keras上手之：与tensorflow混合编程

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 tffrom keras import backend as Kfrom keras.layers import Densefrom keras.objectives import categorical_crossentropyfrom keras.metrics import categorical_accuracy as accuracyfrom 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 kerasimg = tf.placeholder(tf.float32, shape=(None, 784))# keras layers can be called on tensorflow tensorsx = Dense(128, activation='relu')(img)x = Dense(128, activation='relu')(x)preds = Dense(10, activation='softmax')(x)# labellabels = tf.placeholder(tf.float32, shape=(None, 10))# loss functionloss = 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 variablesinit_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 tffrom keras import backend as Kfrom keras.layers import Densefrom keras.objectives import categorical_crossentropyfrom keras.metrics import categorical_accuracy as accuracyfrom 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 keras    img = 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.labels    return {img:xs, labels:ys}# keras layers can be called on tensorflow tensorswith 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))# tensorboardwriter = 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 variablesinit_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：