I'm trying to do 10 test predictions similar to the code in the CNTK_103B_MNIST_FeedForwardNetwork tutorial, but I'm getting an error as shown below. What could be the problem with my code?
Code / Error Message
To answer my own question. I used the incorrect name of the CNTK input_variable in the model.
I changed this line of code:
predicted_label_prob = pred_basic_model.eval({input : x})
to
predicted_label_prob = pred_basic_model.eval({pred_basic_model.arguments[0] : x})
and now it works.
Related
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 calling a model in a function detect:
def detect(img):
detector_output = detector(tf.reshape(img, (1, img.shape[0], img.shape[1], img.shape[2])))
classes = detector_output['detection_classes'][0].numpy()
most_likely = tf.convert_to_tensor(classes[0])
box = detector_output['detection_boxes'][0][0]
box = tf.math.multiply(box, [img.shape[0], img.shape[1], img.shape[0], img.shape[1]])
box = tf.cast(box, tf.int16)
return (box, most_likely)
this is called in another function reads via tf.data.datasets map api
dataset = dataset.map(reads, num_parallel_calls = AUTO).batch(32)
I think the issue is that this tensorflow hub model (or all object detection models I could find) does not support batching.
Calling the function via reads by itself works fine.
except if I use the tf.function decorator, then weirdly even by itself detect(img) throws the same error.
I tried with several models from here with the same result.
detector needs the shape with the 1 dimension up front.
I know there should be some reverse flatten() or squeeze() but I couldn't find it, apologies for the bad style!
The issue is also likely here in the reshaping.
the full error:
TypeError: Failed to convert object of type <class 'tuple'> to Tensor. Contents: (1, None, None, 3). Consider casting elements to a supported type.
Edit: I fixed the error by using tf.expand_dims instead of reshaping above.
I'd still be glad for a good explanation to understand better what went trong.
Thank you for your help!
I created the minimum working example. You can reproduce it here
I created my own Initializer called ComplexGlorotUniform(Initializer).
Then I created a file like: init_dispatcher = {"complex_glorot_uniform": ComplexGlorotUniform()}
Finally, I did:
from tensorflow.keras.utils import get_custom_objects
get_custom_objects().update(init_dispatcher)
I generated a sequential model of Dense layers using kernel_initializer="complex_glorot_uniform".
Now when using tf.keras.models.clone_model I get the error:
ValueError: Unknown initializer: ComplexGlorotUniform. Please ensure this object is passed to the `custom_objects` argument. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object
I DO think the custom_object is working correctly because he knows it's ComplexGlorotUniform and not the string I gave. Also, the layer is created correctly, is when calling the clone model method that it gets broken.
Well, I am still unsure why but I solved it by changing the init_dispatcher to init_dispatcher = {"ComplexGlorotUniform": ComplexGlorotUniform} to make the name match the string.
I guess it is Ok as it works for me but I am unsure if this is how it is supposed to work.
I've built the following TensorArray:
ta = tf.TensorArray(
dtype=tf.float32,
size=0,
dynamic_size=True,
element_shape=tf.TensorShape([None, None])
)
and called ta = ta.write(idx, my_tensor) inside a while_loop.
When evaluating the output = ta.stack() tensor in a session, I receive this error message:
ValueError: Cannot use '.../TensorArrayWrite/TensorArrayWriteV3' as
input to '.../TensorArrayStack_1/TensorArraySizeV3' because
'.../TensorArrayWrite/TensorArrayWriteV3' is in a while loop. See info
log for more details.
I don't understand this error message, could you please help me ?
Update: A minimal example might be difficult to come up with, but this is what I am doing: I am using the reference to the ta TensorArray inside the cell_input_fn of AttentionWrapper. This callback is used in AttentionWrapper's call method, where another TensorArray named alignment_history is being written. Therefore the while_loop code is not designed by me, it's part of the TF dynamic RNN computation tf.nn.dynamic_rnn.
Not sure if this is what's biting you, but you have to make sure your while_loop function takes the tensor array as input and emits an updated one as output; and you have to use the final version of the TensorArray at the end of the while_loop:
def fn(ta_old):
return ta_old.write(...)
ta_final = while_loop(..., body=fn, [tf.TensorArray(...)])
values = ta_final.stack()
specifically you should never access ta_old outside of fn().
I would like to use placeholders for the dropout rate, number of hidden units, and number of layers in an LSTM-based RNN. Below is the code I am currently trying.
dropout_rate = tf.placeholder(tf.float32)
n_units = tf.placeholder(tf.uint8)
n_layers = tf.placeholder(tf.uint8)
net = rnn_cell.BasicLSTMCell(n_units)
net = rnn_cell.DropoutWrapper(net, output_keep_prob = dropout_rate)
net = rnn_cell.MultiRNNCell([net] * n_layers)
The last line gives the following error:
TypeError: Expected uint8, got <tensorflow.python.ops.rnn_cell.DropoutWrapper
object ... of type 'DropoutWrapper' instead.
I would appreciate any help.
The Error is raised from the following code: [net] * n_layers.
You are trying to make a list looking like [net, net, ..., net] (with a length of n_layers), but n_layers is now a placeholder of unknown value.
I can't think of a way to do that with a placeholder, so I guess you must go back to a standard n_layers=3. (Anyway, putting n_layers as a placeholder was not a good practice in the first place.)