def encoded_to_compressed_tensor(encoded):
"""
Transform a binary encoded string image to its compressed tensor.
Parameters
----------
encoded: bytes
The binary representation of the image
Returns
-------
numpy.ndarray
"""
return io_ops.decode_raw(encoded, 'uint8')
def decode(self, serialized_example):
""" Load final output to neural network training procedure
:param serialized_example: compressed example recorded
on TFRecordWriter
:return: image, label as one hot encoder
"""
features = io.parse_single_example(serialized_example,
self.get_template())
image = io.decode_raw(features['image'], 'uint8')
image = reshape(image, self.shape + (3, ))
image = cast(image, dtype='float32') / 255
return image, one_hot(features['label'], self.labels)
def bytes_sequence_to_encoding_bilstm(feature_tensor, feature_info, file_io: FileIO):
"""
Encode a string tensor into an encoding.
Works by converting the string into a bytes sequence and then generating
a categorical/char embedding for each of the 256 bytes. The char/byte embeddings
are then combined using a biLSTM
Args:
feature_tensor: String feature tensor that is to be encoded
feature_info: Dictionary representing the feature_config for the input feature
Returns:
Encoded feature tensor
Args under feature_layer_info:
max_length: int; max length of bytes sequence
embedding_size: int; dimension size of the embedding;
if null, then the tensor is just converted to its one-hot representation
encoding_size: int: dimension size of the sequence encoding computed using a biLSTM
NOTE:
The input dimension for the embedding is fixed to 256 because the string is
converted into a bytes sequence.
"""
args = feature_info["feature_layer_info"]["args"]
# Decode string tensor to bytes
feature_tensor = io.decode_raw(
feature_tensor, out_type=tf.uint8, fixed_length=args.get("max_length", None),
)
feature_tensor = tf.squeeze(feature_tensor, axis=1)
if "embedding_size" in args:
char_embedding = layers.Embedding(
name="{}_bytes_embedding".format(
feature_info.get("node_name", feature_info.get("name"))
),
input_dim=256,
output_dim=args["embedding_size"],
mask_zero=True,
input_length=args.get("max_length", None),
)(feature_tensor)
else:
char_embedding = tf.one_hot(feature_tensor, depth=256)
encoding = get_bilstm_encoding(char_embedding, int(args["encoding_size"] / 2))
return encoding
def __init__(self, observation_size, net_arch, initializer, activation,
clip_range, value_coef, entropy_coef, learning_rate,
pre_training_learning_rate, action_bounds, policy):
"""
:param observation_size:
:param net_arch:
:param initializer:
:param activation:
:param clip_range:
:param value_coef:
:param entropy_coef:
:param learning_rate:
:param pre_training_learning_rate:
:param action_bounds:
:param policy:
"""
"""Set class constants"""
self.observation_size = observation_size
self.net_arch = net_arch
self.initializer = initializer
self.activation = activation
self.clip_range = clip_range
self.value_coef = value_coef
self.entropy_coef = entropy_coef
if action_bounds is None:
action_bounds = [0.0, 1.5]
self.action_bounds = action_bounds
self.learning_rate = learning_rate
self.pre_training_learning_rate = pre_training_learning_rate
if policy is None:
policy = GaussFull()
self.policy = policy
"""Set up the tensorflow graph"""
self.graph = Graph()
with self.graph.as_default():
self.sess = Session(graph=self.graph)
""" core """
# place holders
self.observation_string_ph = placeholder(
shape=(None, 1), dtype=string, name="observation_string_ph")
self.action_ph = placeholder(dtype=float32,
shape=(None, 1),
name="action_ph")
self.old_neg_logits = placeholder(dtype=float32,
shape=(None, 1),
name="old_neg_logits")
self.advantage_ph = placeholder(dtype=float32,
shape=(None, 1),
name="advantage_ph")
self.value_target_ph = placeholder(dtype=float32,
shape=(None, 1),
name="value_target_ph")
# learning rate tensors
self.learning_rate_ph = placeholder_with_default(
input=self.learning_rate, shape=())
self.pre_training_learning_rate_ph = placeholder_with_default(
input=self.pre_training_learning_rate, shape=())
# observation tensor
replaced1 = regex_replace(self.observation_string_ph, "/", "_")
replaced2 = regex_replace(replaced1, r"\+", "-")
byte_tensor = decode_base64(replaced2)
decoded = decode_raw(byte_tensor, out_type=float32)
squeezed = squeeze(decoded, axis=1)
self.observation_input = ensure_shape(
squeezed,
shape=(None, self.observation_size),
name="observation_input")
# policy net
latent_policy = net_core(self.observation_input, self.net_arch,
self.initializer, self.activation)
self.policy.construct(latent_policy=latent_policy)
self.clipped_action = clip_by_value(
cast(self.policy.action, float32), self.action_bounds[0],
self.action_bounds[1], "clipped_action")
# value net
latent_value = net_core(self.observation_input, self.net_arch,
self.initializer, self.activation)
self.value = identity(
input=Dense(units=1,
activation=None,
kernel_initializer=self.initializer)(latent_value),
name="value")
"""loss calculation"""
# policy loss
self.neg_logits = self.policy.neg_logits_from_actions(
self.action_ph)
ratio = exp(self.old_neg_logits - self.neg_logits)
standardized_adv = (self.advantage_ph - reduce_mean(
self.advantage_ph)) / (reduce_std(self.advantage_ph) + 1e-8)
raw_policy_loss = -standardized_adv * ratio
clipped_policy_loss = -standardized_adv * clip_by_value(
ratio, 1 - self.clip_range, 1 + self.clip_range)
self.policy_loss = reduce_mean(
maximum(raw_policy_loss, clipped_policy_loss))
self.value_loss = mean_squared_error(self.value_target_ph,
self.value)
# entropy loss
self.entropy_loss = -reduce_mean(self.policy.entropy)
# total loss
self.total_loss = self.policy_loss + self.value_coef * self.value_loss + self.entropy_coef * self.entropy_loss
# optimizer
optimizer = AdamOptimizer(learning_rate=self.learning_rate_ph)
# training ops
self.training_op = optimizer.minimize(self.total_loss)
# pre training
self.dist_param_target_ph = placeholder(
dtype=float32,
shape=(None, self.policy.dist_params.shape[1]),
name="dist_param_label_ph")
self.pre_training_loss = mean_squared_error(
self.dist_param_target_ph, self.policy.dist_params)
pre_training_optimizer = GradientDescentOptimizer(
learning_rate=self.pre_training_learning_rate_ph)
self.pre_training_op = pre_training_optimizer.minimize(
self.pre_training_loss)
"""utility nodes"""
# inspect model weights
self.trainable_variables = trainable_variables()
# saviour
self.saver = Saver()
# tensorboard summaries
self.summary = merge([
histogram("values", self.value),
histogram("advantages", standardized_adv),
histogram("actions", self.clipped_action),
histogram("det_actions",
replace_nan(self.policy.det_action, 0.0)),
histogram("value_targets", self.value_target_ph),
scalar("policy_loss", self.policy_loss),
scalar("value_loss", self.value_loss),
scalar("entropy_loss", self.entropy_loss)
])
self.pre_summary = merge([
histogram("pretraining_actions", self.clipped_action),
scalar("pretraining_loss", self.pre_training_loss)
])
# initialization
init = global_variables_initializer()
self.sess.run(init)
def bytes_sequence_to_encoding_bilstm(feature_tensor, feature_info,
file_io: FileIO):
"""
Encode a string tensor into an encoding.
Works by converting the string into a bytes sequence and then generating
a categorical/char embedding for each of the 256 bytes. The char/byte embeddings
are then combined using a biLSTM
Parameters
----------
feature_tensor : Tensor object
String feature tensor that is to be encoded
feature_info : dict
Dictionary representing the feature_config for the input feature
file_io : FileIO object
FileIO handler object for reading and writing
Returns
-------
Tensor object
Encoded feature tensor
Notes
-----
Args under `feature_layer_info`:
max_length : int
max length of bytes sequence
embedding_size : int
dimension size of the embedding;
if null, then the tensor is just converted to its one-hot representation
encoding_size : int
dimension size of the sequence encoding computed using a biLSTM
The input dimension for the embedding is fixed to 256 because the string is
converted into a bytes sequence.
"""
args = feature_info["feature_layer_info"]["args"]
# Decode string tensor to bytes
feature_tensor = io.decode_raw(
feature_tensor,
out_type=tf.uint8,
fixed_length=args.get("max_length", None),
)
feature_tensor = tf.squeeze(feature_tensor, axis=1)
if "embedding_size" in args:
char_embedding = layers.Embedding(
name="{}_bytes_embedding".format(
feature_info.get("node_name", feature_info.get("name"))),
input_dim=256,
output_dim=args["embedding_size"],
mask_zero=True,
input_length=args.get("max_length", None),
)(feature_tensor)
else:
char_embedding = tf.one_hot(feature_tensor, depth=256)
kernel_initializer = args.get("lstm_kernel_initializer", "glorot_uniform")
encoding = get_bilstm_encoding(
embedding=char_embedding,
lstm_units=int(args["encoding_size"] / 2),
kernel_initializer=kernel_initializer,
)
return encoding
def _parse_sequence_example_fn(sequence_example_proto):
"""
Parse the input `tf.Example` proto using the features_spec
Args:
sequence_example_proto: tfrecord SequenceExample protobuf data
Returns:
features: parsed features extracted from the protobuf
labels: parsed label extracted from the protobuf
"""
context_features, sequence_features = io.parse_single_sequence_example(
serialized=sequence_example_proto,
context_features=context_features_spec,
sequence_features=sequence_features_spec,
)
features_dict = dict()
# Explode context features into all records
for feature_info in feature_config.get_context_features():
feature_node_name = feature_info.get("node_name",
feature_info["name"])
feature_layer_info = feature_info.get("feature_layer_info")
feature_tensor = context_features.get(feature_node_name)
feature_tensor = tf.expand_dims(feature_tensor, axis=0)
feature_tensor = tf.tile(feature_tensor,
multiples=[max_num_records])
# If feature is a string, then decode into numbers
if feature_layer_info["type"] == FeatureTypeKey.STRING:
feature_tensor = io.decode_raw(
feature_tensor,
out_type=tf.uint8,
fixed_length=feature_layer_info["max_length"],
)
feature_tensor = tf.cast(feature_tensor, tf.float32)
features_dict[feature_node_name] = feature_tensor
# Pad sequence features to max_num_records
for feature_info in feature_config.get_sequence_features():
feature_node_name = feature_info.get("node_name",
feature_info["name"])
feature_layer_info = feature_info["feature_layer_info"]
feature_tensor = sequence_features.get(feature_node_name)
if isinstance(feature_tensor, sparse.SparseTensor):
if feature_node_name == feature_config.get_rank(
key="node_name"):
# Add mask for identifying padded records
mask = tf.ones_like(
sparse.to_dense(sparse.reset_shape(feature_tensor)))
mask = tf.expand_dims(mask, axis=2)
def crop_fn():
tf.print(
"\n[WARN] Bad query found. Number of records : ",
tf.shape(mask)[1])
return image.crop_to_bounding_box(
mask,
offset_height=0,
offset_width=0,
target_height=1,
target_width=max_num_records,
)
mask = tf.cond(
tf.shape(mask)[1] < max_num_records,
# Pad if there are missing records
lambda: image.pad_to_bounding_box(
mask,
offset_height=0,
offset_width=0,
target_height=1,
target_width=max_num_records,
),
# Crop if there are extra records
crop_fn,
)
mask = tf.squeeze(mask)
# Check validity of mask
tf.debugging.assert_greater(
tf.cast(tf.reduce_sum(mask), tf.float32),
tf.constant(0.0))
features_dict["mask"] = mask
feature_tensor = sparse.reset_shape(
feature_tensor, new_shape=[1, max_num_records])
feature_tensor = sparse.to_dense(feature_tensor)
feature_tensor = tf.squeeze(feature_tensor)
# If feature is a string, then decode into numbers
if feature_layer_info["type"] == FeatureTypeKey.STRING:
feature_tensor = io.decode_raw(
feature_tensor,
out_type=tf.uint8,
fixed_length=feature_layer_info["max_length"],
)
feature_tensor = tf.cast(feature_tensor, tf.float32)
else:
raise ValueError("Invalid input : {}".format(feature_name))
features_dict[feature_node_name] = feature_tensor
labels = features_dict.pop(feature_config.get_label(key="name"))
# Check if label is one-hot and correctly masked
tf.debugging.assert_equal(tf.cast(tf.reduce_sum(labels), tf.float32),
tf.constant(1.0))
return features_dict, labels
def feature_layer(inputs):
train_features = list()
metadata_features = dict()
numeric_tile_shape = tf.shape(
tf.expand_dims(tf.gather(inputs["mask"], indices=0), axis=0))
for feature_info in feature_config.get_all_features(
include_label=False):
feature_name = feature_info["name"]
feature_node_name = feature_info.get("node_name", feature_name)
feature_layer_info = feature_info["feature_layer_info"]
if feature_layer_info["type"] == FeatureTypeKey.NUMERIC:
# Numeric input features
if feature_info["dtype"] in (tf.float32, tf.int64):
dense_feature = inputs[feature_node_name]
if feature_info[
"tfrecord_type"] == SequenceExampleTypeKey.CONTEXT:
dense_feature = tf.tile(dense_feature,
numeric_tile_shape)
if "fn" in feature_layer_info:
dense_feature = feature_layer_map.get_fn(
feature_layer_info["fn"])(dense_feature,
feature_info)
elif feature_info["trainable"]:
dense_feature = tf.expand_dims(tf.cast(
dense_feature, tf.float32),
axis=-1)
if feature_info["trainable"]:
train_features.append(dense_feature)
else:
metadata_features[feature_node_name] = tf.cast(
dense_feature, tf.float32)
# String input features
elif feature_info["dtype"] in (tf.string, ):
if feature_info["trainable"]:
decoded_string_tensor = io.decode_raw(
inputs[feature_node_name],
out_type=tf.uint8,
fixed_length=feature_layer_info["args"]
["max_length"],
)
if "fn" in feature_layer_info:
dense_feature = feature_layer_map.get_fn(
feature_layer_info["fn"])(
decoded_string_tensor, feature_info)
"""
Creating a tensor [1, sequence_size, 1] dynamically
NOTE:
Tried multiple methods using `convert_to_tensor`, `concat`, with no results
"""
if feature_info[
"tfrecord_type"] == SequenceExampleTypeKey.CONTEXT:
tile_dims = tf.shape(
tf.expand_dims(
tf.expand_dims(tf.gather(inputs["mask"],
indices=0),
axis=0),
axis=-1,
))
dense_feature = tf.tile(dense_feature, tile_dims)
train_features.append(dense_feature)
elif feature_layer_info["type"] == FeatureTypeKey.STRING:
if feature_info["trainable"]:
raise ValueError(
"Can not train on string tensors directly. Please use a feature layer"
)
else:
metadata_features[feature_node_name] = inputs[
feature_node_name]
elif feature_layer_info["type"] == FeatureTypeKey.CATEGORICAL:
if feature_info["trainable"]:
if "fn" in feature_layer_info:
dense_feature = feature_layer_map.get_fn(
feature_layer_info["fn"])(
inputs[feature_node_name], feature_info)
if feature_info[
"tfrecord_type"] == SequenceExampleTypeKey.CONTEXT:
tile_dims = tf.shape(
tf.expand_dims(
tf.expand_dims(tf.gather(inputs["mask"],
indices=0),
axis=0),
axis=-1,
))
dense_feature = tf.tile(dense_feature, tile_dims)
train_features.append(dense_feature)
else:
raise NotImplementedError
else:
raise Exception(
"Unknown feature type {} for feature : {}".format(
feature_layer_info["type"], feature_name))
return train_features, metadata_features
def _parse_sequence_example_fn(sequence_example_proto):
"""
Parse the input `tf.Example` proto using the features_spec
Args:
sequence_example_proto: tfrecord SequenceExample protobuf data
Returns:
TODO(ashish): note - "features" is not a Features object. It's a {feat_name: tf.Tensor} mapping
(so perhaps a bad name?)
features: parsed features extracted from the protobuf
labels: parsed label extracted from the protobuf
"""
context, examples = io.parse_single_sequence_example(
serialized=sequence_example_proto,
context_features=context_features_spec,
sequence_features=sequence_features_spec,
)
features = dict()
# Explode context features into all records
for feat, t in context.items():
t = tf.expand_dims(t, axis=0)
t = tf.tile(t, multiples=[max_num_records])
# If feature is a string, then decode into numbers
if feature_config.get_dict(
)[feat]["type"] == FeatureTypeKey.STRING:
t = io.decode_raw(
t,
out_type=tf.uint8,
fixed_length=feature_config.get_dict()[feat]["max_length"],
)
t = tf.cast(t, tf.float32)
features[feat] = t
# Pad sequence features to max_num_records
for feat, t in examples.items():
if isinstance(t, sparse.SparseTensor):
if feat == "pos":
# Add mask for identifying padded records
mask = tf.ones_like(sparse.to_dense(sparse.reset_shape(t)))
mask = tf.expand_dims(mask, axis=2)
mask = image.pad_to_bounding_box(
mask,
offset_height=0,
offset_width=0,
target_height=1,
target_width=max_num_records,
)
features["mask"] = tf.squeeze(mask)
t = sparse.reset_shape(t, new_shape=[1, max_num_records])
t = sparse.to_dense(t)
t = tf.squeeze(t)
# If feature is a string, then decode into numbers
if feature_config.get_dict(
)[feat]["type"] == FeatureTypeKey.STRING:
t = io.decode_raw(
t,
out_type=tf.uint8,
fixed_length=feature_config.get_dict()[feat]
["max_length"],
)
t = tf.cast(t, tf.float32)
else:
#
# Handle dense tensors
#
# if len(t.shape) == 1:
# t = tf.expand_dims(t, axis=0)
# if len(t.shape) == 2:
# t = tf.pad(t, paddings=[[0, 0], [0, max_num_records]])
# t = tf.squeeze(t)
# else:
# raise Exception('Invalid input : {}'.format(feat))
raise ValueError("Invalid input : {}".format(feat))
features[feat] = t
labels = features.pop(feature_config.label)
return features, labels