'''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()

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()

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)