Keras - 保存mnist数据集的图像嵌入

Question

我为MNIST db编写了以下简单的MLP网络。

from __future__ import print_function

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras import callbacks


batch_size = 100
num_classes = 10
epochs = 20

tb = callbacks.TensorBoard(log_dir='/Users/shlomi.shwartz/tensorflow/notebooks/logs/minist', histogram_freq=10, batch_size=32,
                           write_graph=True, write_grads=True, write_images=True,
                           embeddings_freq=10, embeddings_layer_names=None,
                           embeddings_metadata=None)

early_stop = callbacks.EarlyStopping(monitor='val_loss', min_delta=0,
                     patience=3, verbose=1, mode='auto')


# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Dense(200, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(100, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(60, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(30, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='softmax'))

model.summary()

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

history = model.fit(x_train, y_train,
                    callbacks=[tb,early_stop],
                    batch_size=batch_size,
                    epochs=epochs,
                    verbose=1,
                    validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

模型运行正常，我可以看到TensorBoard上的标量信息。 但是，当我更改embeddings_freq = 10以尝试可视化图像时（如此处所示 ）我收到以下错误：

Traceback (most recent call last):
  File "/Users/shlomi.shwartz/IdeaProjects/TF/src/minist.py", line 65, in <module>
    validation_data=(x_test, y_test))
  File "/Users/shlomi.shwartz/tensorflow/lib/python3.6/site-packages/keras/models.py", line 870, in fit
    initial_epoch=initial_epoch)
  File "/Users/shlomi.shwartz/tensorflow/lib/python3.6/site-packages/keras/engine/training.py", line 1507, in fit
    initial_epoch=initial_epoch)
  File "/Users/shlomi.shwartz/tensorflow/lib/python3.6/site-packages/keras/engine/training.py", line 1117, in _fit_loop
    callbacks.set_model(callback_model)
  File "/Users/shlomi.shwartz/tensorflow/lib/python3.6/site-packages/keras/callbacks.py", line 52, in set_model
    callback.set_model(model)
  File "/Users/shlomi.shwartz/tensorflow/lib/python3.6/site-packages/keras/callbacks.py", line 719, in set_model
    self.saver = tf.train.Saver(list(embeddings.values()))
  File "/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1139, in __init__
    self.build()
  File "/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1161, in build
    raise ValueError("No variables to save")
ValueError: No variables to save

问：我错过了什么？ 这是在Keras这样做的正确方法吗？

更新：我知道有一些先决条件，以便使用嵌入投影，但我没有在Keras找到一个很好的教程，任何帮助将不胜感激。

Answer 1

在callbacks.TensorBoard中，所谓的“嵌入”。广义上， callbacks.TensorBoard是任何图层权重。 据Keras文件说 ：

embeddings_layer_names：要关注的图层名称列表。 如果为None或空列表，则将监视所有嵌入层。

因此，默认情况下 ，它将监视Embedding层，但您并不需要Embedding层来使用此可视化工具。

在您提供的MLP示例中，缺少的是embeddings_layer_names参数。 你必须弄清楚你要想象的层。 假设您想要显示所有Dense图层的权重（或Keras中的kernel ），您可以指定embeddings_layer_names如下所示：

model = Sequential()
model.add(Dense(200, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(100, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(60, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(30, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='softmax'))

embedding_layer_names = set(layer.name
                            for layer in model.layers
                            if layer.name.startswith('dense_'))

tb = callbacks.TensorBoard(log_dir='temp', histogram_freq=10, batch_size=32,
                           write_graph=True, write_grads=True, write_images=True,
                           embeddings_freq=10, embeddings_metadata=None,
                           embeddings_layer_names=embedding_layer_names)

model.compile(...)
model.fit(...)

然后，您可以在TensorBoard中看到类似的内容：

如果你想弄清楚embeddings_layer_names发生了什么，你可以在Keras源中看到相关的行 。

---

编辑：

所以这是一个用于可视化图层输出的脏解决方案。 由于最初的TensorBoard回调不支持这一点，因此实现新的回调似乎是不可避免的。

由于在这里占用大量的页面空间来重写整个TensorBoard回调，我只需要扩展原始的TensorBoard ，并写出不同的部分（已经很长）。 但是为了避免重复计算和模型保存，重写TensorBoard回调将是一种更好，更清洁的方式。

import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector
from keras import backend as K
from keras.models import Model
from keras.callbacks import TensorBoard

class TensorResponseBoard(TensorBoard):
    def __init__(self, val_size, img_path, img_size, **kwargs):
        super(TensorResponseBoard, self).__init__(**kwargs)
        self.val_size = val_size
        self.img_path = img_path
        self.img_size = img_size

    def set_model(self, model):
        super(TensorResponseBoard, self).set_model(model)

        if self.embeddings_freq and self.embeddings_layer_names:
            embeddings = {}
            for layer_name in self.embeddings_layer_names:
                # initialize tensors which will later be used in `on_epoch_end()` to
                # store the response values by feeding the val data through the model
                layer = self.model.get_layer(layer_name)
                output_dim = layer.output.shape[-1]
                response_tensor = tf.Variable(tf.zeros([self.val_size, output_dim]),
                                              name=layer_name + '_response')
                embeddings[layer_name] = response_tensor

            self.embeddings = embeddings
            self.saver = tf.train.Saver(list(self.embeddings.values()))

            response_outputs = [self.model.get_layer(layer_name).output
                                for layer_name in self.embeddings_layer_names]
            self.response_model = Model(self.model.inputs, response_outputs)

            config = projector.ProjectorConfig()
            embeddings_metadata = {layer_name: self.embeddings_metadata
                                   for layer_name in embeddings.keys()}

            for layer_name, response_tensor in self.embeddings.items():
                embedding = config.embeddings.add()
                embedding.tensor_name = response_tensor.name

                # for coloring points by labels
                embedding.metadata_path = embeddings_metadata[layer_name]

                # for attaching images to the points
                embedding.sprite.image_path = self.img_path
                embedding.sprite.single_image_dim.extend(self.img_size)

            projector.visualize_embeddings(self.writer, config)

    def on_epoch_end(self, epoch, logs=None):
        super(TensorResponseBoard, self).on_epoch_end(epoch, logs)

        if self.embeddings_freq and self.embeddings_ckpt_path:
            if epoch % self.embeddings_freq == 0:
                # feeding the validation data through the model
                val_data = self.validation_data[0]
                response_values = self.response_model.predict(val_data)
                if len(self.embeddings_layer_names) == 1:
                    response_values = [response_values]

                # record the response at each layers we're monitoring
                response_tensors = []
                for layer_name in self.embeddings_layer_names:
                    response_tensors.append(self.embeddings[layer_name])
                K.batch_set_value(list(zip(response_tensors, response_values)))

                # finally, save all tensors holding the layer responses
                self.saver.save(self.sess, self.embeddings_ckpt_path, epoch)

要使用它：

tb = TensorResponseBoard(log_dir=log_dir, histogram_freq=10, batch_size=10,
                         write_graph=True, write_grads=True, write_images=True,
                         embeddings_freq=10,
                         embeddings_layer_names=['dense_1'],
                         embeddings_metadata='metadata.tsv',
                         val_size=len(x_test), img_path='images.jpg', img_size=[28, 28])

在启动TensorBoard之前，您需要将标签和图像保存到log_dir以进行可视化：

from PIL import Image
img_array = x_test.reshape(100, 100, 28, 28)
img_array_flat = np.concatenate([np.concatenate([x for x in row], axis=1) for row in img_array])
img = Image.fromarray(np.uint8(255 * (1. - img_array_flat)))
img.save(os.path.join(log_dir, 'images.jpg'))
np.savetxt(os.path.join(log_dir, 'metadata.tsv'), np.where(y_test)[1], fmt='%d')

这是结果：

Answer 2

在Keras中至少需要一个嵌入层。 关于统计数据是一个很好的解释。 它不直接用于Keras，但概念大致相同。 什么是神经网络中的嵌入层

Answer 3

因此，我得出结论，您实际想要的是（从您的帖子中不完全清楚）是以类似于此Tensorboard演示的方式可视化模型的预测 。

首先， 即使在Tensorflow中 ，再现这些东西也是非常重要的，更不用说Keras了 。 所述演示非常简短并传递对元数据和精灵图像等引用的引用，这些引用是获得此类可视化所必需的。

一句话：尽管非常重要，但确实可以用Keras做到这一点。 你不需要Keras回调; 您只需要模型预测，必要的元数据和精灵图像，以及一些纯TensorFlow代码。 所以，

第1步 - 获取测试集的模型预测：

emb = model.predict(x_test) # 'emb' for embedding

步骤2a - 使用测试集的真实标签构建元数据文件：

import numpy as np

LOG_DIR = '/home/herc/SO/tmp'  # FULL PATH HERE!!!

metadata_file = os.path.join(LOG_DIR, 'metadata.tsv')
with open(metadata_file, 'w') as f:
    for i in range(len(y_test)):
        c = np.nonzero(y_test[i])[0][0]
        f.write('{}\n'.format(c))

步骤2b -让精灵图像mnist_10k_sprite.png由TensorFlow人提供的在这里 ，并把它放在你的LOG_DIR

第3步 - 编写一些Tensorflow代码：

import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector

embedding_var = tf.Variable(emb,  name='final_layer_embedding')
sess = tf.Session()
sess.run(embedding_var.initializer)
summary_writer = tf.summary.FileWriter(LOG_DIR)
config = projector.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = embedding_var.name

# Specify the metadata file:
embedding.metadata_path = os.path.join(LOG_DIR, 'metadata.tsv')

# Specify the sprite image: 
embedding.sprite.image_path = os.path.join(LOG_DIR, 'mnist_10k_sprite.png')
embedding.sprite.single_image_dim.extend([28, 28]) # image size = 28x28

projector.visualize_embeddings(summary_writer, config)
saver = tf.train.Saver([embedding_var])
saver.save(sess, os.path.join(LOG_DIR, 'model2.ckpt'), 1)

然后，在LOG_DIR中运行Tensorboard， LOG_DIR标签选择颜色，这是您得到的：

修改它以便获得其他层的预测是很简单的，尽管在这种情况下Keras Functional API可能是更好的选择。