我正在尝试为由两个独立tf.keras.Model对象组成的变分自动编码器 (VAE) 编写自定义训练循环。这个 VAE 的目标是多类分类。像往常一样，编码器模型的输出作为解码器模型的输入。解码器是循环解码器。同样像往常一样，VAE 中涉及两个损失函数：重建损失（分类交叉熵）和潜在损失。我当前架构的灵感来自这个github上的 pytorch 实现。
问题：每当我使用tape.gradient(loss, decoder.trainable_weights)解码器模型计算梯度时，返回的列表中每个元素只有 NoneType 对象。我假设我在使用 时犯了一些错误reconstruction_tensor，它位于我在下面编写的代码的底部附近。由于我需要进行迭代解码过程，如何在reconstruction_tensor不返回 NoneType 渐变元素列表的情况下使用类似的东西？如果您愿意，可以使用此colab 笔记本运行代码。
为了进一步阐明这个问题中的张量是什么样的，我将说明原始输入、将分配预测“令牌”的零张量，以及基于来自解码器的预测“令牌”对零张量的单次更新：
Example original input tensor of shape (batch_size, max_seq_length, num_classes):
_ _ _ _ _ _ _ _
| | 1 0 0 0 | | 0 1 0 0 | | 0 0 0 1 | |
| | 0 1 0 0 | | 1 0 0 0 | | 1 0 0 0 | |
|_ |_ 0 0 1 0 _| , |_ 0 0 0 1 _|, |_ 0 1 0 0 _| _|
Initial zeros tensor:
_ _ _ _ _ _ _ _
| | 0 0 0 0 | | 0 0 0 0 | | 0 0 0 0 | |
| | 0 0 0 0 | | 0 0 0 0 | | 0 0 0 0 | |
|_ |_ 0 0 0 0 _| , |_ 0 0 0 0 _|, |_ 0 0 0 0 _| _|
Example zeros tensor after a single iteration of the decoding loop:
_ _ _ _ _ _ _ _
| | 0.2 0.4 0.1 0.3 | | 0.1 0.2 0.6 0.1 | | 0.7 0.05 0.05 0.2 | |
| | 0 0 0 0 | | 0 0 0 0 | | 0 0 0 0 | |
|_ |_ 0 0 0 0 _| , |_ 0 0 0 0 _|, |_ 0 0 0 0 _| _|
这是重现问题的代码：
# Arbitrary data
batch_size = 3
max_seq_length = 3
num_classes = 4
original_inputs = tf.one_hot(tf.argmax((np.random.randn(batch_size, max_seq_length, num_classes)), axis=2), depth=num_classes)
latent_dims = 5 # Must be less than (max_seq_length * num_classes)
def sampling(inputs):
"""Reparametrization function. Used for Lambda layer"""
mus, log_vars = inputs
epsilon = tf.keras.backend.random_normal(shape=tf.keras.backend.shape(mus))
z = mus + tf.keras.backend.exp(log_vars/2) * epsilon
return z
def latent_loss_fxn(mus, log_vars):
"""Return latent loss for means and log variance."""
return -0.5 * tf.keras.backend.mean(1. + log_vars - tf.keras.backend.exp(log_vars) - tf.keras.backend.pow(mus, 2))
class DummyEncoder(tf.keras.Model):
def __init__(self, latent_dimension):
"""Define the hidden layer (bottleneck) and sampling layers"""
super().__init__()
self.hidden = tf.keras.layers.Dense(units=32)
self.dense_mus = tf.keras.layers.Dense(units=latent_dimension)
self.dense_log_vars = tf.keras.layers.Dense(units=latent_dimension)
self.sampling = tf.keras.layers.Lambda(function=sampling)
def call(self, inputs):
"""Define forward computation that outputs z, mu, log_var of input."""
dense_projection = self.hidden(inputs)
mus = self.dense_mus(dense_projection)
log_vars = self.dense_log_vars(dense_projection)
z = self.sampling([mus, log_vars])
return z, mus, log_vars
class DummyDecoder(tf.keras.Model):
def __init__(self, num_classes):
"""Define GRU layer and the Dense output layer"""
super().__init__()
self.gru = tf.keras.layers.GRU(units=1, return_sequences=True, return_state=True)
self.dense = tf.keras.layers.Dense(units=num_classes, activation='softmax')
def call(self, x, hidden_states=None):
"""Define forward computation"""
outputs, h_t = self.gru(x, hidden_states)
# The purpose of this computation is to use the unnormalized log
# probabilities from the GRU to produce normalized probabilities via
# the softmax activation function in the Dense layer
reconstructions = self.dense(outputs)
return reconstructions, h_t
# Instantiate the models
encoder_model = DummyEncoder(latent_dimension=5)
decoder_model = DummyDecoder(num_classes=num_classes)
# Instantiate reconstruction loss function
cce_loss_fxn = tf.keras.losses.CategoricalCrossentropy()
# Begin tape
with tf.GradientTape(persistent=True) as tape:
# Flatten the inputs for the encoder
reshaped_inputs = tf.reshape(original_inputs, shape=(tf.shape(original_inputs)[0], -1))
# Encode the input
z, mus, log_vars = encoder_model(reshaped_inputs, training=True)
# Expand dimensions of z so it meets recurrent decoder requirements of
# (batch, timesteps, features)
z = tf.expand_dims(z, axis=1)
################################
# SUSPECTED CAUSE OF PROBLEM
################################
# A tensor that will be modified based on model outputs
reconstruction_tensor = tf.Variable(tf.zeros_like(original_inputs))
################################
# END SUSPECTED CAUSE OF PROBLEM
################################
# A decoding loop to iteratively generate the next token (i.e., outputs)...
# in the sequence
hidden_states = None
for ith_token in range(max_seq_length):
# Reconstruct the ith_token for a given sample in the batch
reconstructions, hidden_states = decoder_model(z, hidden_states, training=True)
# Reshape the reconstructions to allow assigning to reconstruction_tensor
reconstructions = tf.squeeze(reconstructions)
# After the loop is done iterating, this tensor is the model's prediction of the
# original inputs. Therefore, after a single iteration of the loop,
# a single token prediction for each sample in the batch is assigned to
# this tensor.
reconstruction_tensor = reconstruction_tensor[:, ith_token,:].assign(reconstructions)
# Calculates losses
recon_loss = cce_loss_fxn(original_inputs, reconstruction_tensor)
latent_loss = latent_loss_fxn(mus, log_vars)
loss = recon_loss + latent_loss
# Calculate gradients
encoder_gradients = tape.gradient(loss, encoder_model.trainable_weights)
decoder_gradients = tape.gradient(loss, decoder_model.trainable_weights)
# Release tape
del tape
# Inspect gradients
print('Valid Encoder Gradients:', not(None in encoder_gradients))
print('Valid Decoder Gradients:', not(None in decoder_gradients), ' -- ', decoder_gradients)
>>> Valid Encoder Gradients: True
>>> Valid Decoder Gradients: False -- [None, None, None, None, None]