tensorflow具备许多优秀的函数和功能，比如tensorboard，keras作为tensorflow的高级API， 封装很多tensorflow的代码，使得代码模块化，非常方便。
当然，由于keras的模型和层与tensorflow的张量高度兼容，可以用keras建模，用tensorflow输入输出。
例如下面的例子：
import tensorflow as tf
from keras import backend as K
from keras.layers import Dense
from keras.objectives import categorical_crossentropy
from keras.metrics import categorical_accuracy as accuracy
from tensorflow.examples.tutorials.mnist import input_data
# create a tf session，and register with keras。
sess = tf.Session()
K.set_session(sess)# this place holder is the same with input layer in keras
img = tf.placeholder(tf.float32, shape=(None, 784))
# keras layers can be called on tensorflow tensors
x = Dense(128, activation='relu')(img)
x = Dense(128, activation='relu')(x)
preds = Dense(10, activation='softmax')(x)
# label
labels = tf.placeholder(tf.float32, shape=(None, 10))
# loss function
loss = tf.reduce_mean(categorical_crossentropy(labels, preds))train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)mnist_data = input_data.read_data_sets('MNIST_data', one_hot=True)# initialize all variables
init_op = tf.global_variables_initializer()
sess.run(init_op)with sess.as_default():for i in range(1000):batch = mnist_data.train.next_batch(50)train_step.run(feed_dict={img:batch[0],labels:batch[1]})acc_value = accuracy(labels, preds)
with sess.as_default():print(acc_value.eval(feed_dict={img:mnist_data.test.images,labels:mnist_data.test.labels}))上述代码中，在训练阶段直接采用了tf的方式，甚至都没有定义keras的model！最重要的一步就是这里:
# create a tf session，and register with keras。
sess = tf.Session()
K.set_session(sess)创建一个TensorFlow会话并且注册Keras。这意味着Keras将使用我们注册的会话来初始化它在内部创建的所有变量。
keras的层和模型都充分兼容tensorflow的各种scope, 例如name scope，device scope和graph scope。修改一下，在tensorboard输出训练过程中的loss曲线：
import tensorflow as tf
from keras import backend as K
from keras.layers import Dense
from keras.objectives import categorical_crossentropy
from keras.metrics import categorical_accuracy as accuracy
from tensorflow.examples.tutorials.mnist import input_datasess = tf.Session()
K.set_session(sess)with tf.name_scope('input'):# this place holder is the same with input layer in kerasimg = tf.placeholder(tf.float32, shape=(None, 784))labels = tf.placeholder(tf.float32, shape=(None, 10))mnist_data = input_data.read_data_sets('MNIST_data', one_hot=True)
def feed_dict(train):if train:xs, ys = mnist_data.train.next_batch(50)else:xs, ys = mnist_data.test.images, mnist_data.test.labelsreturn {img:xs, labels:ys}# keras layers can be called on tensorflow tensors
with tf.name_scope('NN'):x = Dense(128, activation='relu')(img)x = Dense(128, activation='relu')(x)preds = Dense(10, activation='softmax')(x)with tf.name_scope('loss'):loss = tf.reduce_mean(categorical_crossentropy(labels, preds))
# tensorboard
writer = tf.summary.FileWriter('./keras_tensorflow_log/')
outloss = tf.summary.scalar('loss', loss)
merged = tf.summary.merge([outloss])with tf.name_scope('train'):train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)# initialize all variables
init_op = tf.global_variables_initializer()
sess.run(init_op)with sess.as_default():for i in range(1000):summary, loss = sess.run([merged, train_step], feed_dict=feed_dict(True))writer.add_summary(summary, global_step=i)
writer.close() 在命令行输入：
tensorboard --logdir=./keras_tensorflow_log
打开tensorboard就可以看到loss history了：
一定注意地址：http://0.0.0.0:6006/
