我对神经网络编程很陌生，对Keras上的代码示例也有问题。
Keras：https://keras.io/examples/generative/vae/
Github：https://github.com/keras-team/keras-io/blob/master/examples/generative/vae.py
"""
Title: Variational AutoEncoder
Author: [fchollet](https://twitter.com/fchollet)
Date created: 2020/05/03
Last modified: 2020/05/03
Description: Convolutional Variational AutoEncoder (VAE) trained on MNIST digits.
"""
"""
## Setup
"""
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
"""
## Create a sampling layer
"""
class Sampling(layers.Layer):
"""Uses (z_mean, z_log_var) to sample z, the vector encoding a digit."""
def call(self, inputs):
z_mean, z_log_var = inputs
batch = tf.shape(z_mean)[0]
dim = tf.shape(z_mean)[1]
epsilon = tf.keras.backend.random_normal(shape=(batch, dim))
return z_mean + tf.exp(0.5 * z_log_var) * epsilon
"""
## Build the encoder
"""
latent_dim = 2
encoder_inputs = keras.Input(shape=(28, 28, 1))
x = layers.Conv2D(32, 3, activation="relu", strides=2, padding="same")(encoder_inputs)
x = layers.Conv2D(64, 3, activation="relu", strides=2, padding="same")(x)
x = layers.Flatten()(x)
x = layers.Dense(16, activation="relu")(x)
z_mean = layers.Dense(latent_dim, name="z_mean")(x)
z_log_var = layers.Dense(latent_dim, name="z_log_var")(x)
z = Sampling()([z_mean, z_log_var])
encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name="encoder")
encoder.summary()
"""
## Build the decoder
"""
latent_inputs = keras.Input(shape=(latent_dim,))
x = layers.Dense(7 * 7 * 64, activation="relu")(latent_inputs)
x = layers.Reshape((7, 7, 64))(x)
x = layers.Conv2DTranspose(64, 3, activation="relu", strides=2, padding="same")(x)
x = layers.Conv2DTranspose(32, 3, activation="relu", strides=2, padding="same")(x)
decoder_outputs = layers.Conv2DTranspose(1, 3, activation="sigmoid", padding="same")(x)
decoder = keras.Model(latent_inputs, decoder_outputs, name="decoder")
decoder.summary()
"""
## Define the VAE as a `Model` with a custom `train_step`
"""
class VAE(keras.Model):
def __init__(self, encoder, decoder, **kwargs):
super(VAE, self).__init__(**kwargs)
self.encoder = encoder
self.decoder = decoder
def train_step(self, data):
if isinstance(data, tuple):
data = data[0]
with tf.GradientTape() as tape:
z_mean, z_log_var, z = self.encoder(data)
reconstruction = self.decoder(z)
reconstruction_loss = tf.reduce_mean(
keras.losses.binary_crossentropy(data, reconstruction)
)
reconstruction_loss *= 28 * 28
kl_loss = 1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)
kl_loss = tf.reduce_mean(kl_loss)
kl_loss *= -0.5
total_loss = reconstruction_loss + kl_loss
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,
}
"""
## Train the VAE
"""
(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()
mnist_digits = np.concatenate([x_train, x_test], axis=0)
mnist_digits = np.expand_dims(mnist_digits, -1).astype("float32") / 255
vae = VAE(encoder, decoder)
vae.compile(optimizer=keras.optimizers.Adam())
vae.fit(mnist_digits, epochs=30, batch_size=128)
"""
## Display a grid of sampled digits
"""
import matplotlib.pyplot as plt
def plot_latent(encoder, decoder):
# display a n*n 2D manifold of digits
n = 30
digit_size = 28
scale = 2.0
figsize = 15
figure = np.zeros((digit_size * n, digit_size * n))
# linearly spaced coordinates corresponding to the 2D plot
# of digit classes in the latent space
grid_x = np.linspace(-scale, scale, n)
grid_y = np.linspace(-scale, scale, n)[::-1]
for i, yi in enumerate(grid_y):
for j, xi in enumerate(grid_x):
z_sample = np.array([[xi, yi]])
x_decoded = decoder.predict(z_sample)
digit = x_decoded[0].reshape(digit_size, digit_size)
figure[
i * digit_size : (i + 1) * digit_size,
j * digit_size : (j + 1) * digit_size,
] = digit
plt.figure(figsize=(figsize, figsize))
start_range = digit_size // 2
end_range = n * digit_size + start_range
pixel_range = np.arange(start_range, end_range, digit_size)
sample_range_x = np.round(grid_x, 1)
sample_range_y = np.round(grid_y, 1)
plt.xticks(pixel_range, sample_range_x)
plt.yticks(pixel_range, sample_range_y)
plt.xlabel("z[0]")
plt.ylabel("z[1]")
plt.imshow(figure, cmap="Greys_r")
plt.show()
plot_latent(encoder, decoder)
"""
## Display how the latent space clusters different digit classes
"""
def plot_label_clusters(encoder, decoder, data, labels):
# display a 2D plot of the digit classes in the latent space
z_mean, _, _ = encoder.predict(data)
plt.figure(figsize=(12, 10))
plt.scatter(z_mean[:, 0], z_mean[:, 1], c=labels)
plt.colorbar()
plt.xlabel("z[0]")
plt.ylabel("z[1]")
plt.show()
(x_train, y_train), _ = keras.datasets.mnist.load_data()
x_train = np.expand_dims(x_train, -1).astype("float32") / 255
plot_label_clusters(encoder, decoder, x_train, y_train)
这是关于在MNIST数据集上使用Keras构建的VAE(Variational AutoEncoder(。当我从GitHub复制示例代码时，我总是会收到以下失败(我没有更改代码(：
"ValueError: The model cannot be compiled because it has no loss to optimize." Also I get following Warning: *"WARNING:tensorflow:Output output_1 missing from loss dictionary.
我们认为这是故意的。拟合和评估API将不期望任何数据被传递到output_1
开始时的更多警告：
"WARNING:tensorflow:AutoGraph could not transform <bound method Sampling.call of <__main__.Sampling object at 0x000002CB451262E8>> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause:
WARNING: AutoGraph could not transform <bound method Sampling.call of <__main__.Sampling object at 0x000002CB451262E8>> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output."
到目前为止，我在Windows 10上尝试了Python 3.6和Python 3.7。有人收到这个错误吗？谁知道解决方案？
提前谢谢！
我也遇到了类似的问题。这个例子假设使用TF2.3.0，检查你的TF版本，如果可能的话，升级它
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