I have code in my augmentation tf.data pipeline...
# BLURE
filter_size = tf.random.uniform(shape=[], minval=0, maxval=5)
image = tfa.image.mean_filter2d(image, filter_shape=filter_size)
But I'm constantly getting error...
TypeError: The `filter_shape` argument must be a tuple of 2 integers. Received: Tensor("filter_shape:0", shape=(), dtype=int32)
I tried getting static value from random tensorflow like this...
# BLURE
filter_size = tf.get_static_value(tf.random.uniform(shape=[], minval=0, maxval=5))
image = tfa.image.mean_filter2d(image, filter_shape=filter_size)
But I get error...
TypeError: The `filter_shape` argument must be a tuple of 2 integers. Received: None
And this errors makes me sad :(
I want to create augmentation pipeline for tf.data btw...
You should specify an output shape. However, when I did that I ran into another error which hints that the shape requested by mean_filter2d should not be a Tensor. Therefore, I decided to simply go with the random module to generate a random tuple to modify your image.
import random
import tensorflow_addons as tfa
filter_size = tuple(random.randrange(0, 5) for _ in range(2))
image_bllr = tfa.image.mean_filter2d(image, filter_shape=filter_size)
Related
Currently I try to code my own loss function, but when returning the result (a tensor that consists of a list with the loss values) I get the following error:
ValueError: No gradients provided for any variable: ['conv2d/kernel:0', 'conv2d/bias:0', 'conv2d_1/kernel:0', 'conv2d_1/bias:0', 'dense/kernel:0', 'dense/bias:0', 'dense_1/kernel:0', 'dense_1/bias:0', 'dense_2/kernel:0', 'dense_2/bias:0'].
However in tutorials and in their docs they also use tf.recude_mean and when using it like them (they showed how to code mse loss function) I dont get the error, so it seems that I am missing something
My code:
gl = tfa.losses.GIoULoss()
def loss(y_true, y_pred):
batch_size = y_true.shape[0]
# now contains 32 lists (a batch) of bbxs -> shape is (32, 7876)
bbx_true = y_true.numpy()
# now contains 32 lists (a batch) of bbxs here we have to double access [0] in order to get the entry itself
# -> shape is (32, 1, 1, 7876)
bbx_pred = y_pred.numpy()
losses = []
curr_true = []
curr_pred = []
for i in range(batch_size):
curr_true = bbx_true[i]
curr_pred = bbx_pred[i][0][0]
curr_true = [curr_true[x:x+4] for x in range(0, len(curr_true), 4)]
curr_pred = [curr_pred[x:x+4] for x in range(0, len(curr_pred), 4)]
if len(curr_true) == 0:
curr_true.append([0., 0.,0.,0.])
curr_loss = gl(curr_true, curr_pred)
losses.append(curr_loss)
return tf.math.reduce_mean(losses, axis=-1)
Basically I want to achive bounding box regression and because of that I want to use the GIoUloss loss function. Because my model outputs 7896 neurons (the max amount of bounding boxes I want to predict according to my training set times 4) and the gioloss function needs the input as an array of lists with 4 elements each, I have to perform this transformation.
How do I have to change my code in order to also build up a gradient
Numpy don't provide autograd functions so you need to have Tensorflow tensors exclusively in your loss (otherwise the gradient is lost during backpropagation). So avoid using .numpy() and use the tensorflow operators and slicing on tensoflow tensors instead.
I am trying to write a function that runs KMeans on a dataset and outputs the cluster centroids. My aim is to use this in a custom keras layer, so I am using TensorFlow's implementation of KMeans that takes a tensor as the input dataset.
My problem however is that I can't make it work even as a standalone function. The problem comes from the fact that KMeans accepts a generator function that provides mini-batches instead of a plain tensor, but when I am using closure to do that, I get a graph disconnected error:
import tensorflow as tf # version: 2.4.1
from tensorflow.compat.v1.estimator.experimental import KMeans
#tf.function
def KMeansCentroids(inputs, num_clusters, steps, use_mini_batch=False):
# `inputs` is a 2D tensor
def input_fn():
# Each one of the lines below results in the same "Graph Disconnected" error. Tuples don't really needed but just to be consistent with the documentation
return (inputs, None)
return (tf.data.Dataset.from_tensor_slices(inputs), None)
return (tf.convert_to_tensor(inputs), None)
kmeans = KMeans(
num_clusters=num_clusters,
use_mini_batch=use_mini_batch)
kmeans.train(input_fn, steps=steps) # This is where the error happens
return kmeans.cluster_centers()
>>> x = tf.random.uniform((100, 2))
>>> c = KMeansCentroids(x, 5, 10)
The exact error is:
ValueError:
Tensor("strided_slice:0", shape=(), dtype=int32)
must be from the same graph as
Tensor("Equal:0", shape=(), dtype=bool)
(graphs are FuncGraph(name=KMeansCentroids, id=..) and <tensorflow.python.framework.ops.Graph object at ...>).
If I were to use a numpy dataset and convert to tensor inside the function, the code would work just fine.
Also, making input_fn() return directly tf.random.uniform((100, 2)) (ignoring the inputs argument), would again work. That's why I am guessing that tensorflow doesn't support closures since it needs to build the computation graph at the beginning.
But I don't see how to work around that.
Could it be a version error due to KMeans being a compat.v1.experimental module?
Note that the documentation of KMeans states for the input_fn():
The function should construct and return one of the following:
A tf.data.Dataset object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below.
A tuple (features, labels): Where features is a tf.Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor. Both features and labels are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
The problem you're facing is more about invoking tensor outside the created graph. Basically, when you called the .train function, a new graph will be created and that is with the graph defined in that input_fn and the graph defined in the model_fn.
kmeans.train(input_fn, steps=steps)
And, after that all the tensors those coming outside these functions will be treated as outsiders and won't part of this new graph. That's why you're getting a graph disconnected error for trying to use outsider tensor. To resolve this, you need to create the necessary tensors within these graphs.
import tensorflow as tf
from tensorflow.compat.v1.estimator.experimental import KMeans
#tf.function
def KMeansCentroids(num_clusters, steps, use_mini_batch=False):
def input_fn(batch_size):
pinputs = tf.random.uniform((100, 2))
dataset = tf.data.Dataset.from_tensor_slices((pinputs))
dataset = dataset.shuffle(1000).repeat()
return dataset.batch(batch_size)
kmeans = KMeans(
num_clusters=num_clusters,
use_mini_batch=use_mini_batch)
kmeans.train(input_fn = lambda: input_fn(5),
steps=steps)
return kmeans.cluster_centers()
c = KMeansCentroids(5, 10)
Here is some more info for reading, 1. FYI, I tested your code with a few versions of tf > 2, and I don't think it's related to version error or something.
Re-mentioning here for future readers. An alternative of using KMeans within Keras layers:
tf_kmeans.py
ClusteringLayer
I am trying to create a simple neural net in TensorFlow. The only tricky part is I have a custom operation that I have implemented with py_func. When I pass the output from py_func to a Dense layer, TensorFlow complains that the rank should be known. The specific error is:
ValueError: Inputs to `Dense` should have known rank.
I don't know how to preserve the shape of my data when I pass it through py_func. My question is how do I get the correct shape? I have a simple example below to illustrate the problem.
def my_func(x):
return np.sinh(x).astype('float32')
inp = tf.convert_to_tensor(np.arange(5))
y = tf.py_func(my_func, [inp], tf.float32, False)
with tf.Session() as sess:
with sess.as_default():
print(inp.shape)
print(inp.eval())
print(y.shape)
print(y.eval())
The output from this snippet is:
(5,)
[0 1 2 3 4]
<unknown>
[ 0.
1.17520118 3.62686038 10.01787472 27.28991699]
Why is y.shape <unknown>? I want the shape to be (5,) the same as inp. Thanks!
Since py_func can execute arbitrary Python code and output anything, TensorFlow can't figure out the shape (it would require analyzing Python code of function body) You can instead give the shape manually
y.set_shape(inp.get_shape())
I have a placeholder variable that expects a batch of input images:
input_placeholder = tf.placeholder(tf.float32, [None] + image_shape, name='input_images')
Now I have 2 sources for the input data:
1) a tensor and
2) some numpy data.
For the numpy input data, I know how to feed data to the placeholder variable:
sess = tf.Session()
mLoss, = sess.run([loss], feed_dict = {input_placeholder: myNumpyData})
How can I feed a tensor to that placeholder variable?
mLoss, = sess.run([loss], feed_dict = {input_placeholder: myInputTensor})
gives me an error:
TypeError: The value of a feed cannot be a tf.Tensor object. Acceptable feed values include Python scalars, strings, lists, or numpy ndarrays.
I don't want to convert the tensor into a numpy array using .eval(), since that would slow my program down, is there any other way?
This has been discussed on GitHub in 2016, and please check here. Here is the key point by concretevitamin:
One key thing to note is that Tensor is simply a symbolic object. The values of your feed_dict are the actual values, e.g. a Numpy ndarry.
The tensor as a symbolic object is flowing in the graph while the actual values are outside of it, then we can only pass the actual values into the graph and the symbolic object can not exist outside the graph.
You can use feed_dict to feed data into non-placeholders. So, first, wire up your dataflow graph directly to your myInputTensor tensor data source (i.e. don't use a placeholder). Then when you want to run with your numpy data you can effectively mask myImportTensor with myNumpyData, like this:
mLoss, = sess.run([loss], feed_dict={myImportTensor: myNumpyData})
[I'm still trying to figure out how to do this with multiple tensor data sources however.]
One way of solving the problem is to actually remove the Placeholder tensor and replace it by your "myInputTensor".
You will use the myInputTensor as the source for the other operations in the graph and when you want to infer the graph with your np array as input data, you will feed a value to this tensor directly.
Here is a quick example:
import tensorflow as tf
import numpy as np
# Input Tensor
myInputTensor = tf.ones(dtype=tf.float32, shape=1) # In your case, this would be the results of some ops
output = myInputTensor * 5.0
with tf.Session() as sess:
print(sess.run(output)) # == 5.0, using the Tensor value
myNumpyData = np.zeros(1)
print(sess.run(output, {myInputTensor: myNumpyData}) # == 0.0 * 5.0 = 0.0, using the np value
This works for me in latest version...maybe you have older version of TF?
a = tf.Variable(1)
sess.run(2*a, feed_dict={a:5}) # prints 10
I want to reshape a tensor using the [int, -1] notation (to flatten an image, for example). But I don't know the first dimension ahead of time. One use case is train on a large batch, then evaluate on a smaller batch.
Why does this give the following error: got list containing Tensors of type '_Message'?
import tensorflow as tf
import numpy as np
x = tf.placeholder(tf.float32, shape=[None, 28, 28])
batch_size = tf.placeholder(tf.int32)
def reshape(_batch_size):
return tf.reshape(x, [_batch_size, -1])
reshaped = reshape(batch_size)
with tf.Session() as sess:
sess.run([reshaped], feed_dict={x: np.random.rand(100, 28, 28), batch_size: 100})
# Evaluate
sess.run([reshaped], feed_dict={x: np.random.rand(8, 28, 28), batch_size: 8})
Note: when I have the reshape outside of the function it seems to work, but I have very large models that I use multiple times, so I need to keep them in a function and pass the dim using an argument.
To make this work, replace the function:
def reshape(_batch_size):
return tf.reshape(x, [_batch_size, -1])
…with the function:
def reshape(_batch_size):
return tf.reshape(x, tf.pack([_batch_size, -1]))
The reason for the error is that tf.reshape() expects a value that is convertible to a tf.Tensor as its second argument. TensorFlow will automatically convert a list of Python numbers to a tf.Tensor but will not automatically convert a mixed list of numbers and tensors (such as a tf.placeholder())—instead raising the somewhat unintuitive error message you saw.
The tf.pack() op takes a list of objects convertible to a tensor, and converts each element individually, so it can handle the combination of a placeholder and an integer.
hi all the issue is due to Keras version. I tried above all without any success. Uninstall Keras and install via pip. It worked for me.
I was facing this error with Keras 1.0.2 & resolved with Keras 1.2.0
Hope this will help. Thank you