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 inputstf_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:

The official tutorial of Tensorflow: