I'm now writing a custom op, and it's output shape is dynamic, how to define the output_shape in infer_shape function.
The infer_shape function of CustomOpProp has an in_shape parameter. You can use it to calculate the output_shape dynamically.
If you cannot infer input shape without input data, but can infer maximum shape, then you can use it as commented here: https://github.com/apache/incubator-mxnet/issues/9758
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First, I would like to say that I only have little experience in Keras/Tensorflow and probably lack some understanding on tensors manipulations.
I am using a model which input is an "oversized" matrix (NxN). That is, I feed it with data that can be smaller (ie. (KxK), K <= N) where "missing" data (to fit the NxN shape) is filled with zeros. The output is an encoded version (Nx2) of the input.
I'm using a custom loss function that I would like to be computed only on the (Kx2) first values of the model's output. To do so, I think the solution is to "slice" the y_pred tensor in my loss function since I don't want to simply mask it with a boolean tensor. However, I can't figure out how to pass K as a dynamic argument to my custom loss.
Wrapping the function within another function that takes an argument does not fit my needs since the K value will change on each data sample
Passing K in the model's input and getting it back through a function wrapp (eg. https://stackoverflow.com/a/55445837/6315123) as mentionned in the first point does not work either, since slices cannot be computed from Tensor (as far as I understand); and evaluate the tensor within the loss function doesn't seem possible.
How can I pass such an argument to my loss function ?
Thanks !
I'm using tf.data.experimental.make_csv_dataset to create a dataset from a .csv file. I'm also using tf.keras.layers.DenseFeatures as an input layer of my model.
I'm struggling to create a DenseFeatures layer properly so that it is compatible with my dataset in the case when batch_size parameter of make_csv_dataset is not equal to 1 (in case if batch_size=1 my setup works as expected).
I create DenseFeatures layer using a list of tf.feature_column.numeric_column elements with shape=(my_batch_size,), but it seems like in this case for some reason the input layer expects [my_batch_size,my_batch_size] shape instead of [my_batch_size,1].
With my_batch_size=19 I'm getting the following error when trying to fit the model:
ValueError: Cannot reshape a tensor with 19 elements to shape [19,19] (361 elements) for 'MyModel/Input/MyColumn1/Reshape' (op: 'Reshape') with input shapes: [19,1], [2] and with input
tensors computed as partial shapes: input[1] = [19,19].
If I don't specify shape when creating numeric_column it doesn't work either. I'm getting the following error:
tensorflow.python.framework.errors_impl.InvalidArgumentError: The second input must be a scalar, but it has shape [19]
which assumes that numeric_column expects a scalar but recieves the whole batch in one Tensor.
How do I create an input layer of DenseFeatures so that it accepts the dataset produced by make_csv_dataset(batch_size=my_batch_size)?
From the tf.feature_column.numeric_column documentation:
shape: An iterable of integers specifies the shape of the Tensor. An integer can be given which means a single dimension Tensor with given width. The Tensor representing the column will have the shape of [batch_size] + shape.
This means that you must not pass the batch size to the shape argument: shape=().
Currently, with a batch size of 1, you get shape=(1,) that TF can handle thanks to broadcasting or something like that (dimensions of size 1 are easily added by TF if necessary), that's why it works.
Hope this can help. Provide more code if you want more help.
I am trying to predict() the output for a single data point d, using my trained Keras model loaded from a file. But I get a ValueError If predicting from data tensors, you should specify the 'step' argument. What does that mean?
I tried setting step=1, but then I get a different error ValueError: Cannot feed value of shape () for Tensor u'input_1:0', which has shape '(?, 600)'.
Here is my code:
d = np.concatenate((hidden[p[i]], hidden[x[i]])).resize((1,600))
hidden[p[i]] = autoencoder.predict(d,steps=)
The model is expecting (?,600) as input. I have concatenated two numpy arrays of shape (300,) each to get (600,), which is resized to (1,600). This (1,600) is my input to predict().
In my case, the input to predict was None (because I had a bug in another part of the code).
In official doc, steps refer to the total number of steps before stopping. So steps=1 means make predictions on one batch instead of making prediction on one record (single data point).
https://keras.io/models/sequential/
-> Define value of steps argument,
d = np.concatenate((hidden[p[i]],
hidden[x[i]])).resize((1,600))
hidden[p[i]] = autoencoder.predict(d,steps=1)
If you are using a test data generator, it is good practice to define the steps, as mentioned in the documentation.
If you are predicting a single instance, no need to define the steps. Just make sure the argument (i.e. instance 'd') is not None, otherwise that error will show up. Some reshaping may also be necessary.
in my case i got the same error, i just reshaped the data to predict with numpy function reshape() to the shape of the data originally used to train the model.
I use tf.strided_slice to get one value out of the 1d tensor. Unfortunately, inferred shape is ?. How can I assert/declare that it has shape [1]?
P.S. I used reshape, but it might have performance implications in some cases
Use x.set_shape() to provide additional information about the shape of this tensor that cannot be inferred from the graph alone.
You can get more information from the FAQ:
The tf.Tensor.set_shape method updates the static shape of a Tensor
object, and it is typically used to provide additional shape
information when this cannot be inferred directly. It does not change
the dynamic shape of the tensor.
At each iteration I want to dynamically provide how many placeholders I want and then will feed data to them. Is that possible and how ? I tried to create the whole model (placeholders, loss, optimizer) inside epoch loop but that gave uninitialised variables error.
At present I have n=5 placeholders each of shape=(1, k) in a list and I feed data to them. But n needs to dynamically defined during data feeding inside epoch loop.
Maybe you misunderstood what a tensor is.
If you think of a tensor like a multi-dimensional list, you can understand that having a dynamically number of placeholder with a shape [1, k] is no sense.
Instead, you have to use a single tensor.
Thus, define your input placeholder as a tensor with shape [None, 1, k].
placeholder_ = tf.placeholder(tf.float32, [None, 1, k])
With this statement you define a placeholder with tf.float32 type and an undefined number of elements (the None part) with shape [1,k].
In every iteration, you have to feed the placeholder with the right values. Eg running
result = sess.run(defined_op, feed_dict={
placeholder_: numpy_ndarray_with_N_elements_with_shape_1_k
})
In that way you don't need to define new variables into the computational graph (that simply doesn't work) but feed it with the desired values.