plt.subplot(nrows,2,1+subplot_cidx)
+
+  if output_dist == 'poisson':
+    rates = means = conversion_factor * model_vals['output_dist_params']
+    plot_time_series(rates, bidx, n_to_plot=n_to_plot, scale=scale,
+                     title=col_title + " rates (LFADS - red, Truth - black)")
+  elif output_dist == 'gaussian':
+    means_vars = model_vals['output_dist_params']
+    means, vars = np.split(means_vars,2, axis=2) # bxtxn
+    stds = np.sqrt(vars)
+    plot_time_series(means, bidx, n_to_plot=n_to_plot, scale=scale,
+                     title=col_title + " means (LFADS - red, Truth - black)")
+    plot_time_series(means+stds, bidx, n_to_plot=n_to_plot, scale=scale,
+                     color='c')
+    plot_time_series(means-stds, bidx, n_to_plot=n_to_plot, scale=scale,
+                     color='c')
+  else:
+    assert 'NIY'
+
+
+  if truth_bxtxn is not None:
+    plot_time_series(truth_bxtxn, bidx, n_to_plot=n_to_plot, color='k',
+                     scale=scale)
+
+  input_title = ""
+  if "controller_outputs" in model_vals.keys():
+    input_title += " Controller Output"
+    plt.subplot(nrows,2,3+subplot_cidx)
+    u_t = model_vals['controller_outputs'][0:-1]
+    plot_time_series(u_t, bidx, n_to_plot=n_to_plot, color='c', scale=1.0,
+                     title=col_title + input_title)
+
+  if ext_input_bxtxi is not None:
+    input_title += " External Input"
+    plot_time_series(ext_input_bxtxi, n_to_plot=n_to_plot, color='b',
+                     scale=scale, title=col_title + input_title)
+
+  plt.subplot(nrows,2,5+subplot_cidx)
+  plot_time_series(means, bidx,
+                   n_to_plot=n_to_plot, scale=1.0,
+                   title=col_title + " Spikes (LFADS - red, Spikes - black)")
+  plot_time_series(data_bxtxn, bidx, n_to_plot=n_to_plot, color='k', scale=1.0)
+
+  plt.subplot(nrows,2,7+subplot_cidx)
+  plot_time_series(model_vals['factors'], bidx, n_to_plot=n_to_plot, color='b',
+                   scale=2.0, title=col_title + " Factors")
+
+  plt.subplot(nrows,2,9+subplot_cidx)
+  plot_time_series(model_vals['gen_states'], bidx, n_to_plot=n_to_plot,
+                   color='g', scale=1.0, title=col_title + " Generator State")
+
+  if bidx is not None:
+    data_nxt = data_bxtxn[bidx,:,:].T
+    params_nxt = model_vals['output_dist_params'][bidx,:,:].T
+  else:
+    data_nxt = np.mean(data_bxtxn, axis=0).T
+    params_nxt = np.mean(model_vals['output_dist_params'], axis=0).T
+  if output_dist == 'poisson':
+    means_nxt = params_nxt
+  elif output_dist == 'gaussian': # (means+vars) x time
+    means_nxt = np.vsplit(params_nxt,2)[0] # get means
+  else:
+    assert "NIY"
+
+  plt.subplot(nrows,2,11+subplot_cidx)
+  plt.imshow(data_nxt, aspect='auto', interpolation='nearest')
+  plt.title(col_title + ' Data')
+
+  plt.subplot(nrows,2,13+subplot_cidx)
+  plt.imshow(means_nxt, aspect='auto', interpolation='nearest')
+  plt.title(col_title + ' Means')
+
+
+def plot_lfads(train_bxtxd, train_model_vals,
+               train_ext_input_bxtxi=None, train_truth_bxtxd=None,
+               valid_bxtxd=None, valid_model_vals=None,
+               valid_ext_input_bxtxi=None, valid_truth_bxtxd=None,
+               bidx=None, cf=1.0, output_dist='poisson'):
+
+  # Plotting
+  f = plt.figure(figsize=(18,20), tight_layout=True)
+  plot_lfads_timeseries(train_bxtxd, train_model_vals,
+                        train_ext_input_bxtxi,
+                        truth_bxtxn=train_truth_bxtxd,
+                        conversion_factor=cf, bidx=bidx,
+                        output_dist=output_dist, col_title='Train')
+  plot_lfads_timeseries(valid_bxtxd, valid_model_vals,
+                        valid_ext_input_bxtxi,
+                        truth_bxtxn=valid_truth_bxtxd,
+                        conversion_factor=cf, bidx=bidx,
+                        output_dist=output_dist,
+                        subplot_cidx=1, col_title='Valid')
+
+  # Convert from figure to an numpy array width x height x 3 (last for RGB)
+  f.canvas.draw()
+  data = np.fromstring(f.canvas.tostring_rgb(), dtype=np.uint8, sep='')
+  data_wxhx3 = data.reshape(f.canvas.get_width_height()[::-1] + (3,))
+  plt.close()
+
+  return data_wxhx3

lfads/run_lfads.py

+# Copyright 2017 Google Inc. 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.
+#
+# ==============================================================================
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from lfads import LFADS
+import numpy as np
+import os
+import tensorflow as tf
+import re
+import utils
+
+# Lots of hyperparameters, but most are pretty insensitive.  The
+# explanation of these hyperparameters is found below, in the flags
+# session.
+
+CHECKPOINT_PB_LOAD_NAME = "checkpoint"
+CHECKPOINT_NAME = "lfads_vae"
+CSV_LOG = "fitlog"
+OUTPUT_FILENAME_STEM = ""
+DEVICE = "gpu:0" # "cpu:0", or other gpus, e.g. "gpu:1"
+MAX_CKPT_TO_KEEP = 5
+MAX_CKPT_TO_KEEP_LVE = 5
+PS_NEXAMPLES_TO_PROCESS = 1e8 # if larger than number of examples, process all
+EXT_INPUT_DIM = 0
+IC_DIM = 64
+FACTORS_DIM = 50
+IC_ENC_DIM = 128
+GEN_DIM = 200
+GEN_CELL_INPUT_WEIGHT_SCALE = 1.0
+GEN_CELL_REC_WEIGHT_SCALE = 1.0
+CELL_WEIGHT_SCALE = 1.0
+BATCH_SIZE = 128
+LEARNING_RATE_INIT = 0.01
+LEARNING_RATE_DECAY_FACTOR = 0.95
+LEARNING_RATE_STOP = 0.00001
+LEARNING_RATE_N_TO_COMPARE = 6
+INJECT_EXT_INPUT_TO_GEN = False
+DO_TRAIN_IO_ONLY = False
+DO_RESET_LEARNING_RATE = False
+FEEDBACK_FACTORS_OR_RATES = "factors"
+
+# Calibrated just above the average value for the rnn synthetic data.
+MAX_GRAD_NORM = 200.0
+CELL_CLIP_VALUE = 5.0
+KEEP_PROB = 0.95
+TEMPORAL_SPIKE_JITTER_WIDTH = 0
+OUTPUT_DISTRIBUTION = 'poisson' # 'poisson' or 'gaussian'
+NUM_STEPS_FOR_GEN_IC = np.inf # set to num_steps if greater than num_steps
+
+DATA_DIR = "/tmp/rnn_synth_data_v1.0/"
+DATA_FILENAME_STEM = "chaotic_rnn_inputs_g1p5"
+LFADS_SAVE_DIR = "/tmp/lfads_chaotic_rnn_inputs_g1p5/"
+CO_DIM = 1
+DO_CAUSAL_CONTROLLER = False
+DO_FEED_FACTORS_TO_CONTROLLER = True
+CONTROLLER_INPUT_LAG = 1
+PRIOR_AR_AUTOCORRELATION = 10.0
+PRIOR_AR_PROCESS_VAR = 0.1
+DO_TRAIN_PRIOR_AR_ATAU = True
+DO_TRAIN_PRIOR_AR_NVAR = True
+CI_ENC_DIM = 128
+CON_DIM = 128
+CO_PRIOR_VAR_SCALE = 0.1
+KL_INCREASE_STEPS = 2000
+L2_INCREASE_STEPS = 2000
+L2_GEN_SCALE = 2000.0
+L2_CON_SCALE = 0.0
+# scale of regularizer on time correlation of inferred inputs
+CO_MEAN_CORR_SCALE = 0.0
+KL_IC_WEIGHT = 1.0
+KL_CO_WEIGHT = 1.0
+KL_START_STEP = 0
+L2_START_STEP = 0
+IC_PRIOR_VAR_MIN = 0.1
+IC_PRIOR_VAR_SCALE = 0.1
+IC_PRIOR_VAR_MAX = 0.1
+IC_POST_VAR_MIN = 0.0001      # protection from KL blowing up
+
+flags = tf.app.flags
+flags.DEFINE_string("kind", "train",
+                    "Type of model to build {train, \
+                    posterior_sample_and_average, \
+                    prior_sample, write_model_params")
+flags.DEFINE_string("output_dist", OUTPUT_DISTRIBUTION,
+                    "Type of output distribution, 'poisson' or 'gaussian'")
+flags.DEFINE_boolean("allow_gpu_growth", False,
+                     "If true, only allocate amount of memory needed for \
+                     Session. Otherwise, use full GPU memory.")
+
+# DATA
+flags.DEFINE_string("data_dir", DATA_DIR, "Data for training")
+flags.DEFINE_string("data_filename_stem", DATA_FILENAME_STEM,
+                    "Filename stem for data dictionaries.")
+flags.DEFINE_string("lfads_save_dir", LFADS_SAVE_DIR, "model save dir")
+flags.DEFINE_string("checkpoint_pb_load_name", CHECKPOINT_PB_LOAD_NAME,
+                    "Name of checkpoint files, use 'checkpoint_lve' for best \
+                    error")
+flags.DEFINE_string("checkpoint_name", CHECKPOINT_NAME,
+                    "Name of checkpoint files (.ckpt appended)")
+flags.DEFINE_string("output_filename_stem", OUTPUT_FILENAME_STEM,
+                    "Name of output file (postfix will be added)")
+flags.DEFINE_string("device", DEVICE,
+                    "Which device to use (default: \"gpu:0\", can also be \
+                    \"cpu:0\", \"gpu:1\", etc)")
+flags.DEFINE_string("csv_log", CSV_LOG,
+                    "Name of file to keep running log of fit likelihoods, \
+                    etc (.csv appended)")
+flags.DEFINE_integer("max_ckpt_to_keep", MAX_CKPT_TO_KEEP,
+                 "Max # of checkpoints to keep (rolling)")
+flags.DEFINE_integer("ps_nexamples_to_process", PS_NEXAMPLES_TO_PROCESS,
+                 "Number of examples to process for posterior sample and \
+                 average (not number of samples to average over).")
+flags.DEFINE_integer("max_ckpt_to_keep_lve", MAX_CKPT_TO_KEEP_LVE,
+                 "Max # of checkpoints to keep for lowest validation error \
+                 models (rolling)")
+flags.DEFINE_integer("ext_input_dim", EXT_INPUT_DIM, "Dimension of external \
+inputs")
+flags.DEFINE_integer("num_steps_for_gen_ic", NUM_STEPS_FOR_GEN_IC,
+                     "Number of steps to train the generator initial conditon.")
+
+
+# If there are observed inputs, there are two ways to add that observed
+# input to the model.  The first is by treating as something to be
+# inferred, and thus encoding the observed input via the encoders, and then
+# input to the generator via the "inferred inputs" channel.  Second, one
+# can input the input directly into the generator.  This has the downside
+# of making the generation process strictly dependent on knowing the
+# observed input for any generated trial.
+flags.DEFINE_boolean("inject_ext_input_to_gen",
+                     INJECT_EXT_INPUT_TO_GEN,
+                     "Should observed inputs be input to model via encoders, \
+                     or injected directly into generator?")
+
+# CELL
+
+# The combined recurrent and input weights of the encoder and
+# controller cells are by default set to scale at ws/sqrt(#inputs),
+# with ws=1.0.  You can change this scaling with this parameter.
+flags.DEFINE_float("cell_weight_scale", CELL_WEIGHT_SCALE,
+                     "Input scaling for input weights in generator.")
+
+
+# GENERATION
+
+# Note that the dimension of the initial conditions is separated from the
+# dimensions of the generator initial conditions (and a linear matrix will
+# adapt the shapes if necessary).  This is just another way to control
+# complexity.  In all likelihood, setting the ic dims to the size of the
+# generator hidden state is just fine.
+flags.DEFINE_integer("ic_dim", IC_DIM, "Dimension of h0")
+# Setting the dimensions of the factors to something smaller than the data
+# dimension is a way to get a reduced dimensionality representation of your
+# data.
+flags.DEFINE_integer("factors_dim", FACTORS_DIM,
+                     "Number of factors from generator")
+flags.DEFINE_integer("ic_enc_dim", IC_ENC_DIM,
+                     "Cell hidden size, encoder of h0")
+
+# Controlling the size of the generator is one way to control complexity of
+# the dynamics (there is also l2, which will squeeze out unnecessary
+# dynamics also).  The modern deep learning approach is to make these cells
+# as large as tolerable (from a waiting perspective), and then regularize
+# them to death with drop out or whatever.  I don't know if this is correct
+# for the LFADS application or not.
+flags.DEFINE_integer("gen_dim", GEN_DIM,
+                     "Cell hidden size, generator.")
+# The weights of the generator cell by default set to scale at
+# ws/sqrt(#inputs), with ws=1.0.  You can change ws for
+# the input weights or the recurrent weights with these hyperparameters.
+flags.DEFINE_float("gen_cell_input_weight_scale", GEN_CELL_INPUT_WEIGHT_SCALE,
+                     "Input scaling for input weights in generator.")
+flags.DEFINE_float("gen_cell_rec_weight_scale", GEN_CELL_REC_WEIGHT_SCALE,
+                     "Input scaling for rec weights in generator.")
+
+# KL DISTRIBUTIONS
+# If you don't know what you are donig here, please leave alone, the
+# defaults should be fine for most cases, irregardless of other parameters.
+#
+# If you don't want the prior variance to be learned, set the
+# following values to the same thing: ic_prior_var_min,
+# ic_prior_var_scale, ic_prior_var_max.  The prior mean will be
+# learned regardless.
+flags.DEFINE_float("ic_prior_var_min", IC_PRIOR_VAR_MIN,
+                   "Minimum variance in posterior h0 codes.")
+flags.DEFINE_float("ic_prior_var_scale", IC_PRIOR_VAR_SCALE,
+                   "Variance of ic prior distribution")
+flags.DEFINE_float("ic_prior_var_max", IC_PRIOR_VAR_MAX,
+                   "Maximum variance of IC prior distribution.")
+# If you really want to limit the information from encoder to decoder,
+# Increase ic_post_var_min above 0.0.
+flags.DEFINE_float("ic_post_var_min", IC_POST_VAR_MIN,
+                   "Minimum variance of IC posterior distribution.")
+flags.DEFINE_float("co_prior_var_scale", CO_PRIOR_VAR_SCALE,
+                   "Variance of control input prior distribution.")
+
+
+flags.DEFINE_float("prior_ar_atau",  PRIOR_AR_AUTOCORRELATION, 
+                   "Initial autocorrelation of AR(1) priors.")
+flags.DEFINE_float("prior_ar_nvar", PRIOR_AR_PROCESS_VAR, 
+                   "Initial noise variance for AR(1) priors.")
+flags.DEFINE_boolean("do_train_prior_ar_atau", DO_TRAIN_PRIOR_AR_ATAU,
+                     "Is the value for atau an init, or the constant value?")
+flags.DEFINE_boolean("do_train_prior_ar_nvar", DO_TRAIN_PRIOR_AR_NVAR,
+                     "Is the value for noise variance an init, or the constant \
+                     value?")
+
+# CONTROLLER
+# This parameter critically controls whether or not there is a controller
+# (along with controller encoders placed into the LFADS graph.  If CO_DIM >
+# 1, that means there is a 1 dimensional controller outputs, if equal to 0,
+# then no controller.
+flags.DEFINE_integer("co_dim", CO_DIM,
+    "Number of control net outputs (>0 builds that graph).")
+
+# The controller will be more powerful if it can see the encoding of the entire
+# trial.  However, this allows the controller to create inferred inputs that are
+# acausal with respect to the actual data generation process.  E.g. the data
+# generator could have an input at time t, but the controller, after seeing the
+# entirety of the trial could infer that the input is coming a little before
+# time t, because there are no restrictions on the data the controller sees.
+# One can force the controller to be causal (with respect to perturbations in
+# the data generator) so that it only sees forward encodings of the data at time
+# t that originate at times before or at time t.  One can also control the data
+# the controller sees by using an input lag (forward encoding at time [t-tlag]
+# for controller input at time t.  The same can be done in the reverse direction
+# (controller input at time t from reverse encoding at time [t+tlag], in the
+# case of an acausal controller).  Setting this lag > 0 (even lag=1) can be a
+# powerful way of avoiding very spiky decodes. Finally, one can manually control
+# whether the factors at time t-1 are fed to the controller at time t.
+#
+# If you don't care about any of this, and just want to smooth your data, set
+#    do_causal_controller = False
+#    do_feed_factors_to_controller = True
+#    causal_input_lag = 0
+flags.DEFINE_boolean("do_causal_controller",
+                     DO_CAUSAL_CONTROLLER,
+                     "Restrict the controller create only causal inferred \
+                     inputs?")
+# Strictly speaking, feeding either the factors or the rates to the controller
+# violates causality, since the g0 gets to see all the data. This may or may not
+# be only a theoretical concern.
+flags.DEFINE_boolean("do_feed_factors_to_controller", 
+                     DO_FEED_FACTORS_TO_CONTROLLER, 
+                     "Should factors[t-1] be input to controller at time t?")
+flags.DEFINE_string("feedback_factors_or_rates", FEEDBACK_FACTORS_OR_RATES,
+                    "Feedback the factors or the rates to the controller? \
+                     Acceptable values: 'factors' or 'rates'.")
+flags.DEFINE_integer("controller_input_lag", CONTROLLER_INPUT_LAG, 
+                     "Time lag on the encoding to controller t-lag for \
+                     forward, t+lag for reverse.")
+
+flags.DEFINE_integer("ci_enc_dim", CI_ENC_DIM,
+                     "Cell hidden size, encoder of control inputs")
+flags.DEFINE_integer("con_dim", CON_DIM,
+                     "Cell hidden size, controller")
+
+
+# OPTIMIZATION
+flags.DEFINE_integer("batch_size", BATCH_SIZE,
+                     "Batch size to use during training.")
+flags.DEFINE_float("learning_rate_init", LEARNING_RATE_INIT,
+                   "Learning rate initial value")
+flags.DEFINE_float("learning_rate_decay_factor", LEARNING_RATE_DECAY_FACTOR,
+                   "Learning rate decay, decay by this fraction every so \
+                   often.")
+flags.DEFINE_float("learning_rate_stop", LEARNING_RATE_STOP,
+                   "The lr is adaptively reduced, stop training at this value.")
+# Rather put the learning rate on an exponentially decreasiong schedule,
+# the current algorithm pays attention to the learning rate, and if it
+# isn't regularly decreasing, it will decrease the learning rate.  So far,
+# it works fine, though it is not perfect.
+flags.DEFINE_integer("learning_rate_n_to_compare", LEARNING_RATE_N_TO_COMPARE,
+                     "Number of previous costs current cost has to be worse \
+                     than, to lower learning rate.")
+
+# This sets a value, above which, the gradients will be clipped.  This hp
+# is extremely useful to avoid an infrequent, but highly pathological
+# problem whereby the gradient is so large that it destroys the
+# optimziation by setting parameters too large, leading to a vicious cycle
+# that ends in NaNs.  If it's too large, it's useless, if it's too small,
+# it essentially becomes the learning rate.  It's pretty insensitive, though.
+flags.DEFINE_float("max_grad_norm", MAX_GRAD_NORM,
+                   "Max norm of gradient before clipping.")
+
+# If your optimizations start "NaN-ing out", reduce this value so that
+# the values of the network don't grow out of control.  Typically, once
+# this parameter is set to a reasonable value, one stops having numerical
+# problems.
+flags.DEFINE_float("cell_clip_value", CELL_CLIP_VALUE,
+                   "Max value recurrent cell can take before being clipped.")
+
+# This flag is used for an experiment where one sees if training a model with
+# many days data can be used to learn the dynamics from a held-out days data.
+# If you don't care about that particular experiment, this flag should always be
+# false.
+flags.DEFINE_boolean("do_train_io_only", DO_TRAIN_IO_ONLY,
+                     "Train only the input (readin) and output (readout) \
+                     affine functions.")
+
+flags.DEFINE_boolean("do_reset_learning_rate", DO_RESET_LEARNING_RATE,
+                     "Reset the learning rate to initial value.")
+
+
+# OVERFITTING
+# Dropout is done on the input data, on controller inputs (from
+# encoder), on outputs from generator to factors.
+flags.DEFINE_float("keep_prob", KEEP_PROB, "Dropout keep probability.")
+# It appears that the system will happily fit spikes (blessing or
+# curse, depending).  You may not want this.  Jittering the spikes a
+# bit will help (-/+ bin size, as specified here).
+flags.DEFINE_integer("temporal_spike_jitter_width",
+                     TEMPORAL_SPIKE_JITTER_WIDTH,
+                     "Shuffle spikes around this window.")
+
+# General note about helping ascribe controller inputs vs dynamics:
+#
+# If controller is heavily penalized, then it won't have any output.
+# If dynamics are heavily penalized, then generator won't make
+# dynamics.  Note this l2 penalty is only on the recurrent portion of
+# the RNNs, as dropout is also available, penalizing the feed-forward
+# connections.
+flags.DEFINE_float("l2_gen_scale", L2_GEN_SCALE,
+                   "L2 regularization cost for the generator only.")
+flags.DEFINE_float("l2_con_scale", L2_CON_SCALE,
+                   "L2 regularization cost for the controller only.")
+flags.DEFINE_float("co_mean_corr_scale", CO_MEAN_CORR_SCALE,
+                   "Cost of correlation (thru time)in the means of \
+                   controller output.")
+
+# UNDERFITTING
+# If the primary task of LFADS is "filtering" of data and not
+# generation, then it is possible that the KL penalty is too strong.
+# Empirically, we have found this to be the case.  So we add a
+# hyperparameter in front of the the two KL terms (one for the initial
+# conditions to the generator, the other for the controller outputs).
+# You should always think of the the default values as 1.0, and that
+# leads to a standard VAE formulation whereby the numbers that are
+# optimized are a lower-bound on the log-likelihood of the data. When
+# these 2 HPs deviate from 1.0, one cannot make any statement about
+# what those LL lower bounds mean anymore, and they cannot be compared
+# (AFAIK).
+flags.DEFINE_float("kl_ic_weight", KL_IC_WEIGHT,
+                   "Strength of KL weight on initial conditions KL penatly.")
+flags.DEFINE_float("kl_co_weight", KL_CO_WEIGHT,
+                   "Strength of KL weight on controller output KL penalty.")
+
+# Sometimes the task can be sufficiently hard to learn that the
+# optimizer takes the 'easy route', and simply minimizes the KL
+# divergence, setting it to near zero, and the optimization gets
+# stuck.  These two parameters will help avoid that by by getting the
+# optimization to 'latch' on to the main optimization, and only
+# turning in the regularizers later.
+flags.DEFINE_integer("kl_start_step", KL_START_STEP,
+                     "Start increasing weight after this many steps.")
+# training passes, not epochs, increase by 0.5 every kl_increase_steps
+flags.DEFINE_integer("kl_increase_steps", KL_INCREASE_STEPS,
+                     "Increase weight of kl cost to avoid local minimum.")
+# Same story for l2 regularizer.  One wants a simple generator, for scientific
+# reasons, but not at the expense of hosing the optimization.
+flags.DEFINE_integer("l2_start_step", L2_START_STEP,
+                     "Start increasing l2 weight after this many steps.")
+flags.DEFINE_integer("l2_increase_steps", L2_INCREASE_STEPS,
+                     "Increase weight of l2 cost to avoid local minimum.")
+
+FLAGS = flags.FLAGS
+
+
+def build_model(hps, kind="train", datasets=None):
+  """Builds a model from either random initialization, or saved parameters.
+
+  Args:
+    hps: The hyper parameters for the model.
+    kind: (optional) The kind of model to build.  Training vs inference require
+      different graphs.
+    datasets: The datasets structure (see top of lfads.py).
+
+  Returns:
+    an LFADS model.
+  """
+
+  build_kind = kind
+  if build_kind == "write_model_params":
+    build_kind = "train"
+  with tf.variable_scope("LFADS", reuse=None):
+    model = LFADS(hps, kind=build_kind, datasets=datasets)
+
+  if not os.path.exists(hps.lfads_save_dir):
+    print("Save directory %s does not exist, creating it." % hps.lfads_save_dir)
+    os.makedirs(hps.lfads_save_dir)
+
+  cp_pb_ln = hps.checkpoint_pb_load_name
+  cp_pb_ln = 'checkpoint' if cp_pb_ln == "" else cp_pb_ln
+  if cp_pb_ln == 'checkpoint':
+    print("Loading latest training checkpoint in: ", hps.lfads_save_dir)
+    saver = model.seso_saver
+  elif cp_pb_ln == 'checkpoint_lve':
+    print("Loading lowest validation checkpoint in: ", hps.lfads_save_dir)
+    saver = model.lve_saver
+  else:
+    print("Loading checkpoint: ", cp_pb_ln, ", in: ", hps.lfads_save_dir)
+    saver = model.seso_saver
+
+  ckpt = tf.train.get_checkpoint_state(hps.lfads_save_dir,
+                                       latest_filename=cp_pb_ln)
+
+  session = tf.get_default_session()
+  print("ckpt: ", ckpt)
+  if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
+    print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
+    saver.restore(session, ckpt.model_checkpoint_path)
+  else:
+    print("Created model with fresh parameters.")
+    if kind in ["posterior_sample_and_average", "prior_sample",
+                "write_model_params"]:
+      print("Possible error!!! You are running ", kind, " on a newly \
+      initialized model!")
+      print("Are you sure you sure ", ckpt.model_checkpoint_path, " exists?")
+
+    tf.global_variables_initializer().run()
+
+  if ckpt:
+    train_step_str = re.search('-[0-9]+$', ckpt.model_checkpoint_path).group()
+  else:
+    train_step_str = '-0'
+
+  fname = 'hyperparameters' + train_step_str + '.txt'
+  hp_fname = os.path.join(hps.lfads_save_dir, fname)
+  hps_for_saving = jsonify_dict(hps)
+  utils.write_data(hp_fname, hps_for_saving, use_json=True)
+
+  return model
+
+
+def jsonify_dict(d):
+  """Turns python booleans into strings so hps dict can be written in json.
+  Creates a shallow-copied dictionary first, then accomplishes string
+  conversion.
+
+  Args: 
+    d: hyperparameter dictionary
+
+  Returns: hyperparameter dictionary with bool's as strings
+  """
+
+  d2 = d.copy()   # shallow copy is fine by assumption of d being shallow
+  def jsonify_bool(boolean_value):
+    if boolean_value:
+      return "true"
+    else:
+      return "false"
+
+  for key in d2.keys():
+    if isinstance(d2[key], bool):
+      d2[key] = jsonify_bool(d2[key])
+  return d2
+
+
+def build_hyperparameter_dict(flags):
+  """Simple script for saving hyper parameters.  Under the hood the
+  flags structure isn't a dictionary, so it has to be simplified since we
+  want to be able to view file as text.
+
+  Args:
+    flags: From tf.app.flags
+
+  Returns:
+    dictionary of hyper parameters (ignoring other flag types).
+  """
+  d = {}
+  # Data
+  d['output_dist'] = flags.output_dist
+  d['data_dir'] = flags.data_dir
+  d['lfads_save_dir'] = flags.lfads_save_dir
+  d['checkpoint_pb_load_name'] = flags.checkpoint_pb_load_name
+  d['checkpoint_name'] = flags.checkpoint_name
+  d['output_filename_stem'] = flags.output_filename_stem
+  d['max_ckpt_to_keep'] = flags.max_ckpt_to_keep
+  d['max_ckpt_to_keep_lve'] = flags.max_ckpt_to_keep_lve
+  d['ps_nexamples_to_process'] = flags.ps_nexamples_to_process
+  d['ext_input_dim'] = flags.ext_input_dim
+  d['data_filename_stem'] = flags.data_filename_stem
+  d['device'] = flags.device
+  d['csv_log'] = flags.csv_log
+  d['num_steps_for_gen_ic'] = flags.num_steps_for_gen_ic
+  d['inject_ext_input_to_gen'] = flags.inject_ext_input_to_gen
+  # Cell
+  d['cell_weight_scale'] = flags.cell_weight_scale
+  # Generation
+  d['ic_dim'] = flags.ic_dim
+  d['factors_dim'] = flags.factors_dim
+  d['ic_enc_dim'] = flags.ic_enc_dim
+  d['gen_dim'] = flags.gen_dim
+  d['gen_cell_input_weight_scale'] = flags.gen_cell_input_weight_scale
+  d['gen_cell_rec_weight_scale'] = flags.gen_cell_rec_weight_scale
+  # KL distributions
+  d['ic_prior_var_min'] = flags.ic_prior_var_min
+  d['ic_prior_var_scale'] = flags.ic_prior_var_scale
+  d['ic_prior_var_max'] = flags.ic_prior_var_max
+  d['ic_post_var_min'] = flags.ic_post_var_min
+  d['co_prior_var_scale'] = flags.co_prior_var_scale
+  d['prior_ar_atau'] = flags.prior_ar_atau
+  d['prior_ar_nvar'] =  flags.prior_ar_nvar
+  d['do_train_prior_ar_atau'] = flags.do_train_prior_ar_atau
+  d['do_train_prior_ar_nvar'] = flags.do_train_prior_ar_nvar
+  # Controller
+  d['do_causal_controller'] = flags.do_causal_controller
+  d['controller_input_lag'] = flags.controller_input_lag
+  d['do_feed_factors_to_controller'] = flags.do_feed_factors_to_controller
+  d['feedback_factors_or_rates'] = flags.feedback_factors_or_rates
+  d['co_dim'] = flags.co_dim
+  d['ci_enc_dim'] = flags.ci_enc_dim
+  d['con_dim'] = flags.con_dim
+  d['co_mean_corr_scale'] = flags.co_mean_corr_scale
+  # Optimization
+  d['batch_size'] = flags.batch_size
+  d['learning_rate_init'] = flags.learning_rate_init
+  d['learning_rate_decay_factor'] = flags.learning_rate_decay_factor
+  d['learning_rate_stop'] = flags.learning_rate_stop
+  d['learning_rate_n_to_compare'] = flags.learning_rate_n_to_compare
+  d['max_grad_norm'] = flags.max_grad_norm
+  d['cell_clip_value'] = flags.cell_clip_value
+  d['do_train_io_only'] = flags.do_train_io_only
+  d['do_reset_learning_rate'] = flags.do_reset_learning_rate
+
+  # Overfitting
+  d['keep_prob'] = flags.keep_prob
+  d['temporal_spike_jitter_width'] = flags.temporal_spike_jitter_width
+  d['l2_gen_scale'] = flags.l2_gen_scale
+  d['l2_con_scale'] = flags.l2_con_scale
+  # Underfitting
+  d['kl_ic_weight'] = flags.kl_ic_weight
+  d['kl_co_weight'] = flags.kl_co_weight
+  d['kl_start_step'] = flags.kl_start_step
+  d['kl_increase_steps'] = flags.kl_increase_steps
+  d['l2_start_step'] = flags.l2_start_step
+  d['l2_increase_steps'] = flags.l2_increase_steps
+
+  return d
+
+
+class hps_dict_to_obj(dict):
+  """Helper class allowing us to access hps dictionary more easily."""
+
+  def __getattr__(self, key):
+    if key in self:
+      return self[key]
+    else:
+      assert False, ("%s does not exist." % key)
+  def __setattr__(self, key, value):
+    self[key] = value
+
+
+def train(hps, datasets):
+  """Train the LFADS model.
+
+  Args:
+    hps: The dictionary of hyperparameters.
+    datasets: A dictionary of data dictionaries.  The dataset dict is simply a
+      name(string)-> data dictionary mapping (See top of lfads.py).
+  """
+  model = build_model(hps, kind="train", datasets=datasets)
+  if hps.do_reset_learning_rate:
+    sess = tf.get_default_session()
+    sess.run(model.learning_rate.initializer)
+
+  model.train_model(datasets)
+
+
+def write_model_runs(hps, datasets, output_fname=None):
+  """Run the model on the data in data_dict, and save the computed values.
+
+  LFADS generates a number of outputs for each examples, and these are all
+  saved.  They are:
+    The mean and variance of the prior of g0.
+    The mean and variance of approximate posterior of g0.
+    The control inputs (if enabled)
+    The initial conditions, g0, for all examples.
+    The generator states for all time.
+    The factors for all time.
+    The rates for all time.
+
+  Args:
+    hps: The dictionary of hyperparameters.
+    datasets: A dictionary of data dictionaries.  The dataset dict is simply a
+      name(string)-> data dictionary mapping (See top of lfads.py).
+    output_fname (optional): output filename stem to write the model runs.
+  """
+  model = build_model(hps, kind=hps.kind, datasets=datasets)
+  model.write_model_runs(datasets, output_fname)
+
+
+def write_model_samples(hps, datasets, dataset_name=None, output_fname=None):
+  """Use the prior distribution to generate samples from the model.
+  Generates batch_size number of samples (set through FLAGS).
+
+  LFADS generates a number of outputs for each examples, and these are all
+  saved.  They are:
+    The mean and variance of the prior of g0.
+    The control inputs (if enabled)
+    The initial conditions, g0, for all examples.
+    The generator states for all time.
+    The factors for all time.
+    The output distribution parameters (e.g. rates) for all time.
+
+  Args:
+    hps: The dictionary of hyperparameters.
+    datasets: A dictionary of data dictionaries.  The dataset dict is simply a
+      name(string)-> data dictionary mapping (See top of lfads.py).
+    dataset_name: The name of the dataset to grab the factors -> rates
+      alignment matrices from. Only a concern with models trained on
+      multi-session data. By default, uses the first dataset in the data dict.
+    output_fname: The name prefix of the file in which to save the generated
+      samples.
+  """
+  if not output_fname:
+    output_fname = "model_runs_" + hps.kind
+  else:
+    output_fname = output_fname + "model_runs_" + hps.kind
+  if not dataset_name:
+    dataset_name = datasets.keys()[0]
+  else:
+    if dataset_name not in datasets.keys():
+      raise ValueError("Invalid dataset name '%s'."%(dataset_name))
+  model = build_model(hps, kind=hps.kind, datasets=datasets)
+  model.write_model_samples(dataset_name, output_fname)
+
+
+def write_model_parameters(hps, output_fname=None, datasets=None):
+  """Save all the model parameters
+
+  Save all the parameters to hps.lfads_save_dir.
+
+  Args:
+    hps: The dictionary of hyperparameters.
+    output_fname: The prefix of the file in which to save the generated
+      samples.
+    datasets: A dictionary of data dictionaries.  The dataset dict is simply a
+      name(string)-> data dictionary mapping (See top of lfads.py).
+  """
+  if not output_fname:
+    output_fname = "model_params"
+  else:
+    output_fname = output_fname + "_model_params"
+  fname = os.path.join(hps.lfads_save_dir, output_fname)
+  print("Writing model parameters to: ", fname)
+  # save the optimizer params as well
+  model = build_model(hps, kind="write_model_params", datasets=datasets) 
+  model_params = model.eval_model_parameters(use_nested=False,
+                                             include_strs="LFADS")
+  utils.write_data(fname, model_params, compression=None)
+  print("Done.")
+
+
+def clean_data_dict(data_dict):
+  """Add some key/value pairs to the data dict, if they are missing.
+  Args:
+    data_dict - dictionary containing data for LFADS
+  Returns:
+    data_dict with some keys filled in, if they are absent.
+  """
+
+  keys = ['train_truth', 'train_ext_input', 'valid_data',
+          'valid_truth', 'valid_ext_input', 'valid_train']
+  for k in keys:
+    if k not in data_dict:
+      data_dict[k] = None
+
+  return data_dict
+
+
+def load_datasets(data_dir, data_filename_stem):
+  """Load the datasets from a specified directory.
+
+  Example files look like
+    >data_dir/my_dataset_first_day
+    >data_dir/my_dataset_second_day
+
+  If my_dataset (filename) stem is in the directory, the read routine will try
+  and load it.  The datasets dictionary will then look like
+  dataset['first_day'] -> (first day data dictionary)
+  dataset['second_day'] -> (first day data dictionary)
+
+  Args:
+    data_dir: The directory from which to load the datasets.
+    data_filename_stem: The stem of the filename for the datasets.
+
+  Returns:
+    datasets: a dataset dictionary, with one name->data dictionary pair for
+    each dataset file.
+  """
+  print("Reading data from ", data_dir)
+  datasets = utils.read_datasets(data_dir, data_filename_stem)
+  for k, data_dict in datasets.items():
+    datasets[k] = clean_data_dict(data_dict)
+
+    train_total_size = len(data_dict['train_data'])
+    if train_total_size == 0:
+      print("Did not load training set.")
+    else:
+      print("Found training set with number examples: ", train_total_size)
+
+    valid_total_size = len(data_dict['valid_data'])
+    if valid_total_size == 0:
+      print("Did not load validation set.")
+    else:
+      print("Found validation set with number examples: ", valid_total_size)
+
+  return datasets
+
+
+def main(_):
+  """Get this whole shindig off the ground."""
+  d = build_hyperparameter_dict(FLAGS)
+  hps = hps_dict_to_obj(d)    # hyper parameters
+  kind = FLAGS.kind
+
+  # Read the data, if necessary.
+  train_set = valid_set = None
+  if kind in ["train", "posterior_sample_and_average", "prior_sample",
+              "write_model_params"]:
+    datasets = load_datasets(hps.data_dir, hps.data_filename_stem)
+  else:
+    raise ValueError('Kind {} is not supported.'.format(kind))
+
+  # infer the dataset names and dataset dimensions from the loaded files
+  hps.kind = kind     # needs to be added here, cuz not saved as hyperparam
+  hps.dataset_names = []
+  hps.dataset_dims = {}
+  for key in datasets:
+    hps.dataset_names.append(key)
+    hps.dataset_dims[key] = datasets[key]['data_dim']
+
+  # also store down the dimensionality of the data
+  # - just pull from one set, required to be same for all sets
+  hps.num_steps = datasets.values()[0]['num_steps']
+  hps.ndatasets = len(hps.dataset_names)
+
+  if hps.num_steps_for_gen_ic > hps.num_steps:
+    hps.num_steps_for_gen_ic = hps.num_steps
+
+  # Build and run the model, for varying purposes.
+  config = tf.ConfigProto(allow_soft_placement=True,
+                          log_device_placement=False)
+  if FLAGS.allow_gpu_growth:
+    config.gpu_options.allow_growth = True
+  sess = tf.Session(config=config)
+  with sess.as_default():
+    with tf.device(hps.device):
+      if kind == "train":
+        train(hps, datasets)
+      elif kind == "posterior_sample_and_average":
+        write_model_runs(hps, datasets, hps.output_filename_stem)
+      elif kind == "prior_sample":
+        write_model_samples(hps, datasets, hps.output_filename_stem)
+      elif kind == "write_model_params":
+        write_model_parameters(hps, hps.output_filename_stem, datasets)
+      else:
+        assert False, ("Kind %s is not implemented. " % kind)
+
+
+if __name__ == "__main__":
+    tf.app.run()
+ 

lfads/synth_data/generate_chaotic_rnn_data.py

+# Copyright 2017 Google Inc. 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.
+#
+# ==============================================================================
+from __future__ import print_function
+
+import h5py
+import numpy as np
+import os
+import tensorflow as tf         # used for flags here
+
+from utils import write_datasets
+from synthetic_data_utils import add_alignment_projections, generate_data
+from synthetic_data_utils import generate_rnn, get_train_n_valid_inds
+from synthetic_data_utils import nparray_and_transpose
+from synthetic_data_utils import spikify_data, split_list_by_inds
+import matplotlib
+import matplotlib.pyplot as plt
+import scipy.signal
+
+matplotlib.rcParams['image.interpolation'] = 'nearest'
+DATA_DIR = "rnn_synth_data_v1.0"
+
+flags = tf.app.flags
+flags.DEFINE_string("save_dir", "/tmp/" + DATA_DIR + "/",
+                    "Directory for saving data.")
+flags.DEFINE_string("datafile_name", "thits_data",
+                    "Name of data file for input case.")
+flags.DEFINE_integer("synth_data_seed", 5, "Random seed for RNN generation.")
+flags.DEFINE_float("T", 1.0, "Time in seconds to generate.")
+flags.DEFINE_integer("C", 100, "Number of conditions")
+flags.DEFINE_integer("N", 50, "Number of units for the RNN")
+flags.DEFINE_integer("S", 50, "Number of sampled units from RNN")
+flags.DEFINE_integer("npcs", 10, "Number of PCS for multi-session case.")
+flags.DEFINE_float("train_percentage", 4.0/5.0,
+                   "Percentage of train vs validation trials")
+flags.DEFINE_integer("nspikifications", 40,
+                     "Number of spikifications of the same underlying rates.")
+flags.DEFINE_float("g", 1.5, "Complexity of dynamics")
+flags.DEFINE_float("x0_std", 1.0,
+                   "Volume from which to pull initial conditions (affects diversity of dynamics.")
+flags.DEFINE_float("tau", 0.025, "Time constant of RNN")
+flags.DEFINE_float("dt", 0.010, "Time bin")
+flags.DEFINE_float("input_magnitude", 20.0,
+                   "For the input case, what is the value of the input?")
+flags.DEFINE_float("max_firing_rate", 30.0, "Map 1.0 of RNN to a spikes per second")
+FLAGS = flags.FLAGS
+
+
+# Note that with N small, (as it is 25 above), the finite size effects
+# will have pretty dramatic effects on the dynamics of the random RNN.
+# If you want more complex dynamics, you'll have to run the script a
+# lot, or increase N (or g).
+
+# Getting hard vs. easy data can be a little stochastic, so we set the seed.
+
+# Pull out some commonly used parameters.
+# These are user parameters (configuration)
+rng = np.random.RandomState(seed=FLAGS.synth_data_seed)
+T = FLAGS.T
+C = FLAGS.C
+N = FLAGS.N
+S = FLAGS.S
+input_magnitude = FLAGS.input_magnitude
+nspikifications = FLAGS.nspikifications
+E = nspikifications * C         # total number of trials
+# S is the number of measurements in each datasets, w/ each
+# dataset having a different set of observations.
+ndatasets = N/S                 # ok if rounded down
+train_percentage = FLAGS.train_percentage
+ntime_steps = int(T / FLAGS.dt)
+# End of user parameters
+
+rnn = generate_rnn(rng, N, FLAGS.g, FLAGS.tau, FLAGS.dt, FLAGS.max_firing_rate)
+
+# Check to make sure the RNN is the one we used in the paper.
+if N == 50:
+  assert abs(rnn['W'][0,0] - 0.06239899) < 1e-8, 'Error in random seed?'
+  rem_check = nspikifications * train_percentage
+  assert  abs(rem_check - int(rem_check)) < 1e-8, \
+    'Train percentage  * nspikifications should be integral number.'
+
+
+# Initial condition generation, and condition label generation.  This
+# happens outside of the dataset loop, so that all datasets have the
+# same conditions, which is similar to a neurophys setup.
+condition_number = 0
+x0s = []
+condition_labels = []
+for c in range(C):
+  x0 = FLAGS.x0_std * rng.randn(N, 1)
+  x0s.append(np.tile(x0, nspikifications)) # replicate x0 nspikifications times
+  # replicate the condition label nspikifications times
+  for ns in range(nspikifications):
+    condition_labels.append(condition_number)
+  condition_number += 1
+x0s = np.concatenate(x0s, axis=1)
+
+# Containers for storing data across data.
+datasets = {}
+for n in range(ndatasets):
+  print(n+1, " of ", ndatasets)
+
+  # First generate all firing rates. in the next loop, generate all
+  # spikifications this allows the random state for rate generation to be
+  # independent of n_spikifications.
+  dataset_name = 'dataset_N' + str(N) + '_S' + str(S)
+  if S < N:
+    dataset_name += '_n' + str(n+1)
+
+  # Sample neuron subsets.  The assumption is the PC axes of the RNN
+  # are not unit aligned, so sampling units is adequate to sample all
+  # the high-variance PCs.
+  P_sxn = np.eye(S,N)
+  for m in range(n):
+    P_sxn = np.roll(P_sxn, S, axis=1)
+
+  if input_magnitude > 0.0:
+    # time of "hits" randomly chosen between [1/4 and 3/4] of total time
+    input_times = rng.choice(int(ntime_steps/2), size=[E]) + int(ntime_steps/4)
+  else:
+    input_times = None
+
+  rates, x0s, inputs = \
+      generate_data(rnn, T=T, E=E, x0s=x0s, P_sxn=P_sxn,
+                    input_magnitude=input_magnitude,
+                    input_times=input_times)
+  spikes = spikify_data(rates, rng, rnn['dt'], rnn['max_firing_rate'])
+
+  # split into train and validation sets
+  train_inds, valid_inds = get_train_n_valid_inds(E, train_percentage,
+                                                  nspikifications)
+
+  # Split the data, inputs, labels and times into train vs. validation.
+  rates_train, rates_valid = \
+      split_list_by_inds(rates, train_inds, valid_inds)
+  spikes_train, spikes_valid = \
+      split_list_by_inds(spikes, train_inds, valid_inds)
+  input_train, inputs_valid = \
+      split_list_by_inds(inputs, train_inds, valid_inds)
+  condition_labels_train, condition_labels_valid = \
+      split_list_by_inds(condition_labels, train_inds, valid_inds)
+  input_times_train, input_times_valid = \
+      split_list_by_inds(input_times, train_inds, valid_inds)
+
+  # Turn rates, spikes, and input into numpy arrays.
+  rates_train = nparray_and_transpose(rates_train)
+  rates_valid = nparray_and_transpose(rates_valid)
+  spikes_train = nparray_and_transpose(spikes_train)
+  spikes_valid = nparray_and_transpose(spikes_valid)
+  input_train = nparray_and_transpose(input_train)
+  inputs_valid = nparray_and_transpose(inputs_valid)
+
+  # Note that we put these 'truth' rates and input into this
+  # structure, the only data that is used in LFADS are the spike
+  # trains.  The rest is either for printing or posterity.
+  data = {'train_truth': rates_train,
+          'valid_truth': rates_valid,
+          'input_train_truth' : input_train,
+          'input_valid_truth' : inputs_valid,
+          'train_data' : spikes_train,
+          'valid_data' : spikes_valid,
+          'train_percentage' : train_percentage,
+          'nspikifications' : nspikifications,
+          'dt' : rnn['dt'],
+          'input_magnitude' : input_magnitude,
+          'input_times_train' : input_times_train,
+          'input_times_valid' : input_times_valid,
+          'P_sxn' : P_sxn,
+          'condition_labels_train' : condition_labels_train,
+          'condition_labels_valid' : condition_labels_valid,
+          'conversion_factor': 1.0 / rnn['conversion_factor']}
+  datasets[dataset_name] = data
+
+if S < N:
+  # Note that this isn't necessary for this synthetic example, but
+  # it's useful to see how the input factor matrices were initialized
+  # for actual neurophysiology data.
+  datasets = add_alignment_projections(datasets, npcs=FLAGS.npcs)
+
+# Write out the datasets.
+write_datasets(FLAGS.save_dir, FLAGS.datafile_name, datasets)

lfads/synth_data/generate_itb_data.py

+# Copyright 2017 Google Inc. 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.
+#
+# ==============================================================================
+from __future__ import print_function
+
+import h5py
+import numpy as np
+import os
+import tensorflow as tf
+
+from utils import write_datasets
+from synthetic_data_utils import normalize_rates
+from synthetic_data_utils import get_train_n_valid_inds, nparray_and_transpose
+from synthetic_data_utils import spikify_data, split_list_by_inds
+
+DATA_DIR = "rnn_synth_data_v1.0"
+
+flags = tf.app.flags
+flags.DEFINE_string("save_dir", "/tmp/" + DATA_DIR + "/",
+                    "Directory for saving data.")
+flags.DEFINE_string("datafile_name", "itb_rnn",
+                    "Name of data file for input case.")
+flags.DEFINE_integer("synth_data_seed", 5, "Random seed for RNN generation.")
+flags.DEFINE_float("T", 1.0, "Time in seconds to generate.")
+flags.DEFINE_integer("C", 800, "Number of conditions")
+flags.DEFINE_integer("N", 50, "Number of units for the RNN")
+flags.DEFINE_float("train_percentage", 4.0/5.0,
+                   "Percentage of train vs validation trials")
+flags.DEFINE_integer("nspikifications", 5,
+                     "Number of spikifications of the same underlying rates.")
+flags.DEFINE_float("tau", 0.025, "Time constant of RNN")
+flags.DEFINE_float("dt", 0.010, "Time bin")
+flags.DEFINE_float("max_firing_rate", 30.0,
+                   "Map 1.0 of RNN to a spikes per second")
+flags.DEFINE_float("u_std", 0.25,
+                   "Std dev of input to integration to bound model")
+flags.DEFINE_string("checkpoint_path", "SAMPLE_CHECKPOINT",
+                    """Path to directory with checkpoints of model 
+                    trained on integration to bound task. Currently this
+                    is a placeholder which tells the code to grab the
+                    checkpoint that is provided with the code 
+                    (in /trained_itb/..). If you have your own checkpoint 
+                    you would like to restore, you would point it to 
+                    that path.""")
+FLAGS = flags.FLAGS
+
+
+class IntegrationToBoundModel:
+  def __init__(self, N):
+    scale = 0.8 / float(N**0.5)
+    self.N = N
+    self.Wh_nxn = tf.Variable(tf.random_normal([N, N], stddev=scale))
+    self.b_1xn = tf.Variable(tf.zeros([1, N]))
+    self.Bu_1xn = tf.Variable(tf.zeros([1, N]))
+    self.Wro_nxo = tf.Variable(tf.random_normal([N, 1], stddev=scale))
+    self.bro_o = tf.Variable(tf.zeros([1]))
+
+  def call(self, h_tm1_bxn, u_bx1):
+    act_t_bxn = tf.matmul(h_tm1_bxn, self.Wh_nxn) + self.b_1xn + u_bx1 * self.Bu_1xn
+    h_t_bxn = tf.nn.tanh(act_t_bxn)
+    z_t = tf.nn.xw_plus_b(h_t_bxn, self.Wro_nxo, self.bro_o)
+    return z_t, h_t_bxn
+
+def get_data_batch(batch_size, T, rng, u_std):
+  u_bxt = rng.randn(batch_size, T) * u_std
+  running_sum_b = np.zeros([batch_size])
+  labels_bxt = np.zeros([batch_size, T])
+  for t in xrange(T):
+    running_sum_b += u_bxt[:, t]
+    labels_bxt[:, t] += running_sum_b
+  labels_bxt = np.clip(labels_bxt, -1, 1)
+  return u_bxt, labels_bxt
+
+
+rng = np.random.RandomState(seed=FLAGS.synth_data_seed)
+u_rng = np.random.RandomState(seed=FLAGS.synth_data_seed+1)
+T = FLAGS.T
+C = FLAGS.C
+N = FLAGS.N  # must be same N as in trained model (provided example is N = 50)
+nspikifications = FLAGS.nspikifications
+E = nspikifications * C  # total number of trials
+train_percentage = FLAGS.train_percentage
+ntimesteps = int(T / FLAGS.dt)
+batch_size = 1  # gives one example per ntrial
+
+model = IntegrationToBoundModel(N)
+inputs_ph_t = [tf.placeholder(tf.float32,
+                              shape=[None, 1]) for _ in range(ntimesteps)]
+state = tf.zeros([batch_size, N])
+saver = tf.train.Saver()
+
+P_nxn = rng.randn(N,N) / np.sqrt(N)  # random projections
+
+# unroll RNN for T timesteps
+outputs_t = []
+states_t = []
+
+for inp in inputs_ph_t:
+  output, state = model.call(state, inp)
+  outputs_t.append(output)
+  states_t.append(state)
+
+with tf.Session() as sess:
+  # restore the latest model ckpt
+  if FLAGS.checkpoint_path == "SAMPLE_CHECKPOINT":
+    dir_path = os.path.dirname(os.path.realpath(__file__))
+    model_checkpoint_path = os.path.join(dir_path, "trained_itb/model-65000")
+  else:
+    model_checkpoint_path = FLAGS.checkpoint_path
+  try:
+    saver.restore(sess, model_checkpoint_path)
+    print ('Model restored from', model_checkpoint_path)
+  except:
+    assert False, ("No checkpoints to restore from, is the path %s correct?"
+                   %model_checkpoint_path)
+
+  # generate data for trials
+  data_e = []
+  u_e = []
+  outs_e = []
+  for c in range(C):
+    u_1xt, outs_1xt = get_data_batch(batch_size, ntimesteps, u_rng, FLAGS.u_std)
+
+    feed_dict = {}
+    for t in xrange(ntimesteps):
+      feed_dict[inputs_ph_t[t]] = np.reshape(u_1xt[:,t], (batch_size,-1))
+
+    states_t_bxn, outputs_t_bxn = sess.run([states_t, outputs_t],
+                                           feed_dict=feed_dict)
+    states_nxt = np.transpose(np.squeeze(np.asarray(states_t_bxn)))
+    outputs_t_bxn = np.squeeze(np.asarray(outputs_t_bxn))
+    r_sxt = np.dot(P_nxn, states_nxt)
+
+    for s in xrange(nspikifications):
+      data_e.append(r_sxt)
+      u_e.append(u_1xt)
+      outs_e.append(outputs_t_bxn)
+
+  truth_data_e = normalize_rates(data_e, E, N)
+
+spiking_data_e = spikify_data(truth_data_e, rng, dt=FLAGS.dt,
+                              max_firing_rate=FLAGS.max_firing_rate)
+train_inds, valid_inds = get_train_n_valid_inds(E, train_percentage,
+                                                nspikifications)
+
+data_train_truth, data_valid_truth = split_list_by_inds(truth_data_e,
+                                                        train_inds,
+                                                        valid_inds)
+data_train_spiking, data_valid_spiking = split_list_by_inds(spiking_data_e,
+                                                            train_inds,
+                                                            valid_inds)
+
+data_train_truth = nparray_and_transpose(data_train_truth)
+data_valid_truth = nparray_and_transpose(data_valid_truth)
+data_train_spiking = nparray_and_transpose(data_train_spiking)
+data_valid_spiking = nparray_and_transpose(data_valid_spiking)
+
+# save down the inputs used to generate this data
+train_inputs_u, valid_inputs_u = split_list_by_inds(u_e,
+                                                    train_inds,
+                                                    valid_inds)
+train_inputs_u = nparray_and_transpose(train_inputs_u)
+valid_inputs_u = nparray_and_transpose(valid_inputs_u)
+
+# save down the network outputs (may be useful later)
+train_outputs_u, valid_outputs_u = split_list_by_inds(outs_e,
+                                                      train_inds,
+                                                      valid_inds)
+train_outputs_u = np.array(train_outputs_u)
+valid_outputs_u = np.array(valid_outputs_u)
+
+
+data = { 'train_truth': data_train_truth,
+         'valid_truth': data_valid_truth,
+         'train_data' : data_train_spiking,
+         'valid_data' : data_valid_spiking,
+         'train_percentage' : train_percentage,
+         'nspikifications' : nspikifications,
+         'dt' : FLAGS.dt,
+         'u_std' : FLAGS.u_std,
+         'max_firing_rate': FLAGS.max_firing_rate,
+         'train_inputs_u': train_inputs_u,
+         'valid_inputs_u': valid_inputs_u,
+         'train_outputs_u': train_outputs_u,
+         'valid_outputs_u': valid_outputs_u,
+         'conversion_factor' : FLAGS.max_firing_rate/(1.0/FLAGS.dt) }
+
+# just one dataset here
+datasets = {}
+dataset_name = 'dataset_N' + str(N)
+datasets[dataset_name] = data
+
+# write out the dataset
+write_datasets(FLAGS.save_dir, FLAGS.datafile_name, datasets)
+print ('Saved to ', os.path.join(FLAGS.save_dir,
+                                 FLAGS.datafile_name + '_' + dataset_name))

lfads/synth_data/generate_labeled_rnn_data.py

+# Copyright 2017 Google Inc. 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.
+#
+# ==============================================================================
+from __future__ import print_function
+
+import os
+import h5py
+import numpy as np
+
+from synthetic_data_utils import generate_data, generate_rnn
+from synthetic_data_utils import get_train_n_valid_inds
+from synthetic_data_utils import nparray_and_transpose
+from synthetic_data_utils import spikify_data, split_list_by_inds
+import tensorflow as tf
+from utils import write_datasets
+
+DATA_DIR = "rnn_synth_data_v1.0"
+
+flags = tf.app.flags
+flags.DEFINE_string("save_dir", "/tmp/" + DATA_DIR + "/",
+                    "Directory for saving data.")
+flags.DEFINE_string("datafile_name", "conditioned_rnn_data",
+                    "Name of data file for input case.")
+flags.DEFINE_integer("synth_data_seed", 5, "Random seed for RNN generation.")
+flags.DEFINE_float("T", 1.0, "Time in seconds to generate.")
+flags.DEFINE_integer("C", 400, "Number of conditions")
+flags.DEFINE_integer("N", 50, "Number of units for the RNN")
+flags.DEFINE_float("train_percentage", 4.0/5.0,
+                   "Percentage of train vs validation trials")
+flags.DEFINE_integer("nspikifications", 10,
+                     "Number of spikifications of the same underlying rates.")
+flags.DEFINE_float("g", 1.5, "Complexity of dynamics")
+flags.DEFINE_float("x0_std", 1.0,
+                   "Volume from which to pull initial conditions (affects diversity of dynamics.")
+flags.DEFINE_float("tau", 0.025, "Time constant of RNN")
+flags.DEFINE_float("dt", 0.010, "Time bin")
+flags.DEFINE_float("max_firing_rate", 30.0, "Map 1.0 of RNN to a spikes per second")
+FLAGS = flags.FLAGS
+
+rng = np.random.RandomState(seed=FLAGS.synth_data_seed)
+rnn_rngs = [np.random.RandomState(seed=FLAGS.synth_data_seed+1),
+            np.random.RandomState(seed=FLAGS.synth_data_seed+2)]
+T = FLAGS.T
+C = FLAGS.C
+N = FLAGS.N
+nspikifications = FLAGS.nspikifications
+E = nspikifications * C
+train_percentage = FLAGS.train_percentage
+ntimesteps = int(T / FLAGS.dt)
+
+rnn_a = generate_rnn(rnn_rngs[0], N, FLAGS.g, FLAGS.tau, FLAGS.dt,
+                     FLAGS.max_firing_rate)
+rnn_b = generate_rnn(rnn_rngs[1], N, FLAGS.g, FLAGS.tau, FLAGS.dt,
+                     FLAGS.max_firing_rate)
+rnns = [rnn_a, rnn_b]
+
+# pick which RNN is used on each trial
+rnn_to_use = rng.randint(2, size=E)
+ext_input = np.repeat(np.expand_dims(rnn_to_use, axis=1), ntimesteps, axis=1)
+ext_input = np.expand_dims(ext_input, axis=2)  # these are "a's" in the paper
+
+x0s = []
+condition_labels = []
+condition_number = 0
+for c in range(C):
+  x0 = FLAGS.x0_std * rng.randn(N, 1)
+  x0s.append(np.tile(x0, nspikifications))
+  for ns in range(nspikifications):
+    condition_labels.append(condition_number)
+  condition_number += 1
+x0s = np.concatenate(x0s, axis=1)
+
+P_nxn = rng.randn(N, N) / np.sqrt(N)
+
+# generate trials for both RNNs
+rates_a, x0s_a, _ = generate_data(rnn_a, T=T, E=E, x0s=x0s, P_sxn=P_nxn,
+                                  input_magnitude=0.0, input_times=None)
+spikes_a = spikify_data(rates_a, rng, rnn_a['dt'], rnn_a['max_firing_rate'])
+
+rates_b, x0s_b, _ = generate_data(rnn_b, T=T, E=E, x0s=x0s, P_sxn=P_nxn,
+                                  input_magnitude=0.0, input_times=None)
+spikes_b = spikify_data(rates_b, rng, rnn_b['dt'], rnn_b['max_firing_rate'])
+
+# not the best way to do this but E is small enough
+rates = []
+spikes = []
+for trial in xrange(E):
+  if rnn_to_use[trial] == 0:
+    rates.append(rates_a[trial])
+    spikes.append(spikes_a[trial])
+  else:
+    rates.append(rates_b[trial])
+    spikes.append(spikes_b[trial])
+
+# split into train and validation sets
+train_inds, valid_inds = get_train_n_valid_inds(E, train_percentage,
+                                                nspikifications)
+
+rates_train, rates_valid = split_list_by_inds(rates, train_inds, valid_inds)
+spikes_train, spikes_valid = split_list_by_inds(spikes, train_inds, valid_inds)
+condition_labels_train, condition_labels_valid = split_list_by_inds(
+    condition_labels, train_inds, valid_inds)
+ext_input_train, ext_input_valid = split_list_by_inds(
+    ext_input, train_inds, valid_inds)
+
+rates_train = nparray_and_transpose(rates_train)
+rates_valid = nparray_and_transpose(rates_valid)
+spikes_train = nparray_and_transpose(spikes_train)
+spikes_valid = nparray_and_transpose(spikes_valid)
+
+# add train_ext_input and valid_ext input
+data = {'train_truth': rates_train,
+        'valid_truth': rates_valid,
+        'train_data' : spikes_train,
+        'valid_data' : spikes_valid,
+        'train_ext_input' : np.array(ext_input_train),
+        'valid_ext_input': np.array(ext_input_valid),
+        'train_percentage' : train_percentage,
+        'nspikifications' : nspikifications,
+        'dt' : FLAGS.dt,
+        'P_sxn' : P_nxn,
+        'condition_labels_train' : condition_labels_train,
+        'condition_labels_valid' : condition_labels_valid,
+        'conversion_factor': 1.0 / rnn_a['conversion_factor']}
+
+# just one dataset here
+datasets = {}
+dataset_name = 'dataset_N' + str(N)
+datasets[dataset_name] = data
+
+# write out the dataset
+write_datasets(FLAGS.save_dir, FLAGS.datafile_name, datasets)
+print ('Saved to ', os.path.join(FLAGS.save_dir,
+                                 FLAGS.datafile_name + '_' + dataset_name))

lfads/synth_data/run_generate_synth_data.sh

+#!/bin/bash
+
+# Copyright 2017 Google Inc. 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.
+#
+# ==============================================================================
+
+SYNTH_PATH=/tmp/rnn_synth_data_v1.0/
+
+echo "Generating chaotic rnn data with no input pulses (g=1.5)"
+python generate_chaotic_rnn_data.py --save_dir=$SYNTH_PATH --datafile_name=chaotic_rnn_no_inputs --synth_data_seed=5 --T=1.0 --C=400 --N=50 --S=50 --train_percentage=0.8 --nspikifications=10 --g=1.5 --x0_std=1.0 --tau=0.025 --dt=0.01 --input_magnitude=0.0 --max_firing_rate=30.0
+
+echo "Generating chaotic rnn data with input pulses (g=1.5)"
+python generate_chaotic_rnn_data.py --save_dir=$SYNTH_PATH --datafile_name=chaotic_rnn_inputs_g1p5 --synth_data_seed=5 --T=1.0 --C=400 --N=50 --S=50 --train_percentage=0.8 --nspikifications=10 --g=1.5 --x0_std=1.0 --tau=0.025 --dt=0.01 --input_magnitude=20.0 --max_firing_rate=30.0
+
+echo "Generating chaotic rnn data with input pulses (g=2.5)"
+python generate_chaotic_rnn_data.py --save_dir=$SYNTH_PATH --datafile_name=chaotic_rnn_inputs_g2p5 --synth_data_seed=5 --T=1.0 --C=400 --N=50 --S=50 --train_percentage=0.8 --nspikifications=10 --g=2.5 --x0_std=1.0 --tau=0.025 --dt=0.01 --input_magnitude=20.0 --max_firing_rate=30.0
+
+echo "Generate the multi-session RNN data (no multi-session synth example in paper)"
+python generate_chaotic_rnn_data.py --save_dir=$SYNTH_PATH --datafile_name=chaotic_rnn_multisession --synth_data_seed=5 --T=1.0 --C=150 --N=100 --S=20 --npcs=10 --train_percentage=0.8 --nspikifications=40 --g=1.5 --x0_std=1.0 --tau=0.025 --dt=0.01 --input_magnitude=0.0 --max_firing_rate=30.0
+
+echo "Generating Integration-to-bound RNN data"
+python generate_itb_data.py --save_dir=$SYNTH_PATH --datafile_name=itb_rnn --u_std=0.25 --checkpoint_path=SAMPLE_CHECKPOINT --synth_data_seed=5 --T=1.0 --C=800 --N=50 --train_percentage=0.8 --nspikifications=5 --tau=0.025 --dt=0.01 --max_firing_rate=30.0
+
+echo "Generating chaotic rnn data with external input labels (no external input labels example in paper)"
+python generate_labeled_rnn_data.py --save_dir=$SYNTH_PATH --datafile_name=chaotic_rnns_labeled --synth_data_seed=5 --T=1.0 --C=400 --N=50  --train_percentage=0.8 --nspikifications=10 --g=1.5 --x0_std=1.0 --tau=0.025 --dt=0.01 --max_firing_rate=30.0

lfads/synth_data/synthetic_data_utils.py

+# Copyright 2017 Google Inc. 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.
+#
+# ==============================================================================
+from __future__ import print_function
+
+import h5py
+import numpy as np
+import os
+
+from utils import write_datasets
+import matplotlib
+import matplotlib.pyplot as plt
+import scipy.signal
+
+
+def generate_rnn(rng, N, g, tau, dt, max_firing_rate):
+  """Create a (vanilla) RNN with a bunch of hyper parameters for generating
+chaotic data.
+  Args:
+    rng: numpy random number generator
+    N: number of hidden units
+    g: scaling of recurrent weight matrix in g W, with W ~ N(0,1/N)
+    tau: time scale of individual unit dynamics
+    dt: time step for equation updates
+    max_firing_rate: how to resecale the -1,1 firing rates
+  Returns:
+    the dictionary of these parameters, plus some others.
+"""
+  rnn = {}
+  rnn['N'] = N
+  rnn['W'] = rng.randn(N,N)/np.sqrt(N)
+  rnn['Bin'] = rng.randn(N)/np.sqrt(1.0)
+  rnn['Bin2'] = rng.randn(N)/np.sqrt(1.0)
+  rnn['b'] = np.zeros(N)
+  rnn['g'] = g
+  rnn['tau'] = tau
+  rnn['dt'] = dt
+  rnn['max_firing_rate'] = max_firing_rate
+  mfr = rnn['max_firing_rate']                   # spikes / sec
+  nbins_per_sec = 1.0/rnn['dt']                  # bins / sec
+  # Used for plotting in LFADS
+  rnn['conversion_factor'] = mfr / nbins_per_sec # spikes / bin
+  return rnn
+
+
+def generate_data(rnn, T, E, x0s=None, P_sxn=None, input_magnitude=0.0,
+                  input_times=None):
+  """ Generates data from an randomly initialized RNN.
+  Args:
+    rnn: the rnn
+    T: Time in seconds to run (divided by rnn['dt'] to get steps, rounded down.
+    E: total number of examples
+    S: number of samples (subsampling N)
+  Returns:
+    A list of length E of NxT tensors of the network being run.
+  """
+  N = rnn['N']
+  def run_rnn(rnn, x0, ntime_steps, input_time=None):
+    rs = np.zeros([N,ntime_steps])
+    x_tm1 = x0
+    r_tm1 = np.tanh(x0)
+    tau = rnn['tau']
+    dt = rnn['dt']
+    alpha = (1.0-dt/tau)
+    W = dt/tau*rnn['W']*rnn['g']
+    Bin = dt/tau*rnn['Bin']
+    Bin2 = dt/tau*rnn['Bin2']
+    b = dt/tau*rnn['b']
+
+    us = np.zeros([1, ntime_steps])
+    for t in range(ntime_steps):
+      x_t = alpha*x_tm1 + np.dot(W,r_tm1) + b
+      if input_time is not None and t == input_time:
+        us[0,t] = input_magnitude
+        x_t += Bin * us[0,t] # DCS is this what was used?
+      r_t = np.tanh(x_t)
+      x_tm1 = x_t
+      r_tm1 = r_t
+      rs[:,t] = r_t
+    return rs, us
+
+  if P_sxn is None:
+    P_sxn = np.eye(N)
+  ntime_steps = int(T / rnn['dt'])
+  data_e = []
+  inputs_e = []
+  for e in range(E):
+    input_time = input_times[e] if input_times is not None else None
+    r_nxt, u_uxt = run_rnn(rnn, x0s[:,e], ntime_steps, input_time)
+    r_sxt = np.dot(P_sxn, r_nxt)
+    inputs_e.append(u_uxt)
+    data_e.append(r_sxt)
+
+  S = P_sxn.shape[0]
+  data_e = normalize_rates(data_e, E, S)
+
+  return data_e, x0s, inputs_e
+
+
+def normalize_rates(data_e, E, S):
+  # Normalization, made more complex because of the P matrices.
+  # Normalize by min and max in each channel.  This normalization will
+  # cause offset differences between identical rnn runs, but different
+  # t hits.
+  for e in range(E):
+    r_sxt = data_e[e]
+    for i in range(S):
+      rmin = np.min(r_sxt[i,:])
+      rmax = np.max(r_sxt[i,:])
+      assert rmax - rmin != 0, 'Something wrong'
+      r_sxt[i,:] = (r_sxt[i,:] - rmin)/(rmax-rmin)
+    data_e[e] = r_sxt
+  return data_e
+
+
+def spikify_data(data_e, rng, dt=1.0, max_firing_rate=100):
+  """ Apply spikes to a continuous dataset whose values are between 0.0 and 1.0
+  Args:
+    data_e: nexamples length list of NxT trials
+    dt: how often the data are sampled
+    max_firing_rate: the firing rate that is associated with a value of 1.0
+  Returns:
+    spikified_data_e: a list of length b of the data represented as spikes,
+    sampled from the underlying poisson process.
+    """
+
+  spikifies_data_e = []
+  E = len(data_e)
+  spikes_e = []
+  for e in range(E):
+    data = data_e[e]
+    N,T = data.shape
+    data_s = np.zeros([N,T]).astype(np.int)
+    for n in range(N):
+      f = data[n,:]
+      s = rng.poisson(f*max_firing_rate*dt, size=T)
+      data_s[n,:] = s
+    spikes_e.append(data_s)
+
+  return spikes_e
+
+
+def get_train_n_valid_inds(num_trials, train_fraction, nspikifications):
+  """Split the numbers between 0 and num_trials-1 into two portions for
+  training and validation, based on the train fraction.
+  Args:
+    num_trials: the number of trials
+    train_fraction: (e.g. .80)
+    nspikifications: the number of spiking trials per initial condition
+  Returns:
+    a 2-tuple of two lists: the training indices and validation indices
+    """
+  train_inds = []
+  valid_inds = []
+  for i in range(num_trials):
+    # This line divides up the trials so that within one initial condition,
+    # the randomness of spikifying the condition is shared among both
+    # training and validation data splits.
+    if (i % nspikifications)+1 > train_fraction * nspikifications:
+      valid_inds.append(i)
+    else:
+      train_inds.append(i)
+
+  return train_inds, valid_inds
+
+
+def split_list_by_inds(data, inds1, inds2):
+  """Take the data, a list, and split it up based on the indices in inds1 and
+  inds2.
+  Args:
+    data: the list of data to split
+    inds1, the first list of indices
+    inds2, the second list of indices
+  Returns: a 2-tuple of two lists.
+  """
+  if data is None or len(data) == 0:
+    return [], []
+  else:
+    dout1 = [data[i] for i in inds1]
+    dout2 = [data[i] for i in inds2]
+    return dout1, dout2
+
+
+def nparray_and_transpose(data_a_b_c):
+  """Convert the list of items in data to a numpy array, and transpose it
+  Args:
+    data: data_asbsc: a nested, nested list of length a, with sublist length
+      b, with sublist length c.
+  Returns:
+    a numpy 3-tensor with dimensions a x c x b
+"""
+  data_axbxc = np.array([datum_b_c for datum_b_c in data_a_b_c])
+  data_axcxb = np.transpose(data_axbxc, axes=[0,2,1])
+  return data_axcxb
+
+
+def add_alignment_projections(datasets, npcs, ntime=None, nsamples=None):
+  """Create a matrix that aligns the datasets a bit, under
+  the assumption that each dataset is observing the same underlying dynamical
+  system.
+
+  Args:
+    datasets: The dictionary of dataset structures.
+    npcs:  The number of pcs for each, basically like lfads factors.
+    nsamples (optional): Number of samples to take for each dataset.
+    ntime (optional): Number of time steps to take in each sample.
+
+  Returns:
+    The dataset structures, with the field alignment_matrix_cxf added.
+    This is # channels x npcs dimension
+"""
+  nchannels_all = 0
+  channel_idxs = {}
+  conditions_all = {}
+  nconditions_all = 0
+  for name, dataset in datasets.items():
+    cidxs = np.where(dataset['P_sxn'])[1] # non-zero entries in columns
+    channel_idxs[name] = [cidxs[0], cidxs[-1]+1]
+    nchannels_all += cidxs[-1]+1 - cidxs[0]
+    conditions_all[name] = np.unique(dataset['condition_labels_train'])
+
+  all_conditions_list = \
+      np.unique(np.ndarray.flatten(np.array(conditions_all.values())))
+  nconditions_all = all_conditions_list.shape[0]
+
+  if ntime is None:
+    ntime = dataset['train_data'].shape[1]
+  if nsamples is None:
+    nsamples = dataset['train_data'].shape[0]
+
+  # In the data workup in the paper, Chethan did intra condition
+  # averaging, so let's do that here.
+  avg_data_all = {}
+  for name, conditions in conditions_all.items():
+    dataset = datasets[name]
+    avg_data_all[name] = {}
+    for cname in conditions:
+      td_idxs = np.argwhere(np.array(dataset['condition_labels_train'])==cname)
+      data = np.squeeze(dataset['train_data'][td_idxs,:,:], axis=1)
+      avg_data = np.mean(data, axis=0)
+      avg_data_all[name][cname] = avg_data
+
+  # Visualize this in the morning.
+  all_data_nxtc = np.zeros([nchannels_all, ntime * nconditions_all])
+  for name, dataset in datasets.items():
+    cidx_s = channel_idxs[name][0]
+    cidx_f = channel_idxs[name][1]
+    for cname in conditions_all[name]:
+      cidxs = np.argwhere(all_conditions_list == cname)
+      if cidxs.shape[0] > 0:
+        cidx = cidxs[0][0]
+        all_tidxs = np.arange(0, ntime+1) + cidx*ntime
+        all_data_nxtc[cidx_s:cidx_f, all_tidxs[0]:all_tidxs[-1]] = \
+            avg_data_all[name][cname].T
+
+  # A bit of filtering. We don't care about spectral properties, or
+  # filtering artifacts, simply correlate time steps a bit.
+  filt_len = 6
+  bc_filt = np.ones([filt_len])/float(filt_len)
+  for c in range(nchannels_all):
+    all_data_nxtc[c,:] = scipy.signal.filtfilt(bc_filt, [1.0], all_data_nxtc[c,:])
+
+  # Compute the PCs.
+  all_data_mean_nx1 = np.mean(all_data_nxtc, axis=1, keepdims=True)
+  all_data_zm_nxtc = all_data_nxtc - all_data_mean_nx1
+  corr_mat_nxn = np.dot(all_data_zm_nxtc, all_data_zm_nxtc.T)
+  evals_n, evecs_nxn = np.linalg.eigh(corr_mat_nxn)
+  sidxs = np.flipud(np.argsort(evals_n)) # sort such that 0th is highest
+  evals_n = evals_n[sidxs]
+  evecs_nxn = evecs_nxn[:,sidxs]
+
+  # Project all the channels data onto the low-D PCA basis, where
+  # low-d is the npcs parameter.
+  all_data_pca_pxtc = np.dot(evecs_nxn[:, 0:npcs].T, all_data_zm_nxtc)
+
+  # Now for each dataset, we regress the channel data onto the top
+  # pcs, and this will be our alignment matrix for that dataset.
+  # |B - A*W|^2
+  for name, dataset in datasets.items():
+    cidx_s = channel_idxs[name][0]
+    cidx_f = channel_idxs[name][1]
+    all_data_zm_chxtc = all_data_zm_nxtc[cidx_s:cidx_f,:] # ch for channel
+    W_chxp, _, _, _ = \
+        np.linalg.lstsq(all_data_zm_chxtc.T, all_data_pca_pxtc.T)
+    dataset['alignment_matrix_cxf'] = W_chxp
+
+  do_debug_plot = False
+  if do_debug_plot:
+    pc_vecs = evecs_nxn[:,0:npcs]
+    ntoplot = 400
+
+    plt.figure()
+    plt.plot(np.log10(evals_n), '-x')
+    plt.figure()
+    plt.subplot(311)
+    plt.imshow(all_data_pca_pxtc)
+    plt.colorbar()
+
+    plt.subplot(312)
+    plt.imshow(np.dot(W_chxp.T, all_data_zm_chxtc))
+    plt.colorbar()
+
+    plt.subplot(313)
+    plt.imshow(np.dot(all_data_zm_chxtc.T, W_chxp).T - all_data_pca_pxtc)
+    plt.colorbar()
+
+    import pdb
+    pdb.set_trace()
+
+  return datasets