Feature/ali

edward/__init__.py

@@ -16,7 +16,7 @@
     HMC, MetropolisHastings, SGLD, SGHMC, \
     KLpq, KLqp, MFVI, ReparameterizationKLqp, ReparameterizationKLKLqp, \
     ReparameterizationEntropyKLqp, ScoreKLqp, ScoreKLKLqp, ScoreEntropyKLqp, \
-    GANInference, ALI, WGANInference, MAP, Laplace
+    GANInference, BiGANInference, WGANInference, MAP, Laplace
 from edward.models import PyMC3Model, PythonModel, StanModel, \
     RandomVariable
 from edward.util import copy, dot, get_ancestors, get_children, \

edward/inferences/__init__.py

@@ -2,7 +2,7 @@
 from __future__ import division
 from __future__ import print_function
 
-from edward.inferences.ali import *
+from edward.inferences.bigan_inference import *
 from edward.inferences.gan_inference import *
 from edward.inferences.hmc import *
 from edward.inferences.inference import *

edward/inferences/bigan_inference.py

@@ -0,0 +1,84 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import six
+import tensorflow as tf
+
+from edward.inferences.gan_inference import GANInference
+from edward.util import get_session
+
+
+class BiGANInference(GANInference):
+  """Adversarially Learned Inference (Dumoulin et al., 2017) or
+  Bidirectional Generative Adversarial Networks (Donahue et al., 2017)
+  for joint learning of generator and inference networks.
+
+  Works for the class of implicit (and differentiable) probabilistic
+  models. These models do not require a tractable density and assume
+  only a program that generates samples.
+  """
+  def __init__(self, latent_vars, data, discriminator):
+    """
+    Notes
+    -----
+    ``BiGANInference`` matches a mapping from data to latent variables and a
+    mapping from latent variables to data through a joint
+    discriminator.
+
+    In building the computation graph for inference, the
+    discriminator's parameters can be accessed with the variable scope
+    "Disc".
+    In building the computation graph for inference, the
+    encoder and decoder parameters can be accessed with the variable scope
+    "Gen".
+
+    Examples
+    --------
+    >>> with tf.variable_scope("Gen"):
+    >>>   xf = gen_data(z_ph)
+    >>>   zf = gen_latent(x_ph)
+    >>> inference = ed.BiGANInference({latent_vars: qz}, {xf: x_data}, discriminator)
+    """
+    joint = latent_vars.copy()
+    joint.update(data)
+
+    super(BiGANInference, self).__init__(joint, discriminator)
+
+  def build_loss_and_gradients(self, var_list):
+
+    x_true = list(six.itervalues(self.data))[0]
+    x_fake = list(six.iterkeys(self.data))[0]
+
+    z_true = list(six.itervalues(self.data))[1]
+    z_fake = list(six.iterkeys(self.data))[1]
+
+    with tf.variable_scope("Disc"):
+        # xtzf := x_true, z_fake
+        d_xtzf = self.discriminator(x_true, z_fake)
+    with tf.variable_scope("Disc", reuse=True):
+        # xfzt := x_fake, z_true
+        d_xfzt = self.discriminator(x_fake, z_true)
+
+    loss_d = tf.nn.sigmoid_cross_entropy_with_logits(
+        labels=tf.ones_like(d_xfzt), logits=d_xfzt) + \
+        tf.nn.sigmoid_cross_entropy_with_logits(
+            labels=tf.zeros_like(d_xtzf), logits=d_xtzf)
+    loss = tf.nn.sigmoid_cross_entropy_with_logits(
+        labels=tf.zeros_like(d_xfzt), logits=d_xfzt) + \
+        tf.nn.sigmoid_cross_entropy_with_logits(
+            labels=tf.ones_like(d_xtzf), logits=d_xtzf)
+
+    loss_d = tf.reduce_mean(loss_d)
+    loss = tf.reduce_mean(loss)
+
+    var_list_d = tf.get_collection(
+        tf.GraphKeys.TRAINABLE_VARIABLES, scope="Disc")
+    var_list = tf.get_collection(
+        tf.GraphKeys.TRAINABLE_VARIABLES, scope="Gen")
+
+    grads_d = tf.gradients(loss_d, var_list_d)
+    grads = tf.gradients(loss, var_list)
+    grads_and_vars_d = list(zip(grads_d, var_list_d))
+    grads_and_vars = list(zip(grads, var_list))
+    return loss, grads_and_vars, loss_d, grads_and_vars_d

examples/bigan_example.py

@@ -0,0 +1,134 @@
+#!/usr/bin/env python
+"""Adversarially Learned Inference (Dumoulin et al., 2017) or
+  Bidirectional Generative Adversarial Networks (Donahue et al., 2017)
+  for joint learning of generator and inference networks for MNIST
+"""
+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import division
+
+import edward as ed
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import numpy as np
+import os
+import tensorflow as tf
+
+from tensorflow.contrib import slim
+from tensorflow.examples.tutorials.mnist import input_data
+from edward.models import Uniform
+
+
+M = 100  # batch size during training
+d = 50  # latent dimension
+leak = 0.2  # leak parameter for leakyReLU
+hidden_units = 300
+encoder_variance = 0.01  # Set to 0 for deterministic encoder
+
+
+def leakyrelu(x, alpha=leak):
+    return tf.maximum(x, alpha * x)
+
+
+def gen_latent(x, hidden_units):
+  h = slim.fully_connected(x, hidden_units, activation_fn=leakyrelu)
+  z = slim.fully_connected(h, d, activation_fn=None)
+  return z + np.random.normal(0, encoder_variance, np.shape(z))
+
+
+def gen_data(z, hidden_units):
+  h = slim.fully_connected(z, hidden_units, activation_fn=leakyrelu)
+  x = slim.fully_connected(h, 784, activation_fn=tf.sigmoid)
+  return x
+
+
+def discriminative_network(x, y):
+  #  Discriminator must output probability in logits
+  inputs = tf.concat([x, y], 1)
+  h1 = slim.fully_connected(inputs, hidden_units, activation_fn=leakyrelu)
+  logit = slim.fully_connected(h1, 1, activation_fn=None)
+  return logit
+
+
+def plot(samples):
+  fig = plt.figure(figsize=(4, 4))
+  plt.title(str(samples))
+  gs = gridspec.GridSpec(4, 4)
+  gs.update(wspace=0.05, hspace=0.05)
+
+  for i, sample in enumerate(samples):
+    ax = plt.subplot(gs[i])
+    plt.axis('off')
+    ax.set_xticklabels([])
+    ax.set_yticklabels([])
+    ax.set_aspect('equal')
+    plt.imshow(sample.reshape(28, 28), cmap='Greys_r')
+
+  return fig
+
+
+ed.set_seed(42)
+
+DATA_DIR = "data/mnist"
+IMG_DIR = "img"
+
+if not os.path.exists(DATA_DIR):
+  os.makedirs(DATA_DIR)
+if not os.path.exists(IMG_DIR):
+  os.makedirs(IMG_DIR)
+
+# DATA. MNIST batches are fed at training time.
+mnist = input_data.read_data_sets(DATA_DIR, one_hot=True)
+x_ph = tf.placeholder(tf.float32, [M, 784])
+z_ph = tf.placeholder(tf.float32, [M, d])
+
+# MODEL
+with tf.variable_scope("Gen"):
+  xf = gen_data(z_ph, hidden_units)
+  zf = gen_latent(x_ph, hidden_units)
+
+# INFERENCE:
+optimizer = tf.train.AdamOptimizer()
+optimizer_d = tf.train.AdamOptimizer()
+inference = ed.BiGANInference(
+    latent_vars={zf: z_ph}, data = {xf: x_ph}, discriminator=discriminative_network)
+
+inference.initialize(
+    optimizer=optimizer, optimizer_d=optimizer_d, n_iter=100000, n_print=3000)
+
+sess = ed.get_session()
+init_op = tf.global_variables_initializer()
+sess.run(init_op)
+
+idx = np.random.randint(M, size=16)
+i = 0
+for t in range(inference.n_iter):
+  if t % inference.n_print == 1:
+
+    samples = sess.run(xf, feed_dict={z_ph: z_batch})
+    samples = samples[idx, ]
+    fig = plot(samples)
+    plt.savefig(os.path.join(IMG_DIR, '{}{}.png').format(
+        'Generated', str(i).zfill(3)), bbox_inches='tight')
+    plt.close(fig)
+
+    fig = plot(x_batch[idx, ])
+    plt.savefig(os.path.join(IMG_DIR, '{}{}.png').format(
+        'Base', str(i).zfill(3)), bbox_inches='tight')
+    plt.close(fig)
+
+    zsam = sess.run(zf, feed_dict={x_ph: x_batch})
+    reconstructions = sess.run(xf, feed_dict={z_ph: zsam})
+    reconstructions = reconstructions[idx, ]
+    fig = plot(reconstructions)
+    plt.savefig(os.path.join(IMG_DIR, '{}{}.png').format(
+        'Reconstruct', str(i).zfill(3)), bbox_inches='tight')
+    plt.close(fig)
+
+    i += 1
+
+  x_batch, _ = mnist.train.next_batch(M)
+  z_batch = np.random.normal(0, 1, [M, d])
+
+  info_dict = inference.update(feed_dict={x_ph: x_batch, z_ph: z_batch})
+  inference.print_progress(info_dict)