这篇文章主要讲解了keras如何打印loss对权重的导数，内容清晰明了，对此有兴趣的小伙伴可以学习一下，相信大家阅读完之后会有帮助。
Notes
怀疑模型梯度爆炸，想打印模型 loss 对各权重的导数看看。如果如果fit来训练的话，可以用keras.callbacks.TensorBoard实现。
但此次使用train_on_batch来训练的，用K.gradients和K.function实现。
Codes
以一份 VAE 代码为例
# -*- coding: utf8 -*-
import keras
from keras.models import Model
from keras.layers import Input, Lambda, Conv2D, MaxPooling2D, Flatten, Dense, Reshape
from keras.losses import binary_crossentropy
from keras.datasets import mnist, fashion_mnist
import keras.backend as K
from scipy.stats import norm
import numpy as np
import matplotlib.pyplot as plt
BATCH = 128
N_CLASS = 10
EPOCH = 5
IN_DIM = 28 * 28
H_DIM = 128
Z_DIM = 2
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
x_train = x_train.reshape(len(x_train), -1).astype('float32') / 255.
x_test = x_test.reshape(len(x_test), -1).astype('float32') / 255.
def sampleing(args):
"""reparameterize"""
mu, logvar = args
eps = K.random_normal([K.shape(mu)[0], Z_DIM], mean=0.0, stddev=1.0)
return mu + eps * K.exp(logvar / 2.)
# encode
x_in = Input([IN_DIM])
h = Dense(H_DIM, activation='relu')(x_in)
z_mu = Dense(Z_DIM)(h) # mean，不用激活
z_logvar = Dense(Z_DIM)(h) # log variance，不用激活
z = Lambda(sampleing, output_shape=[Z_DIM])([z_mu, z_logvar]) # 只能有一个参数
encoder = Model(x_in, [z_mu, z_logvar, z], name='encoder')
# decode
z_in = Input([Z_DIM])
h_hat = Dense(H_DIM, activation='relu')(z_in)
x_hat = Dense(IN_DIM, activation='sigmoid')(h_hat)
decoder = Model(z_in, x_hat, name='decoder')
# VAE
x_in = Input([IN_DIM])
x = x_in
z_mu, z_logvar, z = encoder(x)
x = decoder(z)
out = x
vae = Model(x_in, [out, out], name='vae')
# loss_kl = 0.5 * K.sum(K.square(z_mu) + K.exp(z_logvar) - 1. - z_logvar, axis=1)
# loss_recon = binary_crossentropy(K.reshape(vae_in, [-1, IN_DIM]), vae_out) * IN_DIM
# loss_vae = K.mean(loss_kl + loss_recon)
def loss_kl(y_true, y_pred):
return 0.5 * K.sum(K.square(z_mu) + K.exp(z_logvar) - 1. - z_logvar, axis=1)
# vae.add_loss(loss_vae)
vae.compile(optimizer='rmsprop',
loss=[loss_kl, 'binary_crossentropy'],
loss_weights=[1, IN_DIM])
vae.summary()
# 获取模型权重 variable
w = vae.trainable_weights
print(w)
# 打印 KL 对权重的导数
# KL 要是 Tensor，不能是上面的函数 `loss_kl`
grad = K.gradients(0.5 * K.sum(K.square(z_mu) + K.exp(z_logvar) - 1. - z_logvar, axis=1),
w)
print(grad) # 有些是 None 的
grad = grad[grad is not None] # 去掉 None，不然报错
# 打印梯度的函数
# K.function 的输入和输出必要是 list！就算只有一个
show_grad = K.function([vae.input], [grad])
# vae.fit(x_train, # y_train, # 不能传 y_train
# batch_size=BATCH,
# epochs=EPOCH,
# verbose=1,
# validation_data=(x_test, None))
''' 以 train_on_batch 方式训练 '''
for epoch in range(EPOCH):
for b in range(x_train.shape[0] // BATCH):
idx = np.random.choice(x_train.shape[0], BATCH)
x = x_train[idx]
l = vae.train_on_batch([x], [x, x])
# 计算梯度
gd = show_grad([x])
# 打印梯度
print(gd)
# show manifold
PIXEL = 28
N_PICT = 30
grid_x = norm.ppf(np.linspace(0.05, 0.95, N_PICT))
grid_y = grid_x
figure = np.zeros([N_PICT * PIXEL, N_PICT * PIXEL])
for i, xi in enumerate(grid_x):
for j, yj in enumerate(grid_y):
noise = np.array([[xi, yj]]) # 必须秩为 2，两层中括号
x_gen = decoder.predict(noise)
# print('x_gen shape:', x_gen.shape)
x_gen = x_gen[0].reshape([PIXEL, PIXEL])
figure[i * PIXEL: (i+1) * PIXEL,
j * PIXEL: (j+1) * PIXEL] = x_gen
fig = plt.figure(figsize=(10, 10))
plt.imshow(figure, cmap='Greys_r')
fig.savefig('./variational_autoencoder.png')
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