在 tensorflow 2.0 中构建 RealNVP 层的更好方法？

Question

是否有更好的方法来构建 RealNVP 层以用作 tensorflow 2.0 中的标准可训练层？我最终将它包装在一个模型中。对于图层，变量没有出现在 trainable_variables 中。

像这样运行，但我怀疑有更好的方法：

from pylab import *
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_probability as tfp
tfb = tfp.bijectors
tfd = tfp.distributions

# class NVPLayer(tf.keras.layers.Layer):
class NVPLayer(tf.keras.models.Model):

    def __init__(self, *, output_dim, num_masked, **kwargs):
        super().__init__(**kwargs)
        self.output_dim = output_dim
        self.num_masked = num_masked
        self.shift_and_log_scale_fn = tfb.real_nvp_default_template(
            hidden_layers=[2],
            activation=None, # linear
            )
        self.loss = None

    def call(self, *inputs):
        nvp = tfd.TransformedDistribution(
            distribution=tfd.MultivariateNormalDiag(loc=[0., 0., 0.]),
            bijector=tfb.RealNVP(
                num_masked=self.num_masked,
                shift_and_log_scale_fn=self.shift_and_log_scale_fn)
            )
        self.loss = tf.reduce_mean(nvp.log_prob(*inputs)) # how else to do this?
        return nvp.bijector.forward(*inputs)

layer = NVPLayer(output_dim=3, num_masked=1)
x = (np.random.randn(100, 3) * np.array([1, 3, 5]) + np.array([-3, -10, 4])).astype(np.float32)
z0 = layer(x).numpy()

optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)
for i in range(1000):
    with tf.GradientTape() as tape:
        y = layer(x)
        loss = - layer.loss
        print(loss)
    g = tape.gradient(loss, layer.trainable_variables)
    l = optimizer.apply_gradients(zip(g, layer.trainable_variables))

z1 = layer(x).numpy()

print(pd.DataFrame(z0).describe())
print(pd.DataFrame(z1).describe())

Answer 1

感谢您提供代码，我用它作为起点（tf 2.0rc0 和 tfp 0.8rc0）。我玩了一会儿，发现你可以让训练更快，把它包装成@tf.function。

但是，当您这样做时，您必须将双射器和 TransformedDistribution 内容拉入 init 部分。否则，您以后无法访问它们。我使用了与您不同的双射器，但原理保持不变。

但我不知道这是否是最好的方法。

from pylab import *
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_probability as tfp
tfb = tfp.bijectors
tfd = tfp.distributions

class MAF(tf.keras.models.Model):

    def __init__(self, *, output_dim, num_masked, **kwargs): #** additional arguments for the super class
        super().__init__(**kwargs)
        self.output_dim = output_dim
        self.num_masked = num_masked
        self.shift_and_log_scale_fn = tfb.masked_autoregressive_default_template(hidden_layers=[128, 128])
        # Defining the bijector
        num_bijectors = 5
        bijectors=[]
        for i in range(num_bijectors):
            bijectors.append(tfb.MaskedAutoregressiveFlow(shift_and_log_scale_fn=self.shift_and_log_scale_fn))
            bijectors.append(tfb.Permute(permutation=[1, 0]))
        # Discard the last Permute layer.
        bijector = tfb.Chain(list(reversed(bijectors[:-1])))

        # Defining the flow
        self.flow = tfd.TransformedDistribution(
            distribution=tfd.MultivariateNormalDiag(loc=[0., 0.]),
            bijector=bijector)

    def call(self, *inputs): 
        return self.flow.bijector.forward(*inputs)

    def getFlow(self, num):
        return self.flow.sample(num)

print(X.shape)
model = MAF(output_dim=2, num_masked=1)
# model.summary() #Yields an error. The model needs called before it is build.
_ = model(X) 
model.summary()

optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)

@tf.function #Adding the tf.function makes it about 10 times faster!!!
def train_step(X): 
    with tf.GradientTape() as tape:
        predictions = model(X)
        loss = -tf.reduce_mean(model.flow.log_prob(X)) 
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    return loss

# Training
from time import time
    start = time()
    for i in range(1001):
        loss = train_step(X)
        if (i % 50 == 0):
            print(i, " ",loss.numpy(), (time()-start))
            start = time()

# Sampling from the trained model
XF = model.flow.sample(10000) 
plt.scatter(XF[:, 0], XF[:, 1], s=5, color='blue')

该笔记本可以在这里找到https://github.com/tensorchiefs/dl_book_playground/blob/master/flow/Flow_101_learning_parameters.ipynb或使用colab在https://colab.research.google.com/github/tensorchiefs/dl_book_playground/blob /master/flow/Flow_101_learning_parameters.ipynb