我有一个VAE架构脚本如下：
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, Flatten, Dense, Conv2DTranspose, Lambda, Reshape, Layer
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import backend as K
INPUT_DIM = (64,64,3)
CONV_FILTERS = [32,64,64, 128]
CONV_KERNEL_SIZES = [4,4,4,4]
CONV_STRIDES = [2,2,2,2]
CONV_ACTIVATIONS = ['relu','relu','relu','relu']
DENSE_SIZE = 1024
CONV_T_FILTERS = [64,64,32,3]
CONV_T_KERNEL_SIZES = [5,5,6,6]
CONV_T_STRIDES = [2,2,2,2]
CONV_T_ACTIVATIONS = ['relu','relu','relu','sigmoid']
Z_DIM = 32
BATCH_SIZE = 100
LEARNING_RATE = 0.0001
KL_TOLERANCE = 0.5
class Sampling(Layer):
def call(self, inputs):
mu, log_var = inputs
epsilon = K.random_normal(shape=K.shape(mu), mean=0., stddev=1.)
return mu + K.exp(log_var / 2) * epsilon
class VAEModel(Model):
def __init__(self, encoder, decoder, r_loss_factor, **kwargs):
super(VAEModel, self).__init__(**kwargs)
self.encoder = encoder
self.decoder = decoder
self.r_loss_factor = r_loss_factor
def train_step(self, data):
if isinstance(data, tuple):
data = data[0]
def compute_kernel(x, y):
x_size = tf.shape(x)[0]
y_size = tf.shape(y)[0]
dim = tf.shape(x)[1]
tiled_x = tf.tile(tf.reshape(x, tf.stack([x_size, 1, dim])), tf.stack([1, y_size, 1]))
tiled_y = tf.tile(tf.reshape(y, tf.stack([1, y_size, dim])), tf.stack([x_size, 1, 1]))
return tf.exp(-tf.reduce_mean(tf.square(tiled_x - tiled_y), axis=2) / tf.cast(dim, tf.float32))
def compute_mmd(x, y):
x_kernel = compute_kernel(x, x)
y_kernel = compute_kernel(y, y)
xy_kernel = compute_kernel(x, y)
return tf.reduce_mean(x_kernel) + tf.reduce_mean(y_kernel) - 2 * tf.reduce_mean(xy_kernel)
with tf.GradientTape() as tape:
z_mean, z_log_var, z = self.encoder(data)
reconstruction = self.decoder(z)
reconstruction_loss = tf.reduce_mean(
tf.square(data - reconstruction), axis = [1,2,3]
)
reconstruction_loss *= self.r_loss_factor
kl_loss = 1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)
kl_loss = tf.reduce_sum(kl_loss, axis = 1)
kl_loss *= -0.5
true_samples = tf.random.normal(tf.stack([BATCH_SIZE, Z_DIM]))
loss_mmd = compute_mmd(true_samples, z)
total_loss = reconstruction_loss + loss_mmd
grads = tape.gradient(total_loss, self.trainable_weights)
self.optimizer.apply_gradients(zip(grads, self.trainable_weights))
return {
"loss": total_loss,
"reconstruction_loss": reconstruction_loss,
"kl_loss": kl_loss,
"mmd_loss": loss_mmd
}
def call(self,inputs):
latent = self.encoder(inputs)
return self.decoder(latent)
class VAE():
def __init__(self):
self.models = self._build()
self.full_model = self.models[0]
self.encoder = self.models[1]
self.decoder = self.models[2]
self.input_dim = INPUT_DIM
self.z_dim = Z_DIM
self.learning_rate = LEARNING_RATE
self.kl_tolerance = KL_TOLERANCE
def _build(self):
vae_x = Input(shape=INPUT_DIM, name='observation_input')
vae_c1 = Conv2D(filters = CONV_FILTERS[0], kernel_size = CONV_KERNEL_SIZES[0], strides = CONV_STRIDES[0], activation=CONV_ACTIVATIONS[0], name='conv_layer_1')(vae_x)
vae_c2 = Conv2D(filters = CONV_FILTERS[1], kernel_size = CONV_KERNEL_SIZES[1], strides = CONV_STRIDES[1], activation=CONV_ACTIVATIONS[0], name='conv_layer_2')(vae_c1)
vae_c3= Conv2D(filters = CONV_FILTERS[2], kernel_size = CONV_KERNEL_SIZES[2], strides = CONV_STRIDES[2], activation=CONV_ACTIVATIONS[0], name='conv_layer_3')(vae_c2)
vae_c4= Conv2D(filters = CONV_FILTERS[3], kernel_size = CONV_KERNEL_SIZES[3], strides = CONV_STRIDES[3], activation=CONV_ACTIVATIONS[0], name='conv_layer_4')(vae_c3)
vae_z_in = Flatten()(vae_c4)
vae_z_mean = Dense(Z_DIM, name='mu')(vae_z_in)
vae_z_log_var = Dense(Z_DIM, name='log_var')(vae_z_in)
vae_z = Sampling(name='z')([vae_z_mean, vae_z_log_var])
#### DECODER:
vae_z_input = Input(shape=(Z_DIM,), name='z_input')
vae_dense = Dense(1024, name='dense_layer')(vae_z_input)
vae_unflatten = Reshape((1,1,DENSE_SIZE), name='unflatten')(vae_dense)
vae_d1 = Conv2DTranspose(filters = CONV_T_FILTERS[0], kernel_size = CONV_T_KERNEL_SIZES[0] , strides = CONV_T_STRIDES[0], activation=CONV_T_ACTIVATIONS[0], name='deconv_layer_1')(vae_unflatten)
vae_d2 = Conv2DTranspose(filters = CONV_T_FILTERS[1], kernel_size = CONV_T_KERNEL_SIZES[1] , strides = CONV_T_STRIDES[1], activation=CONV_T_ACTIVATIONS[1], name='deconv_layer_2')(vae_d1)
vae_d3 = Conv2DTranspose(filters = CONV_T_FILTERS[2], kernel_size = CONV_T_KERNEL_SIZES[2] , strides = CONV_T_STRIDES[2], activation=CONV_T_ACTIVATIONS[2], name='deconv_layer_3')(vae_d2)
vae_d4 = Conv2DTranspose(filters = CONV_T_FILTERS[3], kernel_size = CONV_T_KERNEL_SIZES[3] , strides = CONV_T_STRIDES[3], activation=CONV_T_ACTIVATIONS[3], name='deconv_layer_4')(vae_d3)
#### MODELS
vae_encoder = Model(vae_x, [vae_z_mean, vae_z_log_var, vae_z], name = 'encoder')
vae_decoder = Model(vae_z_input, vae_d4, name = 'decoder')
vae_full = VAEModel(vae_encoder, vae_decoder, 10000)
opti = Adam(lr=LEARNING_RATE)
vae_full.compile(optimizer=opti)
return (vae_full,vae_encoder, vae_decoder)
def set_weights(self, filepath):
self.full_model.load_weights(filepath)
def train(self, data):
self.full_model.fit(data, data,
shuffle=True,
epochs=1,
batch_size=BATCH_SIZE)
def save_weights(self, filepath):
self.full_model.save_weights(filepath)
问题：
vae = VAE()
vae.set_weights(filepath)
抛出:
File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py",
line 2200, in load_weights
'Unable to load weights saved in HDF5 format into a subclassed ' ValueError: Unable to load weights saved in HDF5 format into a
subclassed Model which has not created its variables yet. Call the
Model first, then load the weights.
我不太懂面向对象编程，也不确定这是什么意思。令人惊讶的是，上面的代码一直在工作，直到它停止工作。该模型正在从头开始训练，并将权重保存在filepath中。但现在加载相同的权重时，它会抛出以上错误！