python - 将展开的 GAN 更新到 TF2

Question

我正在尝试使用示例代码实现此处描述的展开 GAN 模型。但是，它是使用 TF1 实现的，我一直在尽力更新它，但我对 python 和 TF 相对较新（过去大约 6 个月才使用它）。

我似乎无法完成的行（目前，可能还有更多）是这一行：

gen_vars = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES, "generator")
disc_vars = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES, "discriminator")

这些都返回空列表，我看不到我缺少什么。即使没有指定范围，get_collection()返回[]. 早些时候，我们将生成器和鉴别器都定义为范围，如下所示：

def generator(z, output_dim=2, n_hidden=128, n_layer=2):
    with tf.compat.v1.variable_scope("generator"):
        h = slim.stack(z, slim.fully_connected, [n_hidden] * n_layer, activation_fn=tf.nn.tanh)
        x = slim.fully_connected(h, output_dim, activation_fn=None)
    return x

def discriminator(x, n_hidden=128, n_layer=2, reuse=False):
    with tf.compat.v1.variable_scope("discriminator", reuse=reuse):
        h = slim.stack(x, slim.fully_connected, [n_hidden] * n_layer, activation_fn=tf.nn.tanh)
        log_d = slim.fully_connected(h, 1, activation_fn=None)
    return log_d

范围的定义有问题吗？

这是我更新的完整代码，以防我在其他地方遗漏了什么：

%pylab inline
from collections import OrderedDict
import tensorflow as tf
import tensorflow_probability as tfp
ds = tfp.distributions
# slim = tf.contrib.slim
import tf_slim as slim

from keras.optimizers import Adam

try:
    from moviepy.video.io.bindings import mplfig_to_npimage
    import moviepy.editor as mpy
    generate_movie = True
except:
    print("Warning: moviepy not found.")
    generate_movie = False


def remove_original_op_attributes(graph):
    """Remove _original_op attribute from all operations in a graph."""
    for op in graph.get_operations():
        op._original_op = None
        
def graph_replace(*args, **kwargs):
    """Monkey patch graph_replace so that it works with TF 1.0"""
    remove_original_op_attributes(tf.get_default_graph())
    return _graph_replace(*args, **kwargs)




def extract_update_dict(update_ops):
    """Extract variables and their new values from Assign and AssignAdd ops.
    
    Args:
        update_ops: list of Assign and AssignAdd ops, typically computed using Keras' opt.get_updates()

    Returns:
        dict mapping from variable values to their updated value
    """
    name_to_var = {v.name: v for v in tf.compat.v1.global_variables()}
    updates = OrderedDict()
    for update in update_ops:
        var_name = update.op.inputs[0].name
        var = name_to_var[var_name]
        value = update.op.inputs[1]
        if update.op.type == 'Assign':
            updates[var.value()] = value
        elif update.op.type == 'AssignAdd':
            updates[var.value()] = var + value
        else:
            raise ValueError("Update op type (%s) must be of type Assign or AssignAdd"%update_op.op.type)
    return updates



def sample_mog(batch_size, n_mixture=8, std=0.01, radius=1.0):
    thetas = np.linspace(0, 2 * np.pi, n_mixture)
    xs, ys = radius * np.sin(thetas), radius * np.cos(thetas)
    cat = ds.Categorical(tf.zeros(n_mixture))
    comps = [ds.MultivariateNormalDiag([xi, yi], [std, std]) for xi, yi in zip(xs.ravel(), ys.ravel())]
    data = ds.Mixture(cat, comps)
    return data.sample(batch_size)



def generator(z, output_dim=2, n_hidden=128, n_layer=2):
    with tf.compat.v1.variable_scope("generator"):
        h = slim.stack(z, slim.fully_connected, [n_hidden] * n_layer, activation_fn=tf.nn.tanh)
        x = slim.fully_connected(h, output_dim, activation_fn=None)
    return x

def discriminator(x, n_hidden=128, n_layer=2, reuse=False):
    with tf.compat.v1.variable_scope("discriminator", reuse=reuse):
        h = slim.stack(x, slim.fully_connected, [n_hidden] * n_layer, activation_fn=tf.nn.tanh)
        log_d = slim.fully_connected(h, 1, activation_fn=None)
    return log_d



params = dict(
    batch_size=512,
    disc_learning_rate=1e-4,
    gen_learning_rate=1e-3,
    beta1=0.5,
    epsilon=1e-8,
    max_iter=25000,
    viz_every=5000,
    z_dim=256,
    x_dim=2,
    unrolling_steps=5,
)


tf.compat.v1.reset_default_graph()

data = sample_mog(params['batch_size'])

noise = ds.Normal(tf.zeros(params['z_dim']), 
                  tf.ones(params['z_dim'])).sample(params['batch_size'])
# Construct generator and discriminator nets
# with slim.arg_scope([slim.fully_connected], weights_initializer=tf.orthogonal_initializer(gain=1.4)): ## old
with slim.arg_scope([slim.fully_connected], weights_initializer=tf.keras.initializers.Orthogonal(gain=1.4)):
    samples = generator(noise, output_dim=params['x_dim'])
    real_score = discriminator(data)
    fake_score = discriminator(samples, reuse=True)
    
# Saddle objective    
loss = tf.reduce_mean(
    tf.nn.sigmoid_cross_entropy_with_logits(logits=tf.cast(real_score, dtype=tf.float32), labels=tf.cast(tf.ones_like(real_score), dtype=tf.float32)) +
    tf.nn.sigmoid_cross_entropy_with_logits(logits=tf.cast(fake_score, dtype=tf.float32), labels=tf.cast(tf.zeros_like(fake_score), dtype=tf.float32)))

gen_vars = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES, "generator")
disc_vars = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES, "discriminator")

# Vanilla discriminator update
d_opt = Adam(lr=params['disc_learning_rate'], beta_1=params['beta1'], epsilon=params['epsilon'])
# updates = d_opt.get_updates(disc_vars, [], loss) ## old
updates = d_opt.get_updates(loss, [])
d_train_op = tf.group(*updates, name="d_train_op")

### I HAVE NOT UPDATED BEYOND THIS POINT ###

# Unroll optimization of the discrimiantor
if params['unrolling_steps'] > 0:
    # Get dictionary mapping from variables to their update value after one optimization step
    update_dict = extract_update_dict(updates)
    cur_update_dict = update_dict
    for i in xrange(params['unrolling_steps'] - 1):
        # Compute variable updates given the previous iteration's updated variable
        cur_update_dict = graph_replace(update_dict, cur_update_dict)
    # Final unrolled loss uses the parameters at the last time step
    unrolled_loss = graph_replace(loss, cur_update_dict)
else:
    unrolled_loss = loss

# Optimize the generator on the unrolled loss
g_train_opt = tf.train.AdamOptimizer(params['gen_learning_rate'], beta1=params['beta1'], epsilon=params['epsilon'])
g_train_op = g_train_opt.minimize(-unrolled_loss, var_list=gen_vars)


sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())

标签： pythontensorflowkerasgenerative-adversarial-network

Answer 1

的实施get_collection：

def get_collection(key, scope=None):
  """Wrapper for `Graph.get_collection()` using the default graph.

  See `tf.Graph.get_collection`
  for more details.

  Args:
    key: The key for the collection. For example, the `GraphKeys` class contains
      many standard names for collections.
    scope: (Optional.) If supplied, the resulting list is filtered to include
      only items whose `name` attribute matches using `re.match`. Items without
      a `name` attribute are never returned if a scope is supplied and the
      choice or `re.match` means that a `scope` without special tokens filters
      by prefix.

  Returns:
    The list of values in the collection with the given `name`, or
    an empty list if no value has been added to that collection. The
    list contains the values in the order under which they were
    collected.

  @compatibility(eager)
  Collections are not supported when eager execution is enabled.
  @end_compatibility
  """
  return get_default_graph().get_collection(key, scope)

它看起来像在这段代码中，key并且scope参数被交换了。如果您提供"generator"或"discriminator"作为key没有范围即；

gen_vars = tf.compat.v1.get_collection("generator")
disc_vars = tf.compat.v1.get_collection("discriminator")

你应该得到结果（我能够使用 Tensorflow 2.2.0 在本地重现）。我无法完全确定的唯一问题是，在提供时scope，该函数再次返回一个空列表，而不管您提供的范围值如何。例如，tf.compat.v1.GLOBAL_VARIABLES应该返回所有内容，但事实并非如此：

gen_vars = tf.compat.v1.get_default_graph().get_collection('generator', tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES) # returns []
gen_vars = tf.compat.v1.get_default_graph().get_collection('generator', tf.compat.v1.GraphKeys.GLOBAL_VARIABLES) # returns []
disc_vars = tf.compat.v1.get_collection('generator') # returns a list of tensors

更新

看起来即使在上下文管理器中创建变量也不会将它们添加到图形集合中。我必须在各自的函数中调用tf.compat.v1.add_to_collection('generator', x)和tf.compat.v1.add_to_collection('discriminator', log_d)来获得这些结果。

更新#2

我四处搜索，似乎没有上下文管理器可以让您将其中声明的变量添加到 Tensorflow 集合中。不过，为了这个答案的完整性，我已经实现了一个：

from contextlib import contextmanager

@contextmanager
def collection_scope(collection_name):
    import inspect
    from tensorflow.python.framework.ops import EagerTensor
    collection = tf.compat.v1.get_collection_ref(collection_name)
    yield
    # this is a bit of a hack, but it works...
    f = inspect.currentframe().f_back.f_back
    # only take variables which were declared within the context manager
    tf_variables = set([val.ref() for val in f.f_locals.values() if isinstance(val, EagerTensor)]) - \
                   set([val.ref() for val in f.f_back.f_locals.values() if isinstance(val, EagerTensor)])
    collection.extend(tf_variables)

然后，您可以将它放在您的函数中，以代替变量范围 ( tf.compat.v1.variable_scope) 上下文管理器。例如，而不是：

def generator(z, output_dim=2, n_hidden=128, n_layer=2):
    with tf.compat.v1.variable_scope('generator'):
        h = slim.stack(z, slim.fully_connected, [n_hidden] * n_layer, activation_fn=tf.nn.tanh)
        x = slim.fully_connected(h, output_dim, activation_fn=None)
    return x

请执行下列操作：

def generator(z, output_dim=2, n_hidden=128, n_layer=2):
    with collection_scope('generator'):
        h = slim.stack(z, slim.fully_connected, [n_hidden] * n_layer, activation_fn=tf.nn.tanh)
        x = slim.fully_connected(h, output_dim, activation_fn=None)
    return x

通过此更改，在上下文管理器范围内声明的所有张量都将添加到集合“生成器”中 -tf.compat.v1.get_collection('generator')将返回正确的张量列表。