I am trying to understand the code given here by Google. It has a line as below in the function def build_model(ratings, embedding_dim=3, init_stddev=1.)
U = tf.Variable(tf.random_normal(
[A_train.dense_shape[0], embedding_dim], stddev=init_stddev))
Its assigning random values to user vector U. What is not clear is how is A_train.dense_shape[0] getting its value from. All the online documentation states that without using an session.run we cant get value from an tensor, since I am using tensorflow 2.8 so hoepfully without using session.run we will get values. Now the problem is when I try to print the same inside or out side the function I am not getting satisfactory result even with tensorflow2.X
Below are all the print that I have tried
tf.print(A_train.dense_shape[0])
print(A_train.dense_shape[0])
Any suggestion what I am doing wrong here. My tensorflow version is 2.8.2
When we write tf.print(A_train.dense_shape[0]) then the calculation is still in graph, this graph must then be executed, we can do that using below code
trr, ter = split_dataframe(ratings) ## this function is defined in the colab notebook given by Google
A_trr = build_rating_sparse_tensor(trr) ## this function is defined in the colab notebook given by Google
A_trr_shape=tf.print(A_trr.dense_shape[1]) ## print the output
with tf.Session() as sess:
sess.run(A_trr_shape) ## execute the shape graph
Related
I am currently working to implement a nonlinear system in Drake, so I've been setting up a LeafSystem as my plant. Following this tutorial, I was able to get a passive version of my system functioning properly (no variable control input). But the tutorial doesn't cover defining inputs in these custom systems, and neither do any examples I was able to find. Could someone help me with a few questions I have listed below?
As shown in the code shown below, I simply guessed at the DeclareVectorInputPort function, but I feel like there should be a third callback similar to CopyStateOut. Should I add a callback that just sets the input port as the scalar phi (my desired voltage input)?
When I call CalcTimeDerivatives, how should I access phi? Should it be passed as another argument or should the aforementioned callback be used to set phi as some callable member of DEAContinuousSys?
At the end of CalcTimeDerivatives, I also feel like there should be a more efficient way to set all derivatives (both velocity and acceleration) to a single vector rather than individually assigning each index of derivatives, but I wasn't able to figure it out. Tips?
from pydrake.systems.framework import BasicVector, LeafSystem
class DEAContinuousSys(LeafSystem):
def __init__(self):
LeafSystem.__init__(self)
self.DeclareContinuousState(2) # two state variables: lam, lam_d
self.DeclareVectorOutputPort('Lam', BasicVector(2), self.CopyStateOut) # two outputs: lam_d, lam_dd
self.DeclareVectorInputPort('phi', BasicVector(1)) # help: guessed at this one, do I need a third argument?
# TODO: add input instead of constant phi
def DoCalcTimeDerivatives(self, context, derivatives):
Lam = context.get_continuous_state_vector() # get state
Lam = Lam.get_value() # cast as regular array
Lam_d = DEA.dynamics(Lam, None, phi) # derive acceleration (no timestep required) TODO: CONNECT VOLTAGE INPUT
derivatives.get_mutable_vector().SetAtIndex(0, Lam_d[0]) # set velocity
derivatives.get_mutable_vector().SetAtIndex(1, Lam_d[1]) # set acceleration
def CopyStateOut(self, context, output):
Lam = context.get_continuous_state_vector().CopyToVector()
output.SetFromVector(Lam)
continuous_sys = DEAContinuousSys()
You're off to a good start! Normally input ports are wired into some other system's output port so there is no need for them to have their own "Calc" method. However, you can also set them to fixed values, using port.FixValue(context, value) (the port object is returned by DeclareVectorInputPort()). Here is a link to the C++ documentation. I'm not certain of the Python equivalent but it should be very similar.
Hopefully someone else can point you to a Python example if the C++ documentation is insufficient.
You might take a look at the quadrotor2d example from the underactuated notes:
https://github.com/RussTedrake/underactuated/blob/master/underactuated/quadrotor2d.py#L44
EDIT: seems like this is a bug in Coremltools. See here:
https://github.com/apple/coremltools/issues/691#event-3295066804
one-sentence summary: the non-maximum suppression layer outputs four values, but I am only able to use two of those four values as inputs to subsequent layers.
I'm building a neural network model using Apple's Core ML Tools package, and I've added a non-maximum suppression (NMS) layer. This layer has four outputs, per the documentation:
output 1: box coordinates, corresponding to the surviving boxes.
output 2: box scores, corresponding to the surviving boxes.
output 3: indices of the surviving boxes. [this is the output I want to use in a subsequent layer]
output 4: number of boxes selected after the NMS algorithm
I've set up a test model with some simple inputs, and the NMS layer correctly returns the expected values for each of the four outputs listed above. If I then use either output#1 or output#2 in a subsequent layer, I have no problems doing so. However, if I try to use output#3 or output#4 in a subsequent layer, I get an error saying that the input was "not found in any of the outputs of the preceding layers."
Jupyter Notebook
The easiest way to replicate the issue is to download and go through this Jupyter Notebook.
Alternatively, the sample code below also walks through the problem I'm having.
Sample Code
I've set up some sample input data as follows:
boxes = np.array(
[[[0.00842474, 0.83051298, 0.91371644, 0.55096077],
[0.34679857, 0.31710117, 0.62449838, 0.70386912],
[0.08059154, 0.74079195, 0.61650205, 0.28471152]]], np.float32)
scores = np.array(
[[[0.87390688],
[0.2797731 ],
[0.72611251]]], np.float32)
input_features = [('boxes', datatypes.Array(*boxes.shape)),
('scores', datatypes.Array(*scores.shape))]
output_features = [('output', None)]
data = {'boxes': boxes,
'scores': scores }
And I've built the test model as follows:
builder = neural_network.NeuralNetworkBuilder(input_features, output_features, disable_rank5_shape_mapping=True)
# delete the original output, which was just a placeholder while initializing the model
del builder.spec.description.output[0]
# add the NMS layer
builder.add_nms(
name="nms",
input_names=["boxes","scores"],
output_names=["nms_coords", "nms_scores", "surviving_indices", "box_count"],
iou_threshold=0.5,
score_threshold=0.5,
max_boxes=3,
per_class_suppression=False)
# make the model output all four of the NMS layer's outputs
for name in ["nms_coords", "nms_scores", "surviving_indices", "box_count"]:
builder.spec.description.output.add()
builder.spec.description.output[-1].name = name
builder.spec.description.output[-1].type.multiArrayType.dataType = ft.ArrayFeatureType.FLOAT32
# # add a linear activation layer (intentionally commented out)
# builder.add_activation(
# name="identity",
# non_linearity="LINEAR",
# input_name="surviving_indices",
# output_name="activation_output",
# params=[1.0,0.0])
# initialize the model
model = coremltools.models.MLModel(builder.spec)
At this point, when I call
output = model.predict(data, useCPUOnly=True)
print(output)
everything works as expected.
For example, if I call print(output['surviving_indices'], the result is [[ 0. 2. -1.]] (as expected).
However, I'm unable to use either the surviving_indices or the box_count outputs as an input to a subsequent layer. For example, if I add a linear activation layer (by uncommenting the block above), I'll get the following error:
Input 'surviving_indices' of layer 'identity' not found in any of the outputs of the preceding layers.
I have the same problem if I use the box_count output (i.e., by setting input_name="box_count" in the add_activation layer).
I know that the NMS layer is correctly returning outputs named surviving_indices and box_count, because when I call print(builder.spec.neuralNetwork.layers[0]) I get the following result:
name: "nms"
input: "boxes"
input: "scores"
output: "nms_coords"
output: "nms_scores"
output: "surviving_indices"
output: "box_count"
NonMaximumSuppression {
iouThreshold: 0.5
scoreThreshold: 0.5
maxBoxes: 3
}
Furthermore, if I use either the nms_coords or the nms_scores outputs as an input to the linear activation layer, I don't have any problems. It will correctly output the unmodified nms_coords or nms_scores values from the NMS layer's output.
I'm baffled as to why the NMS layer correctly outputs what I want it to, but then I'm unable to use those outputs in subsequent layers.
The issue seems to have been fixed. Upgrade with
pip install coremltools>=5.0b3
From this comment made by a CoreMLTools team member:
https://github.com/apple/coremltools/issues/691#issuecomment-900673764
My macOS is 11.3 and I'm using coremltools version 5.0b3.
Please verify that everything is fixed. Please pip install coremltools==5.0b3 on Big Sur or a Monterey beta, then verify you are no longer seeing this bug.
I have a dataset with 5K (and 60 features) records focused on binary classification.
Please note that this solution doesn't work here
I am trying to generate feature importance using Permutation Feature Importance. However, I get the below error. Can you please look at my code and let me know whether I am making any mistake?
import eli5
from eli5.sklearn import PermutationImportance
logreg =LogisticRegression()
model = logreg.fit(X_train_std, y_train)
perm = PermutationImportance(model, random_state=1)
eli5.show_weights(perm, feature_names = X.columns.tolist())
I get an error like as shown below
AttributeError: 'PermutationImportance' object has no attribute 'feature_importances_'
Can you help me resolve this error?
If you look at your attributes of PermutationImportance object via
ord(perm)
you can see all attributes and methods BUT after you fit your PI object, meaning that you need to do:
perm = PermutationImportance(model, random_state=1).fit(X_train,y)
I've followed the TensorFlow for Poets tutorial and replaced the stock flower_photos with a few classes of my own. Now I've got my labels.txt file and my graph.pb saved on my local machine.
Is there a way for me to deploy this pre-trained model to Google Cloud Platform? I've been reading the docs and all I can find are instructions on how to create, train, and deploy models from within their ML Engine. But I don't want to spend money training my model on Google's servers when I only need them to host my model so I can call it for predictions.
Anyone else run into the same problem?
Deploying a locally trained model is a supported use case; the instructions are essentially the same regardless of where you trained it:
To deploy a model version you'll need:
A TensorFlow SavedModel saved on Google Cloud Storage. You can get a
model by:
Following the Cloud ML Engine training steps to train in the
cloud.
Training elsewhere and exporting to a SavedModel.
Unfortunately, TensorFlow for Poets does not show how to export a SavedModel (I've filed a feature request to address that). In the meantime, you can write a "converter" script like the following (you could alternatively do this at the end of training instead of saving out graph.pb and reading it back in):
input_graph = 'graph.pb'
saved_model_dir = 'my_model'
with tf.Graph() as graph:
# Read in the export graph
with tf.gfile.FastGFile(input_graph, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
# CloudML Engine and early versions of TensorFlow Serving do
# not currently support graphs without variables. Add a
# prosthetic variable.
dummy_var = tf.Variable(0)
# Define SavedModel Signature (inputs and outputs)
in_image = graph.get_tensor_by_name('DecodeJpeg/contents:0')
inputs = {'image_bytes':
tf.saved_model.utils.build_tensor_info(in_image)}
out_classes = graph.get_tensor_by_name('final_result:0')
outputs = {'prediction': tf.saved_model.utils.build_tensor_info(out_classes)}
signature = tf.saved_model.signature_def_utils.build_signature_def(
inputs=inputs,
outputs=outputs,
method_name='tensorflow/serving/predict'
)
# Save out the SavedModel.
b = saved_model_builder.SavedModelBuilder(saved_model_dir)
b.add_meta_graph_and_variables(sess,
[tf.saved_model.tag_constants.SERVING],
signature_def_map={'predict_images': signature})
b.save()
(Untested code based on this codelab and this SO post).
If you want the output to use string labels instead of integer indices, make the following change:
# Loads label file, strips off carriage return
label_lines = [line.rstrip() for line
in tf.gfile.GFile("retrained_labels.txt")]
out_classes = graph.get_tensor_by_name('final_result:0')
out_labels = tf.gather(label_lines, ot_classes)
outputs = {'prediction': tf.saved_model.utils.build_tensor_info(out_labels)}
Partial answer only, unfortunately, but I have been able to accomplish this...but with some ongoing issues that I have not yet resolved. I ported over the trained pb and txt files to my server, installed Tensorflow, and am calling the trained model via HTTP request. It works perfectly...on the first run. Then fails every other time.
tensorflow deployment on openshift, errors with gunicorn and mod_wsgi
Surprised there are not more people out there trying to go after this general issue.
I am using a custom image set to train a neural network using Tensorflow API. After successful training process I get these checkpoint files containing values of different training var. I now want to get an inference model from these checkpoint files, I found this script which does that, which I can then use to generate deepdream images as explained in this tutorial. The problem is when I load my model using:
import tensorflow as tf
model_fn = 'export'
graph = tf.Graph()
sess = tf.InteractiveSession(graph=graph)
with tf.gfile.FastGFile(model_fn, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
t_input = tf.placeholder(np.float32, name='input')
imagenet_mean = 117.0
t_preprocessed = tf.expand_dims(t_input-imagenet_mean, 0)
tf.import_graph_def(graph_def, {'input':t_preprocessed})
I get this error:
graph_def.ParseFromString(f.read())
self.MergeFromString(serialized)
raise message_mod.DecodeError('Unexpected end-group tag.')
google.protobuf.message.DecodeError: Unexpected end-group tag.
The script expect a protocol buffer file, I am not sure the script I am using to generate inference models is giving me proto buffer files or not.
Can someone please suggest what am I doing wrong, or is there a better way to achieve this. I simply want to convert checkpoint files generated by tensor to proto buffer.
Thanks
The link to the script you ran is broken, but in any case the recommended thing is not to try to generate an inference model from a checkpoint, but rather to embed code at the end of your training program that will emit a "SavedModel" export (which is not the same thing as a checkpoint).
Please see [1], and in particular the heading "Building a Saved Model". Note that a Saved Model constitutes multiple files, one of which is indeed a protocol buffer (which directly answers your question I hope); the others are variable files and (optional) asset files.
[1] https://www.tensorflow.org/programmers_guide/saved_model