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# This script exports the learned parameters so that we can use them from Metal.
# Note: Dor this simple demo project the weight matrix is only 20 values and the bias
# is a single number. With such a simple model you might as well stick the parameters
# inside a static array in the iOS app source code. In practice, however, most models
# will have millions of parameters.
import os
import numpy as np
import tensorflow as tf
from sklearn import metrics
checkpoint_dir = "/tmp/voice/"
with tf.Session() as sess:
# Load the graph.
graph_file = os.path.join(checkpoint_dir, "graph.pb")
with tf.gfile.FastGFile(graph_file, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name="")
# Get the model's variables.
W = sess.graph.get_tensor_by_name("model/W:0")
b = sess.graph.get_tensor_by_name("model/b:0")
# Load the saved variables from the checkpoint back into the session.
checkpoint_file = os.path.join(checkpoint_dir, "model")
saver = tf.train.Saver([W, b])
saver.restore(sess, checkpoint_file)
# Just for debugging, print out the learned parameters.
print("W:", W.eval())
print("b:", b.eval())
# Export the contents of W and b as binary files.
W.eval().tofile("W.bin")
b.eval().tofile("b.bin")