Spaces:
Running
Running
File size: 8,337 Bytes
0b8359d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 |
# 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 six.moves import xrange
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("nreplications", 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)
nreplications = FLAGS.nreplications
E = nreplications * 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(nreplications):
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,
nreplications)
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,
'nreplications' : nreplications,
'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))
|