tensorflow和keras混用

在tensorflow中可以调用keras，有时候让模型的建立更加简单。如下这种是官方写法：

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！官网说 最重要的一步就是这里:

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。

经过测试，下面这种不需要k.set_session()也是可以的。

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# build module

img = tf.placeholder(tf.float32, shape=(None, 784))
labels = tf.placeholder(tf.float32, shape=(None, 10))

x = tf.keras.layers.Dense(128, activation='relu')(img)
x = tf.keras.layers.Dense(128, activation='relu')(x)
prediction = tf.keras.layers.Dense(10, activation='softmax')(x)

loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=prediction, labels=labels))

train_optim = tf.train.AdamOptimizer().minimize(loss)
path="/home/vv/PycharmProject/Cnnsvm/MNIST_data"
mnist_data = input_data.read_data_sets(path, one_hot=True)

with tf.Session() as sess:
    init = tf.global_variables_initializer()
    sess.run(init)

    for _ in range(1000):
        batch_x, batch_y = mnist_data.train.next_batch(50)
        sess.run(train_optim, feed_dict={img: batch_x, labels: batch_y})

    acc_pred = tf.keras.metrics.categorical_accuracy(labels, prediction)
    pred = sess.run(acc_pred, feed_dict={labels: mnist_data.test.labels, img: mnist_data.test.images})

    print('accuracy: %.3f' % (sum(pred) / len(mnist_data.test.labels)))
    print(pred)

如果在下载导入mnist数据出错，可以在网站上下好，本地导入。

mnist_data = input_data.read_data_sets(path, one_hot=True)

x1 = tf.layers.conv2d(img2,64,2)
x2 = tf.keras.layers.Conv2D(img2,64,2)
x3 = tf.keras.layers.Conv2D(64,2)(img2)

x1和x3卷积效果相同