FINAL

Author: Rob

.gitignore

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+.ipynb_checkpoints/*
+.idea/* 
+.DS_Store* 
+Archive
+*.pyc
+.~lock.*
+lm/libri-timit-lm.arpa
+lm/libri-timit-lm.klm
+lm/timit-lm.arpa
+lm/timit-lm.klm
+*/.ipynb_checkpoints
+
+kDS.sh
+kDS-LOCAL.sh
+
+data/*
+!enron*
+!e00*

Android/README.md

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+## Tensorflow Guide
+
+
+1. Download ALL of tensorflow (you need to compile TF for your mobile)
+`git clone --recurse-submodules  https://github.com/tensorflow/tensorflow.git`
+
+Important once downloaded change the branch to the one you are using for the model.
+e.g. `git checkout r1.1`
+
+2. Download Android SDK (comes with studio) and NDK:
+Link
+Link
+
+3. Install Bazel from here https://bazel.build/versions/master/docs/install.html
+
+
+4. Edit workspace file at the root of the tensorflow direction you grabbed from git
+
+Uncomment the lines about SDK/NDK
+
+android_sdk_repository
+android_ndk_repository
+
+and fill in the correct paths to those libraries.
+
+
+5. Build .so files
+
+`bazel build -c opt //tensorflow/contrib/android:libtensorflow_inference.so \
+   --crosstool_top=//external:android/crosstool \
+   --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
+   --cpu=armeabi-v7a`
+
+the file is bazel-bin/tensorflow/contrib/android/libtensorflow_inference.so make a note of this
+
+<!-- 6. Build java file -->
+
+These need to be copied into the TENSORFLOW APP
+
+
+
+
+
+

Android/convert_keras_to_android.py

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+# this file is based on https://github.com/amir-abdi/keras_to_tensorflow/
+
+import os
+import os.path as osp
+
+from keras import backend as K
+from keras.models import load_model
+
+import tensorflow as tf
+from tensorflow.python.framework import graph_util
+from tensorflow.python.framework import graph_io
+
+from utils import load_model_checkpoint, save_model
+
+# SET PARAMS
+
+input_fld = './checkpoints/trimmed/'
+model_file = 'TRIMMED_ds_model' #dont use extension e.g .json
+
+
+num_output = 1
+write_graph_def_ascii_flag = True
+prefix_output_node_names_of_final_network = 'output_node'
+output_graph_name = 'constant_graph_weights.pb'
+
+## INIT
+
+output_fld = "./Android/" + 'tensorflow_model/'
+if not os.path.isdir(output_fld):
+    os.mkdir(output_fld)
+
+
+## LOAD KERAS MODEL AND RENAME OUTPUT
+
+K.set_learning_phase(0)
+net_model = load_model_checkpoint(input_fld+model_file)
+
+pred = [None]*num_output
+pred_node_names = [None]*num_output
+for i in range(num_output):
+    pred_node_names[i] = prefix_output_node_names_of_final_network+str(i)
+    pred[i] = tf.identity(net_model.output[i], name=pred_node_names[i])
+print('output nodes names are: ', pred_node_names)
+
+
+# WHY [optional] write graph definition in asci??
+
+sess = K.get_session()
+if write_graph_def_ascii_flag:
+    f = 'only_the_graph_def.pb.ascii'
+    tf.train.write_graph(sess.graph.as_graph_def(), output_fld, f, as_text=True)
+    print('saved the graph definition in ascii format at: ', osp.join(output_fld, f))
+
+# convert variables to constants and save
+
+constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), pred_node_names)
+graph_io.write_graph(constant_graph, output_fld, output_graph_name, as_text=False)
+print('saved the constant graph (ready for inference) at: ', osp.join(output_fld, output_graph_name))
+
+
+##### safety check graph
+pbfile = osp.join(output_fld, output_graph_name)
+
+g = tf.GraphDef()
+g.ParseFromString(open(pbfile, "rb").read())
+print([n for n in g.node if n.name.find("input") != -1]) # same for output or any other node you want to make sure is ok
+print([n for n in g.node if n.name.find("out") != -1]) # same for output or any other node you want to make sure is ok
+
+#the_input
+#output_node0
+
+
+##weird ops might not be defualt
+ops = set([n.op for n in g.node])
+print(ops)
+
+
+

Android/tensorflow_model/comp_graph_label_strings.txt

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+_
+a
+b
+c
+d
+e
+f
+g
+h
+i
+j
+k
+l
+m
+n
+o
+p
+q
+r
+s
+t
+u
+v
+w
+x
+y
+z
+'