When I invoke tf.contrib.layers.convolution2d the tensorflow execution terminates with an error about one of the parameters used
got an unexpected keyword argument 'weight_init'
The parameter passed are the follows:
layer_one = tf.contrib.layers.convolution2d(
float_image_batch,
num_output_channels=32,
kernel_size=(5,5),
activation_fn=tf.nn.relu,
weight_init=tf.random_normal,
stride=(2, 2),
trainable=True)
That is exactly as described in the book that I'm reading. I suspect a possible syntax problem with weight_init=tf.random_normal written directly inside the call, but I don't know how to fix. I'm using Tensorflow 0.12.0
The book that you are reading (You didn't mention which one) might be using an older version of TensorFlow when the initial values for the weight tensor was passed through the weight_init argument. In the TensorFlow library version you are using (You didn't mention your TF version), probably that argument is replaced with weight_initializer. The latest (TensorFlow v0.12.0) documentation for tf.contrib.layers.convolution2d is here.
To fix your problem, you can change the following line in your code:
weight_init=tf.random_normal
to
weight_initializer=tf.random_normal_initializer()
According to the documentation, by default, tf.random_normal_initialier uses a 0.0 mean, a standard deviation of 1.0 and the datatype to be tf.float32. You may change the arguments as per your need using this line instead:
weight_initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0, seed=None, dtype=tf.float32)
Related
When loading images from a directory in Tensorflow, you use something like:
dataset = tf.keras.utils.image_dataset_from_directory(
"S:\\Images",
batch_size=32,
image_size=(128,128),
label_mode=None,
validation_split=0.20, #Reserve 20% of images for validation
subset='training', #If we specify a validation_split, we *must* specify subset
seed=619 #If using validation_split we *must* specify a seed to ensure there is no overlap between training and validation data
)
But of course some of the images (.jpg, .png, .gif, .bmp) will be invalid. So we want to ignore those errors; just skip them (and ideally log the filenames so they can be repaired, removed, or deleted).
There have been some ideas along the way of how to ignore invalid images:
Method 1: tf.contrib.data.ignore_errors (Tensorflow 1.x only)
Warning: The tf.contrib module will not be included in TensorFlow 2.0.
Sample usage:
dataset = dataset.apply(tf.contrib.data.ignore_errors())
The only down-side of this method is that it was only available in Tensorflow 1. Trying to use it today simply won't work, as the tf.contib namespace no longer exists. That led to a built-in method:
Method 2: tf.data.experimental.ignore_errors(log_warning=False) (deprecated)
From the documentation:
Creates a Dataset from another Dataset and silently ignores any errors. (deprecated)
Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.data.Dataset.ignore_errors instead.
Sample usage:
dataset = dataset.apply(tf.data.experimental.ignore_errors(log_warning=True))
And this method works. It works great. And it has the advantage of working.
But it's apparently deprecated, and they documentation says we should use method 3:
Method 3 - tf.data.Dataset.ignore_errors(log_warning=False, name=None)
Drops elements that cause errors.
Sample usage:
dataset = dataset.ignore_errors(log_warning=True, name="Loading images from directory")
Except it doesn't work
The dataset.ignore_errors attribute doesn't work, and gives the error:
AttributeError: 'BatchDataset' object has no attribute 'ignore_errors'
Which means:
the thing that works is deprecated
they tell us to use this other thing instead
and "provide the instructions for updating"
but the other thing doesn't work
So we ask Stackoverflow:
How do i use tf.data.Dataset.ignore_errors to ignore errors?
Bonus Reading
TensorFlow Dataset `.map` - Is it possible to ignore errors?
TensorFlow: How to skip broken data
Untested Workaround
Not only is it not what i was asking, but people are not allowed to read this:
It looks like the tf.data.Dataset.ignore_errors() method is not
available in the BatchDataset object, which is what you are using in
your code. You can try using tf.data.Dataset.filter() to filter out
elements that cause errors when loading the images. You can use a
try-except block inside the lambda function passed to filter() to
catch the errors and return False for elements that cause errors,
which will filter them out. Here's an example of how you can use
filter() to achieve this:
def filter_fn(x):
try:
# Load the image and do some processing
# Return True if the image is valid, False otherwise
return True
except:
return False
dataset = dataset.filter(filter_fn)
Alternatively, you can use the tf.data.experimental.ignore_errors()
method, which is currently available in TensorFlow 2.x. This method
will silently ignore any errors that occur while processing the
elements of the dataset. However, keep in mind that this method is
experimental and may be removed or changed in a future version.
tf.data.Dataset.ignore_errors was introduced in TensorFlow 2.11. You can use tf.data.experimental.ignore_errors for older versions like so:
dataset.apply(tf.data.experimental.ignore_errors())
I was following this tutorial which comes with this notebook.
I plan to use Tensorflow for my project, so I followed this tutorial and added the line
tokenized_datasets = tokenized_datasets["train"].to_tf_dataset(columns=["input_ids"], shuffle=True, batch_size=16, collate_fn=data_collator)
to the end of the notebook.
However, when I ran it, I got the following error:
RuntimeError: Index put requires the source and destination dtypes match, got Float for the destination and Long for the source.
Why didn't this work? How can I use the collator?
The issue is not your code, but how the collator is set up. (It's set up to not use Tensorflow by default.)
If you look at this, you'll see that their collator uses the return_tensors="tf" argument. If you add this to your collator, your code for using the collator will work.
In short, your collator creation should look like
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm_probability=0.15, return_tensors="tf")
This will fix the issue.
I am having issues replicating a pymc2 code using pymc3.
I believe it is due to the fact pymc3 is using the theano type variables which are not compatible with the numpy operations I am using. So I am using the #theano.decorator:
I have this function:
with pymc3.Model() as model:
z_stars = pymc3.Uniform('z_star', self.z_min_ssp_limit, self.z_max_ssp_limit)
Av_stars = pymc3.Uniform('Av_star', 0.0, 5.00)
sigma_stars = pymc3.Uniform('sigma_star',0.0, 5.0)
#Fit observational wavelength
ssp_fit_output = self.ssp_fit_theano(z_stars, Av_stars, sigma_stars,
self.obj_data['obs_wave_resam'],
self.obj_data['obs_flux_norm_masked'],
self.obj_data['basesWave_resam'],
self.obj_data['bases_flux_norm'],
self.obj_data['int_mask'],
self.obj_data['normFlux_obs'])
#Define likelihood
like = pymc.Normal('ChiSq', mu=ssp_fit_output,
sd=self.obj_data['obs_fluxEr_norm'],
observed=self.obj_data['obs_fluxEr_norm'])
#Run the sampler
trace = pymc3.sample(iterations, step=step, start=start_conditions, trace=db)
where:
#theano.compile.ops.as_op(itypes=[t.dscalar,t.dscalar,t.dscalar,t.dvector,
t.dvector,t.dvector,t.dvector,t.dvector,t.dscalar],
otypes=[t.dvector])
def ssp_fit_theano(self, input_z, input_sigma, input_Av, obs_wave, obs_flux_masked,
rest_wave, bases_flux, int_mask, obsFlux_mean):
...
...
The first three variables are scalars (from the pymc3 uniform distribution). The
remaining variables are numpy arrays and the last one is a float. However, I am
getting this "'numpy.ndarray' object has no attribute 'type'" error:
File "/home/user/anaconda/lib/python2.7/site-packages/theano/gof/op.py", line 615, in __call__
node = self.make_node(*inputs, **kwargs)
File "/home/user/anaconda/lib/python2.7/site-packages/theano/gof/op.py", line 963, in make_node
if not all(inp.type == it for inp, it in zip(inputs, self.itypes)):
File "/home/user/anaconda/lib/python2.7/site-packages/theano/gof/op.py", line 963, in <genexpr>
if not all(inp.type == it for inp, it in zip(inputs, self.itypes)):
AttributeError: 'numpy.ndarray' object has no attribute 'type'
Please any advice in the right direction will be most welcomed.
I had a bunch of time-wasting-stops when I went from pymc2 to pymc3. The problem, I think, is that the doc is quite bad. I suspect they neglect the doc as far as the code is still evolving. 3 comments/advises:
I wish you could find some help using '#theano.compile.ops.as_op' here: failure to adapt pymc2 into pymc3 or here how to fit a method belonging to an instance with pymc3?
The drawback of '#theano.compile.ops.as_op' is that you implicitly exclude any analysis related to the gradient of your function. To have access to the gradient, I think you need to define your function in a more complex way presented here how to fit a method belonging to an instance with pymc3?
warning: for the moment, using theano seems to be a source of problem if you want to distribute your code under Windows. See build a .exe for Windows from a python 3 script importing theano with pyinstaller, but I am not sure whether it is just a personal clumsiness or really a problem. Personally I had to give up theano to be able to distribute my code...
I am trying to run an implementation of attention mechanism by Google DeepMind. However it is based on an older version of TensorFlow and I am getting this error
from tensorflow.models.rnn.rnn_cell import RNNCell, linear
concat = linear([inputs, h, self.c], 4 * self._num_units, True)
NameError: global name 'linear' is not defined
I couldn't find the linear model/function in the new tensorflow documentation. Can anyone help me with this? Thanks!
You need to use the tf.nn.rnn_cell._linear function to make the code work. Have a look at this tutorial for a sample usage.
I've been always using linear kernels in libsvm by following command
python grid.py -log2c -1,10,1 -log2g -1,1,1 -t 0 data
But I now consider linear kernel in libsvm is different from liblinear. The example given in liblinear official site gives me "ValueError: could not convert string to float: null"
python grid.py -log2c -3,0,1 -log2g null -svmtrain ./train heart_scale
The other example in liblinear documentation doesn't work neither, saying "Unknown option: -g" and TypeError in line 219: if rate is None: raise "get no rate".
./grid.py -log2c -14,14,1 -log2g 1,1,1 -svmtrain ./train news20.scale
I'm wondering what's the right way of using liblinear train with grid.py.
Get the latest version of grid.py from the the libsvm website.
I happen to modifying my copy of grid.py at the moment and I can see it explicitly has handling for -log2g null.