Quick, Draw! is a game where a player is challenged to draw a number of objects and see if a computer can recognize the drawing.
The recognition in Quick, Draw! is performed by a classifier that takes the user input, given as a sequence of strokes of points in x and y, and recognizes the object category that the user tried to draw.
In this tutorial we'll show how to build an RNNbased recognizer for this problem. The model will use a combination of convolutional layers, LSTM layers, and a softmax output layer to classify the drawings:
The figure above shows the structure of the model that we will build in this tutorial. The input is a drawing that is encoded as a sequence of strokes of points in x, y, and n, where n indicates whether a the point is the first point in a new stroke.
Then, a series of 1dimensional convolutions is applied. Then LSTM layers are applied and the sum of the outputs of all LSTM steps is fed into a softmax layer to make a classification decision among the classes of drawings that we know.
This tutorial uses the data from actual Quick, Draw! games that is publicly available. This dataset contains of 50M drawings in 345 categories.
Run the tutorial code
To try the code for this tutorial:
 Install TensorFlow if you haven't already.
 Download the tutorial code .

Download the data in
TFRecord
format from here and unzip it. More details about how to obtain the original Quick, Draw! data and how to convert that toTFRecord
files is available below. 
Execute the tutorial code with the following command to train the RNNbased model described in this tutorial. Make sure to adjust the paths to point to the unzipped data from the download in step 3.
python train_model.py \
training_data=rnn_tutorial_data/training.tfrecord?????of????? \
eval_data=rnn_tutorial_data/eval.tfrecord?????of????? \
classes_file=rnn_tutorial_data/training.tfrecord.classes
Tutorial details
Download the data
We make the data that we use in this tutorial available as TFRecord
files
containing TFExamples
. You can download the data from here:
http://download.tensorflow.org/data/quickdraw_tutorial_dataset_v1.tar.gz
Alternatively you can download the original data in ndjson
format from the
Google cloud and convert it to the TFRecord
files containing TFExamples
yourself as described in the next section.
Optional: Download the full Quick Draw Data
The full Quick, Draw! dataset is available on Google Cloud Storage as ndjson files separated by category. You can browse the list of files in Cloud Console.
To download the data we recommend using gsutil to download the entire dataset. Note that the original .ndjson files require downloading ~22GB.
Then use the following command to check that your gsutil installation works and that you can access the data bucket:
gsutil ls r "gs://quickdraw_dataset/full/simplified/*"
which will output a long list of files like the following:
gs://quickdraw_dataset/full/simplified/The Eiffel Tower.ndjson
gs://quickdraw_dataset/full/simplified/The Great Wall of China.ndjson
gs://quickdraw_dataset/full/simplified/The Mona Lisa.ndjson
gs://quickdraw_dataset/full/simplified/aircraft carrier.ndjson
...
Then create a folder and download the dataset there.
mkdir rnn_tutorial_data
cd rnn_tutorial_data
gsutil m cp "gs://quickdraw_dataset/full/simplified/*" .
This download will take a while and download a bit more than 23GB of data.
Optional: Converting the data
To convert the ndjson
files to
TFRecord files containing
${tf.train.Example} protos run the following command.
python create_dataset.py ndjson_path rnn_tutorial_data \
output_path rnn_tutorial_data
This will store the data in 10 shards of TFRecord files with 10000 items per class for the training data and 1000 items per class as eval data.
This conversion process is described in more detail in the following.
The original QuickDraw data is formatted as ndjson
files where each line
contains a JSON object like the following:
{"word":"cat",
"countrycode":"VE",
"timestamp":"20170302 23:25:10.07453 UTC",
"recognized":true,
"key_id":"5201136883597312",
"drawing":[
[
[130,113,99,109,76,64,55,48,48,51,59,86,133,154,170,203,214,217,215,208,186,176,162,157,132],
[72,40,27,79,82,88,100,120,134,152,165,184,189,186,179,152,131,114,100,89,76,0,31,65,70]
],[
[76,28,7],
[136,128,128]
],[
[76,23,0],
[160,164,175]
],[
[87,52,37],
[175,191,204]
],[
[174,220,246,251],
[134,132,136,139]
],[
[175,255],
[147,168]
],[
[171,208,215],
[164,198,210]
],[
[130,110,108,111,130,139,139,119],
[129,134,137,144,148,144,136,130]
],[
[107,106],
[96,113]
]
]
}
For our purpose of building a classifier we only care about the fields "word
"
and "drawing
". While parsing the ndjson files, we process them line by line
using a function that converts the strokes from the drawing
field into a
tensor of size [number of points, 3]
containing the differences of consecutive
points. This function also returns the class name as a string.
def parse_line(ndjson_line):
"""Parse an ndjson line and return ink (as np array) and classname."""
sample = json.loads(ndjson_line)
class_name = sample["word"]
inkarray = sample["drawing"]
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]
return np_ink, class_name
Since we want the data to be shuffled for writing we read from each of the category files in random order and write to a random shard.
For the training data we read the first 10000 items for each class and for the eval data we read the next 1000 items for each class.
This data is then reformatted into a tensor of shape [num_training_samples,
max_length, 3]
. Then we determine the bounding box of the original drawing in
screen coordinates and normalize the size such that the drawing has unit height.
Finally, we compute the differences between consecutive points and store these
as a VarLenFeature
in a
tensorflow.Example
under the key ink
. In addition we store the class_index
as a single entry
FixedLengthFeature
and the shape
of the ink
as a FixedLengthFeature
of
length 2.
Defining the model
To define the model we create a new Estimator
. If you want to read more about
estimators, we recommend this tutorial.
To build the model, we:

reshape the input back into the original shape  where the mini batch is padded to the maximal length of its contents. In addition to the ink data we also have the lengths for each example and the target class. This happens in the function
_get_input_tensors
. 
pass the input through to a series of convolution layers in
_add_conv_layers
. 
pass the output of the convolutions into a series of bidirectional LSTM layers in
_add_rnn_layers
. At the end of that, the outputs for each time step are summed up to have a compact, fixed length embedding of the input. 
classify this embedding using a softmax layer in
_add_fc_layers
.
In code this looks like:
inks, lengths, targets = _get_input_tensors(features, targets)
convolved = _add_conv_layers(inks)
final_state = _add_rnn_layers(convolved, lengths)
logits =_add_fc_layers(final_state)
_get_input_tensors
To obtain the input features we first obtain the shape from the features dict
and then create a 1D tensor of size [batch_size]
containing the lengths of the
input sequences. The ink is stored as a SparseTensor in the features dict which
we convert into a dense tensor and then reshape to be [batch_size, ?, 3]
. And
finally, if targets were passed in we make sure they are stored as a 1D tensor
of size [batch_size]
In code this looks like this:
shapes = features["shape"]
lengths = tf.squeeze(
tf.slice(shapes, begin=[0, 0], size=[params["batch_size"], 1]))
inks = tf.reshape(
tf.sparse_tensor_to_dense(features["ink"]),
[params["batch_size"], 1, 3])
if targets is not None:
targets = tf.squeeze(targets)
_add_conv_layers
The desired number of convolution layers and the lengths of the filters is
configured through the parameters num_conv
and conv_len
in the params
dict.
The input is a sequence where each point has dimensionality 3. We are going to
use 1D convolutions where we treat the 3 input features as channels. That means
that the input is a [batch_size, length, 3]
tensor and the output will be a
[batch_size, length, number_of_filters]
tensor.
convolved = inks
for i in range(len(params.num_conv)):
convolved_input = convolved
if params.batch_norm:
convolved_input = tf.layers.batch_normalization(
convolved_input,
training=(mode == tf.estimator.ModeKeys.TRAIN))
# Add dropout layer if enabled and not first convolution layer.
if i > 0 and params.dropout:
convolved_input = tf.layers.dropout(
convolved_input,
rate=params.dropout,
training=(mode == tf.estimator.ModeKeys.TRAIN))
convolved = tf.layers.conv1d(
convolved_input,
filters=params.num_conv[i],
kernel_size=params.conv_len[i],
activation=None,
strides=1,
padding="same",
name="conv1d_%d" % i)
return convolved, lengths
_add_rnn_layers
We pass the output from the convolutions into bidirectional LSTM layers for which we use a helper function from contrib.
outputs, _, _ = contrib_rnn.stack_bidirectional_dynamic_rnn(
cells_fw=[cell(params.num_nodes) for _ in range(params.num_layers)],
cells_bw=[cell(params.num_nodes) for _ in range(params.num_layers)],
inputs=convolved,
sequence_length=lengths,
dtype=tf.float32,
scope="rnn_classification")
see the code for more details and how to use CUDA
accelerated implementations.
To create a compact, fixedlength embedding, we sum up the output of the LSTMs. We first zero out the regions of the batch where the sequences have no data.
mask = tf.tile(
tf.expand_dims(tf.sequence_mask(lengths, tf.shape(outputs)[1]), 2),
[1, 1, tf.shape(outputs)[2]])
zero_outside = tf.where(mask, outputs, tf.zeros_like(outputs))
outputs = tf.reduce_sum(zero_outside, axis=1)
_add_fc_layers
The embedding of the input is passed into a fully connected layer which we then use as a softmax layer.
tf.layers.dense(final_state, params.num_classes)
Loss, predictions, and optimizer
Finally, we need to add a loss, a training op, and predictions to create the
ModelFn
:
cross_entropy = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=targets, logits=logits))
# Add the optimizer.
train_op = tf.contrib.layers.optimize_loss(
loss=cross_entropy,
global_step=tf.train.get_global_step(),
learning_rate=params.learning_rate,
optimizer="Adam",
# some gradient clipping stabilizes training in the beginning.
clip_gradients=params.gradient_clipping_norm,
summaries=["learning_rate", "loss", "gradients", "gradient_norm"])
predictions = tf.argmax(logits, axis=1)
return model_fn_lib.ModelFnOps(
mode=mode,
predictions={"logits": logits,
"predictions": predictions},
loss=cross_entropy,
train_op=train_op,
eval_metric_ops={"accuracy": tf.metrics.accuracy(targets, predictions)})
Training and evaluating the model
To train and evaluate the model we can rely on the functionalities of the
Estimator
APIs and easily run training and evaluation with the Experiment
APIs:
estimator = tf.estimator.Estimator(
model_fn=model_fn,
model_dir=output_dir,
config=config,
params=model_params)
# Train the model.
tf.contrib.learn.Experiment(
estimator=estimator,
train_input_fn=get_input_fn(
mode=tf.contrib.learn.ModeKeys.TRAIN,
tfrecord_pattern=FLAGS.training_data,
batch_size=FLAGS.batch_size),
train_steps=FLAGS.steps,
eval_input_fn=get_input_fn(
mode=tf.contrib.learn.ModeKeys.EVAL,
tfrecord_pattern=FLAGS.eval_data,
batch_size=FLAGS.batch_size),
min_eval_frequency=1000)
Note that this tutorial is just a quick example on a relatively small dataset to get you familiar with the APIs of recurrent neural networks and estimators. Such models can be even more powerful if you try them on a large dataset.
When training the model for 1M steps you can expect to get an accuracy of approximately of approximately 70% on the top1 candidate. Note that this accuracy is sufficient to build the quickdraw game because of the game dynamics the user will be able to adjust their drawing until it is ready. Also, the game does not use the top1 candidate only but accepts a drawing as correct if the target category shows up with a score better than a fixed threshold.