Prior to tf 0.12.0rc I've used summary placeholders of the form:
tag_ph = tf.placeholder(tf.string)
val_ph = tf.placeholder(tf.float)
sum_op = tf.scalar_summary(tag_ph, val_ph)
...
feed_dict = {tag_ph:[some string], val_ph:[some val]}
sum_str = sess.run(sum_op, feed_dict)
writer.add_summary(sum_str)
After upgrading to 0.12.0 and changing tf.scalar_summary() to tf.summary.scalar() the use of a placeholder for the name parameter gives the following error:
TypeError: expected string or bytes-like object
There is no error if I use a static string for name, but I'd like to change the string as the evaluation progresses. How can I do that?
Minimal example:
tag = 'test'
val = 1.234
tag_ph = tf.placeholder(tf.string, [])
val_ph = tf.placeholder(tf.float32, [])
scalar_op = tf.summary.scalar(tag_ph, val_ph)
with tf.Session() as sess:
writer = tf.summary.FileWriter('/tmp/summary_placeholders', sess.graph)
feed_dict = {tag_ph:tag, val_ph:val}
sum_str = sess.run(scalar_op, feed_dict)
writer.add_summary(sum_str)
writer.flush()
This same code (after reverting tf.summary names) works in TF 0.11.0
If the question is how to write non-Tensorflow data as summaries in version >=0.12, here's an example:
import tensorflow as tf
summary_writer = tf.summary.FileWriter('custom_summaries')
summary = tf.Summary()
mydata = {"a": 1, "b": 2}
for name, data in mydata.items():
summary.value.add(tag=name, simple_value=data)
summary_writer.add_summary(summary, global_step=1)
summary_writer.flush()
TensorBoard merges summaries from all files in logdir and displays them, ie, if you do tensorboard --logdir=. you'll see something like this
Related
I have managed this implementation on retraining frozen graph in tensorflow 1 according to this wonderful detail topic. Basically, the methodology is described:
Load frozen model
Replace the constant frozen node with variable node.
The newly replaced variable node then will be redirected to the corresponding output of the frozen node.
This works in tensorflow 1.x by checking the tf.compat.v1.trainable_variables. However, in tensorflow 2.x, it can't work anymore.
Below is the code snippet:
1/ Load frozen model
frozen_path = '...'
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.compat.v1.GraphDef()
with tf.compat.v1.io.gfile.GFile(frozen_path, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.graph_util.import_graph_def(od_graph_def, name='')
2/ Create a clone
with detection_graph.as_default():
const_var_name_pairs = {}
probable_variables = [op for op in detection_graph.get_operations() if op.type == "Const"]
available_names = [op.name for op in detection_graph.get_operations()]
for op in probable_variables:
name = op.name
if name+'/read' not in available_names:
continue
tensor = detection_graph.get_tensor_by_name('{}:0'.format(name))
with tf.compat.v1.Session() as s:
tensor_as_numpy_array = s.run(tensor)
var_shape = tensor.get_shape()
# Give each variable a name that doesn't already exist in the graph
var_name = '{}_turned_var'.format(name)
var = tf.Variable(name=var_name, dtype=op.outputs[0].dtype, initial_value=tensor_as_numpy_array,trainable=True, shape=var_shape)
const_var_name_pairs[name] = var_name
3/ Relace frozen node by Graph Editor
import graph_def_editor as ge
ge_graph = ge.Graph(detection_graph.as_graph_def())
name_to_op = dict([(n.name, n) for n in ge_graph.nodes])
for const_name, var_name in const_var_name_pairs.items():
const_op = name_to_op[const_name+'/read']
var_reader_op = name_to_op[var_name + '/Read/ReadVariableOp']
ge.swap_outputs(ge.sgv(const_op), ge.sgv(var_reader_op))
detection_training_graph = ge_graph.to_tf_graph()
with detection_training_graph.as_default():
writer = tf.compat.v1.summary.FileWriter('remap', detection_training_graph )
writer.close
The problem was my Graph Editor when I import the tf.graph_def instead of the original tf.graph that has Variables.
Quickly solve by fixing step 3
Sol1: Using Graph Editor
ge_graph = ge.Graph(detection_graph)
for const_name, var_name in const_var_name_pairs.items():
const_op = ge_graph._node_name_to_node[const_name+'/read']
var_reader_op = ge_graph._node_name_to_node[var_name+'/Read/ReadVariableOp']
ge.swap_outputs(ge.sgv(const_op), ge.sgv(var_reader_op))
However, this requires disable eager execution. To work around with eager execution, you should attach the MetaGraphDef to Graph Editor as below
with detection_graph.as_default():
meta_saver = tf.compat.v1.train.Saver()
meta = meta_saver.export_meta_graph()
ge_graph = ge.Graph(detection_graph,collections=ge.graph._extract_collection_defs(meta))
However, this is the trickest to make the model trainable in tf2.x
Instead of using Graph Editor to export directly the graph, we should export ourselves. The reason is that the Graph Editor make the Variables data type to be resources. Therefore, we should export the graph as graphdef and import the variable def to the graph:
test_graph = tf.Graph()
with test_graph.as_default():
tf.import_graph_def(ge_graph.to_graph_def(), name="")
for var_name in ge_graph.variable_names:
var = ge_graph.get_variable_by_name(var_name)
ret = variable_pb2.VariableDef()
ret.variable_name = var._variable_name
ret.initial_value_name = var._initial_value_name
ret.initializer_name = var._initializer_name
ret.snapshot_name = var._snapshot_name
ret.trainable = var._trainable
ret.is_resource = True
tf_var = tf.Variable(variable_def=ret,dtype=tf.float32)
test_graph.add_to_collections(var.collection_names, tf_var)
Sol2: Manually map by Graphdef
with detection_graph.as_default() as graph:
training_graph_def = remap_input_node(detection_graph.as_graph_def(),const_var_name_pairs)
current_var = (tf.compat.v1.trainable_variables())
assert len(current_var)>0, "no training variables"
detection_training_graph = tf.Graph()
with detection_training_graph.as_default():
tf.graph_util.import_graph_def(training_graph_def, name='')
for var in current_var:
ret = variable_pb2.VariableDef()
ret.variable_name = var.name
ret.initial_value_name = var.name[:-2] + '/Initializer/initial_value:0'
ret.initializer_name = var.name[:-2] + '/Assign'
ret.snapshot_name = var.name[:-2] + '/Read/ReadVariableOp:0'
ret.trainable = True
ret.is_resource = True
tf_var = tf.Variable(variable_def=ret,dtype=tf.float32)
detection_training_graph.add_to_collections({'trainable_variables', 'variables'}, tf_var)
current_var = (tf.compat.v1.trainable_variables())
assert len(current_var)>0, "no training variables"
I am dealing with an issue in my conversion over to the Dataset API and I guess I just don't have enough experience yet with the API to know how to handle the below situation. We currently have image augmentation that we perform currently using queueing and batching. I was tasked with checking out the new Dataset API and converting over our existing implementation using it rather than queues.
What we would like to do is get a reference to all the paths and handle all operations from just that reference. As you see in the dataset initialization, I have mapped the parse_fn to the dataset itself which then goes about reading the file and extracting the initial values from the filenames. However when I then go about calling the iterators next_batch method and then pass those values to get_summary, I'm now getting an error around shape. I have been trying a number of things which just keeps changing the error and so I felt I should see if anyone on SO saw possibly that I was going about this all wrong and should be taking a different route. Does anything jump out as absolutely wrong in my use of the Dataset API?
Should I not be calling the ops this way any longer? I noticed the majority of the examples I saw they would get the batch, pass the variables to the op and then capture that in a variable and pass that to sess.run, however I haven't found an easy way of doing that as of yet with our setup that wasn't erroring so this was the approach I took instead (but its still erroring). I'll be continuing to try to trace down the problem and post here should I find anything, but if anyone sees something please advise. Thanks!
Current Error:
... in get_summary summary, acc = sess.run([self._summary_op,
self._accuracy], feed_dict=feed_dict) ValueError: Cannot feed value of
shape (32,) for Tensor 'ph_input_labels:0', which has shape '(?, 1)
Below is the block where the get_summary method is called and error is fired:
def perform_train():
if __name__ == '__main__':
#Get all our image paths
filenames = data_layer_train.get_image_paths()
next_batch, iterator = preproc_image_fn(filenames=filenames)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
with sess.graph.as_default():
# Set the random seed for tensorflow
tf.set_random_seed(cfg.RNG_SEED)
classifier_network = c_common.create_model(len(products_to_class_dict), is_training=True)
optimizer, global_step_var = c_common.create_optimizer(classifier_network)
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
# Init tables and dataset iterator
sess.run(tf.tables_initializer())
sess.run(iterator.initializer)
cur_epoch = 0
blobs = None
try:
epoch_size = data_layer_train.get_steps_per_epoch()
num_steps = num_epochs * epoch_size
for step in range(num_steps):
timer_summary.tic()
if blobs is None:
#Now populate from our training dataset
blobs = sess.run(next_batch)
# *************** Below is where it is erroring *****************
summary_train, acc = classifier_network.get_summary(sess, blobs["images"], blobs["labels"], blobs["weights"])
...
Believe the error is in preproc_image_fn:
def preproc_image_fn(filenames, images=None, labels=None, image_paths=None, cells=None, weights=None):
def _parse_fn(filename, label, weight):
augment_instance = False
paths=[]
selected_cells=[]
if vals.FIRST_ITER:
#Perform our check of the path to see if _data_augmentation is within it
#If so set augment_instance to true and replace the substring with an empty string
new_filename = tf.regex_replace(filename, "_data_augmentation", "")
contains = tf.equal(tf.size(tf.string_split([filename], "")), tf.size(tf.string_split([new_filename])))
filename = new_filename
if contains is True:
augment_instance = True
core_file = tf.string_split([filename], '\\').values[-1]
product_id = tf.string_split([core_file], ".").values[0]
label = search_tf_table_for_entry(product_id)
weight = data_layer_train.get_weights(product_id)
image_string = tf.read_file(filename)
img = tf.image.decode_image(image_string, channels=data_layer_train._channels)
img.set_shape([None, None, None])
img = tf.image.resize_images(img, [data_layer_train._target_height, data_layer_train._target_width])
#Previously I was returning the below, but I was getting an error from the op when assigning feed_dict stating that it didnt like the dictionary
#retval = dict(zip([filename], [img])), label, weight
retval = img, label, weight
return retval
num_files = len(filenames)
filenames = tf.constant(filenames)
#*********** Setup dataset below ************
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels, weights))
dataset=dataset.map(_parse_fn)
dataset = dataset.repeat()
dataset = dataset.batch(32)
iterator = dataset.make_initializable_iterator()
batch_features, batch_labels, batch_weights = iterator.get_next()
return {'images': batch_features, 'labels': batch_labels, 'weights': batch_weights}, iterator
def search_tf_table_for_entry(self, product_id):
'''Looks up keys in the table and outputs the values. Will return -1 if not found '''
if product_id is not None:
return self._products_to_class_table.lookup(product_id)
else:
if not self._real_eval:
logger().info("class not found in training {} ".format(product_id))
return -1
Where I create the model and have the placeholders used previously:
...
def create_model(self):
weights_regularizer = tf.contrib.layers.l2_regularizer(cfg.TRAIN.WEIGHT_DECAY)
biases_regularizer = weights_regularizer
# Input data.
self._input_images = tf.placeholder(
tf.float32, shape=(None, self._image_height, self._image_width, self._num_channels), name="ph_input_images")
self._input_labels = tf.placeholder(tf.int64, shape=(None, 1), name="ph_input_labels")
self._input_weights = tf.placeholder(tf.float32, shape=(None, 1), name="ph_input_weights")
self._is_training = tf.placeholder(tf.bool, name='ph_is_training')
self._keep_prob = tf.placeholder(tf.float32, name="ph_keep_prob")
self._accuracy = tf.reduce_mean(tf.cast(self._correct_prediction, tf.float32))
...
self.create_summaries()
def create_summaries(self):
val_summaries = []
with tf.device("/cpu:0"):
for var in self._act_summaries:
self._add_act_summary(var)
for var in self._train_summaries:
self._add_train_summary(var)
self._summary_op = tf.summary.merge_all()
self._summary_op_val = tf.summary.merge(val_summaries)
def get_summary(self, sess, images, labels, weights):
feed_dict = {self._input_images: images, self._input_labels: labels,
self._input_weights: weights, self._is_training: False}
summary, acc = sess.run([self._summary_op, self._accuracy], feed_dict=feed_dict)
return summary, acc
Since the error says:
Cannot feed value of shape (32,) for Tensor 'ph_input_labels:0', which has shape '(?, 1)
My guess is your labels in get_summary has the shape [32]. Can you just reshape it to (32, 1)? Or maybe reshape the label earlier in _parse_fn?
Hello I want to initialize variable named result in the code below.
I tried to initialize with this code* when I tried to serving.
sess.run(tf.global_variables_initializer(),feed_dict=
{userLat:0,userLon:0})
I just want to initialize the variable.
The reason for using the variable is to write validate_shape = false.
The reason for using this option is to resolve error 'Outer dimension for outputs must be unknown, outer dimension of 'Variable:0' is 1' when deploying the model version to the Google Cloud ml engine.
Initialization with the following code will output a value when feed_dict is 0 when attempting a prediction.
sess.run(tf.global_variables_initializer(),feed_dict=
{userLat:0,userLon:0})
Is there a way to simply initialize the value of result?
Or is it possible to store the list of stored tensor values as a String with a comma without shape?
It's a very basic question.
I'm sorry.
I am a beginner of the tensor flow.
I need help. Thank you for reading.
import tensorflow as tf
import sys,os
#define filename queue
filenameQueue =tf.train.string_input_producer(['./data.csv'],
shuffle=False,name='filename_queue')
# define reader
reader = tf.TextLineReader()
key,value = reader.read(filenameQueue)
#define decoder
recordDefaults = [ ["null"],[0.0],[0.0]]
sId,lat, lng = tf.decode_csv(
value, record_defaults=recordDefaults,field_delim=',')
taxiData=[]
with tf.Session() as sess:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(18):
data=sess.run([sId, lat, lng])
tmpTaxiData=[]
tmpTaxiData.append(data[0])
tmpTaxiData.append(data[1])
tmpTaxiData.append(data[2])
taxiData.append(tmpTaxiData)
coord.request_stop()
coord.join(threads)
from math import sin, cos,acos, sqrt, atan2, radians
#server input data
userLat = tf.placeholder(tf.float32, shape=[])
userLon = tf.placeholder(tf.float32, shape=[])
R = 6373.0
radian=0.017453292519943295
distanceList=[]
for i in taxiData:
taxiId=tf.constant(i[0],dtype=tf.string,shape=[])
taxiLat=tf.constant(i[1],dtype=tf.float32,shape=[])
taxiLon=tf.constant(i[2],dtype=tf.float32,shape=[])
distanceValue=6371*tf.acos(tf.cos(radian*userLat)*
tf.cos(radian*taxiLat)*tf.cos(radian*taxiLon-
radian*126.8943311)+tf.sin(radian*37.4685225)*tf.sin(radian*taxiLat))
tmpDistance=[]
tmpDistance.append(taxiId)
tmpDistance.append(distanceValue)
distanceList.append(tmpDistance)
# result sort
sId,distances=zip(*distanceList)
indices = tf.nn.top_k(distances, k=len(distances)).indices
gather=tf.gather(sId, indices[::-1])[0:5]
result=tf.Variable(gather,validate_shape=False)
print "Done training!"
# serving
import os
from tensorflow.python.util import compat
model_version = 1
path = os.path.join("Taximodel", str(model_version))
builder = tf.saved_model.builder.SavedModelBuilder(path)
with tf.Session() as sess:
builder.add_meta_graph_and_variables(
sess,
[tf.saved_model.tag_constants.SERVING],
signature_def_map= {
"serving_default":
tf.saved_model.signature_def_utils.predict_signature_def(
inputs= {"userLat": userLat, "userLon":userLon},
outputs= {"result": result})
})
builder.save()
print 'Done exporting'
You can try to define the graph so that the output tensor preserves the shape (outer dimension) of the input tensor.
For example, something like:
#server input data
userLoc = tf.placeholder(tf.float32, shape=[None, 2])
def calculate_dist(user_loc):
distanceList = []
for i in taxiData:
taxiId=tf.constant(i[0],dtype=tf.string,shape=[])
taxiLat=tf.constant(i[1],dtype=tf.float32,shape=[])
taxiLon=tf.constant(i[2],dtype=tf.float32,shape=[])
distanceValue=6371*tf.acos(tf.cos(radian*user_loc[0])*
tf.cos(radian*taxiLat)*tf.cos(radian*taxiLon-
radian*126.8943311)+tf.sin(radian*37.4685225)*tf.sin(radian*taxiLat))
tmpDistance=[]
tmpDistance.append(taxiId)
tmpDistance.append(distanceValue)
distanceList.append(tmpDistance)
# result sort
sId,distances=zip(*distanceList)
indices = tf.nn.top_k(distances, k=len(distances)).indices
return tf.gather(sId, indices[::-1])[0:5]
result = tf.map_fn(calculate_dist, userLoc)
I pushed up some test data to gcloud for prediction as a binary tfrecord-file. Running my script I got the error ('No JSON object could be decoded', 162). What do you think I am doing wrong?
To push a prediction job to gcloud, i use this script:
REGION=us-east1
MODEL_NAME=mymodel
VERSION=v_hopt_22
INPUT_PATH=gs://mydb/test-data.tfr
OUTPUT_PATH=gs://mydb/prediction.tfr
JOB_NAME=pred_${MODEL_NAME}_${VERSION}_b
args=" --model "$MODEL_NAME
args+=" --version "$VERSION
args+=" --data-format=TF_RECORD"
args+=" --input-paths "$INPUT_PATH
args+=" --output-path "$OUTPUT_PATH
args+=" --region "$REGION
gcloud ml-engine jobs submit prediction $JOB_NAME $args
test-data.tfr has been generated from a numpy array, as so:
import numpy as np
filename = './Datasets/test-data.npz'
data = np.load(filename)
features = data['X'] # features[channel, example, feature]
np_features = np.swapaxes(features, 0, 1) # features[example, channel, feature]
import tensorflow as tf
import nnscoring.data as D
def floats_feature(arr):
return tf.train.Feature(float_list=tf.train.FloatList(value=arr.flatten().tolist()))
writer = tf.python_io.TFRecordWriter("./Datasets/test-data.tfr")
for i, np_example in enumerate(np_features):
if i%1000==0: print(i)
tf_feature = {
ch: floats_feature(x)
for ch, x in zip(D.channels, np_example)
}
tf_features = tf.train.Features(feature=tf_feature)
tf_example = tf.train.Example(features=tf_features)
writer.write(tf_example.SerializeToString())
writer.close()
Update (following yxshi):
I define the following serving function
def tfrecord_serving_input_fn():
import tensorflow as tf
seq_length = int(dt*sr)
examples = tf.placeholder(tf.string, shape=())
feat_map = {
channel: tf.FixedLenSequenceFeature(shape=(seq_length,),
dtype=tf.float32, allow_missing=True)
for channel in channels
}
parsed = tf.parse_single_example(examples, features=feat_map)
features = {
channel: tf.expand_dims(tensor, -1)
for channel, tensor in parsed.iteritems()
}
from collections import namedtuple
InputFnOps = namedtuple("InputFnOps", "features labels receiver_tensors")
tf.contrib.learn.utils.input_fn_utils.InputFnOps = InputFnOps
return InputFnOps(features=features, labels=None, receiver_tensors=examples)
# InputFnOps = tf.contrib.learn.utils.input_fn_utils.InputFnOps
# return InputFnOps(features, None, parsed)
# Error: InputFnOps has no attribute receiver_tensors
.., which I pass to generate_experiment_fn as so:
export_strategies = [
saved_model_export_utils.make_export_strategy(
tfrecord_serving_input_fn,
exports_to_keep = 1,
default_output_alternative_key = None,
)]
gen_exp_fn = generate_experiment_fn(
train_steps_per_iteration = args.train_steps_per_iteration,
train_steps = args.train_steps,
export_strategies = export_strategies
)
(aside: note the dirty patch of InputFnOps)
It looks like the input is not correctly specified in the inference graph. To use tf_record as input data format, your inference graph must accept strings as the input placeholder. In your case, you should have something like below in your inference code:
examples = tf.placeholder(tf.string, name='input', shape=(None,))
with tf.name_scope('inputs'):
feature_map = {
ch: floats_feature(x)
for ch, x in zip(D.channels, np_example)
}
parsed = tf.parse_example(examples, features=feature_map)
f1 = parsed['feature_name_1']
f2 = parsed['feature_name_2']
...
A close example is here:
https://github.com/GoogleCloudPlatform/cloudml-samples/blob/master/flowers/trainer/model.py#L253
Hope it helps.
i wanna display some python variables in tensorboard, but i dont get it done.
My code so far, display only a line in tensorboard for the lines with static number, if i use the outcommented-lines, it does not work ? It then prints:
ValueError: Shapes () and (?,) are not compatible
Someone has an idea?
import tensorflow as tf
step = 0
session = tf.Session()
tensorboardVar = tf.Variable(0, "tensorboardVar")
pythonVar = tf.placeholder("int32", [None])
#update_tensorboardVar = tensorboardVar.assign(pythonVar)
update_tensorboardVar = tensorboardVar.assign(4)
tf.scalar_summary("myVar", update_tensorboardVar)
merged = tf.merge_all_summaries()
sum_writer = tf.train.SummaryWriter('/tmp/train/c/', session.graph)
session.run(tf.initialize_all_variables())
for i in range(100):
_, result = session.run([update_tensorboardVar, merged])
#_, result = session.run([update_tensorboardVar, merged], feed_dict={pythonVar: i})
sum_writer.add_summary(result, step)
step += 1
this is working:
import tensorflow as tf
import numpy as np
step = 0
session = tf.Session()
tensorboardVar = tf.Variable(0, "tensorboardVar")
pythonVar = tf.placeholder("int32", [])
update_tensorboardVar = tensorboardVar.assign(pythonVar)
tf.scalar_summary("myVar", update_tensorboardVar)
merged = tf.merge_all_summaries()
sum_writer = tf.train.SummaryWriter('/tmp/train/c/', session.graph)
session.run(tf.initialize_all_variables())
for i in range(100):
#_, result = session.run([update_tensorboardVar, merged])
j = np.array(i)
_, result = session.run([update_tensorboardVar, merged], feed_dict={pythonVar: j})
sum_writer.add_summary(result, step)
step += 1
An alternative way can be found in the second answer to Computing exact moving average over multiple batches in tensorflow. There it is shown how you can create custom summaries.