TensorBoardColabCallback

gistfile1.txt

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

#log_dir='/Users/shlomi.shwartz/tensorflow/notebooks/logs/minist'
log_dir = 'work_dir'

batch_size  = 100
num_classes = 10
epochs      = 20

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

tbc_cb = TensorBoardColabCallback(tbc,
    histogram_freq=10,
    batch_size=32,
    write_graph=True,
    write_grads=True,
    write_images=True,
    embeddings_freq=0,
    embeddings_layer_names=None,
    embeddings_metadata=None)

tb_cb = [tbc_cb, early_stop]

# 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') / 255
x_test  = x_test.astype('float32')  / 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)
y_test  = keras.utils.to_categorical(y_test)

model = Sequential()
model.add(Dense(32, activation='relu', input_shape=(784,)))
model.add(Dense(10, activation='softmax'))

model.summary()

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

history = model.fit(x_train, y_train,
                    callbacks=tb_cb,
                    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])