leeor-langer

import tensorflow as tf

# Save model to pb file (SavedModel format)
tf.saved_model.save(meta_learnen, model_dir, signatures=meta_learner.call)
print('pb file saved')

# Optimize SavedModel with tflite
converter = tf.lite.TFLiteConverter.from_saved_model(model_dir)
converter.target_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,
                        tf.lite.OpsSet.SELECT_TF_OPS]

leeor-langer

import tensorflow as tf
from tensorflow.keras import Model, layers

class GestureMetaLearner(Model):
    def __init__(self, batch_size, spectral_lvls, num_sensors, window_len_snc, window_len_imu, is_training=True):
        super(GestureMetaLearner, self).__init__()

        # NN high-level params
        self.params1 = 1
        self.params2 = 2

leeor-langer

#include "TensorflowInference.h"

// This is the "feed_dict" key value pair (we have a vector of these for multiple inputs definition)
typedef vector<pair<const char *, vector<long long>>> ModelInputType;
ModelInputType m_modeInputsDefs;

// Using floats for example as input \ output 
typedef std::vector<float> BufferType;

// Resize to NUM_OF_INPUTS

leeor-langer

bazel build -c opt --copt="-fPIC" --cxxopt='-std=c++11' --crosstool_top=//external:android/crosstool --cpu=arm64-v8a --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --config monolithic tensorflow/tools/tfwld:libTensorflowInference.so

leeor-langer

bazel build -c opt --copt="-fPIC" --cxxopt='-std=c++11' --crosstool_top=//external:android/crosstool --cpu=arm64-v8a --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --config monolithic tensorflow/tools/tfwld:libTensorflowInference.so

leeor-langer

package(default_visibility = ["//visibility:public"])

load(
    "//tensorflow:tensorflow.bzl",
    "tf_copts",
    "tf_cc_test",
    "tf_cc_binary",
)

cc_library(