zoukankan      html  css  js  c++  java
  • Using Tensorflow SavedModel Format to Save and Do Predictions

    We are now trying to deploy our Deep Learning model onto Google Cloud. It is required to use Google Function to trigger the Deep Learning predictions. However, when pre-trained models are stored on cloud, it is impossible to get the exact directory path and restore the tensorflow session like what we did on local machine.

    So we turn to use SavedModel, which is quite like a 'Prediction Mode' of tensorflow. According to official turotial: a SavedModel contains a complete TensorFlow program, including weights and computation. It does not require the original model building code to run, which makes it useful for sharing or deploying.

    The Definition of our graph, just here to show the input and output tensors:

    '''RNN Model Definition'''
    tf.reset_default_graph()
    ''''''
    #define inputs
    tf_x = tf.placeholder(tf.float32, [None, window_size,1],name='x')
    tf_y = tf.placeholder(tf.int32, [None, 2],name='y')
    
    
    cells = [tf.keras.layers.LSTMCell(units=n) for n in num_units]
    stacked_rnn_cell = tf.keras.layers.StackedRNNCells(cells)
    outputs, (h_c, h_n) = tf.nn.dynamic_rnn(
            stacked_rnn_cell,                   # cell you have chosen
            tf_x,                      # input
            initial_state=None,         # the initial hidden state
            dtype=tf.float32,           # must given if set initial_state = None
            time_major=False,           # False: (batch, time step, input); True: (time step, batch, input)
    )
    l1 = tf.layers.dense(outputs[:, -1, :],32,activation=tf.nn.relu,name='l1')
    l2 = tf.layers.dense(l1,8,activation=tf.nn.relu,name='l6')
    pred = tf.layers.dense(l2,2,activation=tf.nn.relu,name='pred')
    
    with tf.name_scope('loss'):
        cross_entropy =  tf.nn.softmax_cross_entropy_with_logits_v2(labels=tf_y, logits=pred) 
        loss = tf.reduce_mean(cross_entropy)
        tf.summary.scalar("loss",tensor=loss)
    train_op = tf.train.AdamOptimizer(LR).minimize(loss)
    accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(tf_y, axis=1), tf.argmax(pred, axis=1)), tf.float32))
    
    init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) 
    saver = tf.train.Saver()
    

    Train and Save the model, we use simple_save:

    sess = tf.Session()
    sess.run(init_op)
    
    for i in range(0,n):
        sess.run(train_op,{tf_x:batch_X , tf_y:batch_y})
        ...   
    tf.saved_model.simple_save(sess, 'simple_save/model', 
                               inputs={"x": tf_x},outputs={"pred": pred})
    sess.close()
    

    Restore and Predict:

    with tf.Session(graph=tf.Graph()) as sess:
        tf.saved_model.loader.load(sess, ["serve"], 'simple_save_test/model')
        batch = sess.run('pred/Relu:0',feed_dict={'x:0':dataX.reshape([-1,24,1])}) 
        print(batch)
    

    Reference:

     medium post: https://medium.com/@jsflo.dev/saving-and-loading-a-tensorflow-model-using-the-savedmodel-api-17645576527

    The official tutorial of Tensorflow: https://www.tensorflow.org/guide/saved_model

  • 相关阅读:
    网站调整为黑白的方法
    滚动条样式优化
    js点击页面其他地方如何隐藏div元素菜单
    微信分享网页时自定义标题、描述和图片
    纯CSS3美化单选按钮radio
    纯CSS3实现圆形进度条动画
    解决checkbox的attr(checked)一直为undefined问题
    jQuery – 鼠标经过(hover)事件的延时处理
    PC版模块滚动不显示滚动条效果
    上传文件样式美化
  • 原文地址:https://www.cnblogs.com/rhyswang/p/10971237.html
Copyright © 2011-2022 走看看