是否有任何工具/库可以将tensorflow lstm模型转换为.mlmodel格式以在iOS应用中运行

Question

我有一个由lstm层组成的简单张量流模型-例如 tf.contrib.rnn.LSTMBlockCell 或 tf.keras.layers.LSTM （我可以提供如果需要，还提供示例代码）。我想在iOS应用上运行模型。但是，我看过几个网站，说目前没有任何方法可以转换和运行由iOS应用程序上的LSTM层组成的tensorflow模型。

我已经尝试过使用这些工具/库将tensorflow模型转换为.mlmodel格式（或.tflite格式）
1. Swift for Tensorflow
2. iOS版Tensorflow Lite
3. tfcoreml

但是，这些工具似乎也无法将lstm图层模型转换为.mlmodel格式。但是，这些工具允许使用要添加的自定义图层。但是我不知道如何添加LSTM自定义层。

我是否在说不支持在iOS应用中运行tensorflow lstm模型是错误的吗？如果是，那么请指导我如何继续将模型包含在iOS应用中。是否可以使用其他工具/库将其转换为.mlmodel格式。如果没有，那么将来是否有计划包括对iOS的tensorflow支持？

型号

import tensorflow as tf
from tensorflow.contrib import rnn
from tensorflow.contrib.rnn import *

# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)


#Summary parameters
logs_path = "logs/"

# Training Parameters
learning_rate = 0.001
training_steps = 1000
batch_size = 128
display_step = 200

# Network Parameters
num_input = 28 # MNIST data input (img shape: 28*28)
timesteps = 28 # timesteps
num_hidden = 128 # hidden layer num of features
num_classes = 10 # MNIST total classes (0-9 digits)

# tf Graph input
X = tf.placeholder("float", [None, timesteps, num_input])
Y = tf.placeholder("float", [None, num_classes])

# Define weights
weights = {
    'out': tf.Variable(tf.random_normal([num_hidden, num_classes]))
}
biases = {
    'out': tf.Variable(tf.random_normal([num_classes]))
}


def RNN(x, weights, biases):
    # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input)
    x = tf.unstack(x, timesteps, 1)

    # Define a lstm cell with tensorflow
    lstm_cell = rnn.LSTMBlockCell(num_hidden, forget_bias = 1.0)
    #lstm_cell = tf.keras.layers.LSTMCell(num_hidden, unit_forget_bias=True)

    # Get lstm cell output
    outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)

    # Linear activation, using rnn inner loop last output
    return tf.matmul(outputs[-1], weights['out']) + biases['out']

logits = RNN(X, weights, biases)
with tf.name_scope('Model'):
    prediction = tf.nn.softmax(logits, name = "prediction_layer")

with tf.name_scope('Loss'):
    # Define loss and optimizer
    loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y, name = "loss_layer"), name = "reduce_mean_loss")

with tf.name_scope('SGD'):
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate, name = "Gradient_Descent")
    train_op = optimizer.minimize(loss_op, name = "minimize_layer")


with tf.name_scope('Accuracy'):
    # Evaluate model (with test logits, for dropout to be disabled)
    correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1), name = "correct_pred_layer")
    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name = "reduce_mean_acc_layer")

# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()

#Create a summary to monitor cost tensor
tf.summary.scalar("loss", loss_op)
#Create a summary to monitor accuracy tensor
tf.summary.scalar("accuracy", accuracy)
#Merge all summaries into a single op
merged_summary_op = tf.summary.merge_all()

saver = tf.train.Saver()
save_path = ""
model_save = "model.ckpt"

# Start training
with tf.Session() as sess:

    # Run the initializer
    sess.run(init)

    # op to write logs to Tensorboard
    summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())

    for step in range(1, training_steps+1):
        total_batch = int(mnist.train.num_examples/batch_size)

        batch_x, batch_y = mnist.train.next_batch(batch_size)
        # Reshape data to get 28 seq of 28 elements
        batch_x = batch_x.reshape((batch_size, timesteps, num_input))
        # Run optimization op (backprop)
        sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
        if step % display_step == 0 or step == 1:
            # Calculate batch loss and accuracy
            loss, acc, summary = sess.run([loss_op, accuracy, merged_summary_op], feed_dict={X: batch_x,
                                                                 Y: batch_y})

            # Write logs at every iteration
            summary_writer.add_summary(summary, step * total_batch)

            print("Step " + str(step) + ", Minibatch Loss= " + \
                  "{:.4f}".format(loss) + ", Training Accuracy= " + \
                  "{:.3f}".format(acc))
    saver.save(sess, model_save)
    tf.train.write_graph(sess.graph_def, save_path, 'save_graph.pbtxt')
    #print(sess.graph.get_operations())

    print("Optimization Finished!")

    print("Run the command line:\n" \
            "--> tensorboard --logdir=logs/ " \
            "\nThen open http://0.0.0.0:6006/ into your web browser")

    # Calculate accuracy for 128 mnist test images
    test_len = 128
    test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input))
    test_label = mnist.test.labels[:test_len]
    print("Testing Accuracy:", \
        sess.run(accuracy, feed_dict={X: test_data, Y: test_label}))

用于生成冻结模型的代码

import tensorflow as tf
import numpy as np
from tensorflow.python.tools import freeze_graph

save_path = ""
model_name = "test_model_tf_keras_layers_lstm"
input_graph_path = save_path + "save_graph.pbtxt"
checkpoint_path = save_path + "model.ckpt"
input_saver_def_path = ""
input_binary = False
output_node_names = "Model/prediction_layer" #output node's name. Should match to that mentioned in the code
restore_op_name = 'save/restore_all'
filename_tensor_name = 'save/const:0'
output_frozen_graph_name = save_path + 'frozen_model' + '.pb'   # name of .pb file that one would like to give
clear_devices = True

freeze_graph.freeze_graph(input_graph_path, input_saver_def_path, input_binary, checkpoint_path, output_node_names, restore_op_name, filename_tensor_name, output_frozen_graph_name, clear_devices, "")
print("Model Freezed")

转换代码以生成.mlmodel格式文件

import tfcoreml

coreml_model = tfcoreml.convert(tf_model_path = 'frozen_model_test_model_tf_keras_layers_lstm.pb', 
                      mlmodel_path = 'test_model.mlmodel', 
                      output_feature_names = ['Model/prediction_layer:0'], 
                      add_custom_layers = True)


coreml_model.save("test_model.mlmodel")

显示的错误消息 lstm_cell = rnn.BasicLSTMCell(num_hidden, name = "lstm_cell")

Value Error: Split op case not handled. Input shape = [1, 512], output shape = [1, 128]

显示的错误消息 lstm_cell = rnn.LSTMBlockCell(num_hidden, name = "lstm_cell")

InvalidArgumentError (see above for traceback): No OpKernel was registered to support Op 'LSTMBlockCell' used by node rnn/lstm_cell/LSTMBlockCell (defined at /anaconda2/lib/python2.7/site-packages/tfcoreml/_tf_coreml_converter.py:153) with these attrs: [forget_bias=1, use_peephole=false, cell_clip=-1, T=DT_FLOAT]
Registered devices: [CPU]
Registered kernels:
  <no registered kernels>

     [[node rnn/lstm_cell/LSTMBlockCell (defined at /anaconda2/lib/python2.7/site-packages/tfcoreml/_tf_coreml_converter.py:153) ]]

我希望冻结的tensorflow模型可以转换为.mlmodel格式。