Keras - Save image embedding of the mnist data set

Question

I've written the following simple MLP network for the MNIST db.

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])

The model ran fine, and I could see the scalars info on TensorBoard. However when I've changed embeddings_freq=10 to try and visualize the images (Like seen here) I got the following error:

Traceback (most recent call last):
  File "/Users/shlomi.shwartz/IdeaProjects/TF/src/minist.py", line 65, in 
    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

Q: What am I missing? is that the right way of doing it in Keras?

Update: I understand there is some prerequisite in order to use embedding projection, however I haven't found a good tutorial for doing so in Keras, any help would be appreciated.

Keras - Save image embedding of the mnist data set

3 Answers