Tensorflow : Predict in Recurrent Neural Networks for Drawing Classification tutorial - tensorflow

Question

I used the tutorial code from https://www.tensorflow.org/tutorials/recurrent_quickdraw and all works fine until I tried to make a prediction instead of just evaluate it.
I wrote a new input function for prediction, based on the code in create_dataset.py
def predict_input_fn():
def parse_line(stroke_points):
"""Parse an ndjson line and return ink (as np array) and classname."""
inkarray = json.loads(stroke_points)
stroke_lengths = [len(stroke[0]) for stroke in inkarray]
total_points = sum(stroke_lengths)
np_ink = np.zeros((total_points, 3), dtype=np.float32)
current_t = 0
for stroke in inkarray:
for i in [0, 1]:
np_ink[current_t:(current_t + len(stroke[0])), i] = stroke[i]
current_t += len(stroke[0])
np_ink[current_t - 1, 2] = 1 # stroke_end
# Preprocessing.
# 1. Size normalization.
lower = np.min(np_ink[:, 0:2], axis=0)
upper = np.max(np_ink[:, 0:2], axis=0)
scale = upper - lower
scale[scale == 0] = 1
np_ink[:, 0:2] = (np_ink[:, 0:2] - lower) / scale
# 2. Compute deltas.
np_ink = np_ink[1:, 0:2] - np_ink[0:-1, 0:2]
np_ink = np_ink[1:, :]
features = {}
features["ink"] = tf.train.Feature(float_list=tf.train.FloatList(value=np_ink.flatten()))
features["shape"] = tf.train.Feature(int64_list=tf.train.Int64List(value=np_ink.shape))
f = tf.train.Features(feature=features)
example = tf.train.Example(features=f)
#t = tf.constant(np_ink)
return example
def parse_example(example):
"""Parse a single record which is expected to be a tensorflow.Example."""
# feature_to_type = {
# "ink": tf.VarLenFeature(dtype=tf.float32),
# "shape": tf.FixedLenFeature((0,2), dtype=tf.int64)
# }
feature_to_type = {
"ink": tf.VarLenFeature(dtype=tf.float32),
"shape": tf.FixedLenFeature([2], dtype=tf.int64)
}
example_proto = example.SerializeToString()
parsed_features = tf.parse_single_example(example_proto, feature_to_type)
parsed_features["ink"] = tf.sparse_tensor_to_dense(parsed_features["ink"])
#parsed_features["shape"].set_shape((2))
return parsed_features
example = parse_line(FLAGS.predict_input_stroke_data)
features = parse_example(example)
dataset = tf.data.Dataset.from_tensor_slices(features)
# Our inputs are variable length, so pad them.
dataset = dataset.padded_batch(FLAGS.batch_size, padded_shapes=dataset.output_shapes)
iterator = dataset.make_one_shot_iterator()
next_feature_batch = iterator.get_next()
return next_feature_batch, None # In prediction, we have no labels
I modified the existing model_fn() function and added below at appropirate place
predictions = tf.argmax(logits, axis=1)
if mode == tf.estimator.ModeKeys.PREDICT:
preds = {
"class_index": predictions,
"probabilities": tf.nn.softmax(logits),
'logits': logits
}
return tf.estimator.EstimatorSpec(mode, predictions=preds)
However when i call the following the code
if (FLAGS.predict_input_stroke_data != None):
# prepare_input_tfrecord_for_prediction()
# predict_results = estimator.predict(input_fn=get_input_fn(
# mode=tf.estimator.ModeKeys.PREDICT,
# tfrecord_pattern=FLAGS.predict_input_temp_file,
# batch_size=FLAGS.batch_size))
predict_results = estimator.predict(input_fn=predict_input_fn)
for idx, prediction in enumerate(predict_results):
type = prediction["class_ids"][0] # Get the predicted class (index)
print("Prediction Type: {}\n".format(type))
I get the following error, what is wrong in my code could anyone please help me. I have tried quite a few things to get the shape right but i am unable to. I also tried to first write my strokes data as a tfrecord and then use the existing input_fn to read from the tfrecord that gives me similar errors but slighly different
File "/Users/farooq/.virtualenvs/tensor1.0/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py", line 627, in call_cpp_shape_fn
require_shape_fn)
File "/Users/farooq/.virtualenvs/tensor1.0/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py", line 691, in _call_cpp_shape_fn_impl
raise ValueError(err.message)
ValueError: Shape must be rank 2 but is rank 1 for 'Slice' (op: 'Slice') with input shapes: [?], [2], [2].

Answer 1

I finally solved the problem by taking my input keystrokes, writing them to disk as a TFRecord. I also had to write the same inputstrokes batch_size times to same TFRecord, else i got the shape mismatch errors. And then invoking predict worked.
The main addition for prediction was the following function
def create_tfrecord_for_prediction(batch_size, stoke_data, tfrecord_file):
def parse_line(stoke_data):
"""Parse provided stroke data and ink (as np array) and classname."""
inkarray = json.loads(stoke_data)
stroke_lengths = [len(stroke[0]) for stroke in inkarray]
total_points = sum(stroke_lengths)
np_ink = np.zeros((total_points, 3), dtype=np.float32)
current_t = 0
for stroke in inkarray:
if len(stroke[0]) != len(stroke[1]):
print("Inconsistent number of x and y coordinates.")
return None
for i in [0, 1]:
np_ink[current_t:(current_t + len(stroke[0])), i] = stroke[i]
current_t += len(stroke[0])
np_ink[current_t - 1, 2] = 1 # stroke_end
# Preprocessing.
# 1. Size normalization.
lower = np.min(np_ink[:, 0:2], axis=0)
upper = np.max(np_ink[:, 0:2], axis=0)
scale = upper - lower
scale[scale == 0] = 1
np_ink[:, 0:2] = (np_ink[:, 0:2] - lower) / scale
# 2. Compute deltas.
#np_ink = np_ink[1:, 0:2] - np_ink[0:-1, 0:2]
#np_ink = np_ink[1:, :]
np_ink[1:, 0:2] -= np_ink[0:-1, 0:2]
np_ink = np_ink[1:, :]
features = {}
features["ink"] = tf.train.Feature(float_list=tf.train.FloatList(value=np_ink.flatten()))
features["shape"] = tf.train.Feature(int64_list=tf.train.Int64List(value=np_ink.shape))
f = tf.train.Features(feature=features)
ex = tf.train.Example(features=f)
return ex
if stoke_data is None:
print("Error: Stroke data cannot be none")
return
example = parse_line(stoke_data)
#Remove the file if it already exists
if tf.gfile.Exists(tfrecord_file):
tf.gfile.Remove(tfrecord_file)
writer = tf.python_io.TFRecordWriter(tfrecord_file)
for i in range(batch_size):
writer.write(example.SerializeToString())
writer.flush()
writer.close()
Then in the main function you just have to invoke estimator.predict() reusing the same input_fn=get_input_fn(...)argument except point it to the temporary created tfrecord_file
Hope this helps