The problem can be described in zigzag scanning. However, I wonder if there's is a TensorFlow version of implementation by using something like tf.tensor_scatter_nd_update that TensorFlow suggests.
BxNxN tensor where B represents Batch.
I found a workaround by using 1x1 conv. Use numpy to generate a constant permutation conv kernel ( tf does not support eager tensor assignment... ), then
reshape tensor(BxNxN) to Bx1x1x(NxN) before applying tf.nn.conv2d to it. Finally do some reshape acrobat to flatten it.
Related
I have an autoencoder defined using tf.keras in tensorflow 1.15. I cannot upgrade to tensorflow to 2.0 for some specific reasons.
This particular autoencoder is used for anomaly detection. I currently compute the AUC score of the autoencoder as follows:
All anomalous inputs are labelled 1 and all normal inputs are labelled 0. This is y_true
I feed the autoencoder with unseen inputs and then measure the reconstruction error, like so: errors = np.mean(np.square(data - model.predict(data)), axis=-1)
The mean of this array is then said to the predicted label, y_pred.
I then compute the AUC using auc = metrics.roc_auc_score(y_true, y_pred).
This approach works well. I now need to move towards using tf.data.dataset to feed in my data. Previously, it was numpy arrays. The issue is, I am unable to convert tf.data.dataset to a numpy array and hence unable to compute the mean squared error as seen in 2.
Once I have a tf.data.Dataset, I feed it for prediction like so: results = model.predict(x_test)
This yields a numpy array, results. I want to compute the mean square error of results with x_test. However, x_test is of type tf.data.Dataset. So the question is, how can I convert a tf.data.dataset to a numpy array in tensorflow 1.15 or what is an alternative method to do this?
I need to create upper triangular masking tensor in pytorch. in tensorflow it's easy using matrix_band_part. is it any pytorch equivalent for this function?
it's like numpy triu_indices but I need for tensor, not just matrix.
I don't know how to convert the PyTorch method adaptive_avg_pool2d to Keras or TensorFlow. Anyone can help?
PyTorch mehod is
adaptive_avg_pool2d(14,[14])
I tried to use the average pooling, the reshape the tensor in Keras, but got the error:
ValueError: total size of new array must be unchanged
I'm not sure if I understood your question, but in PyTorch, you pass the spatial dimensions to AdaptiveAvgPool2d. For instance, if you want to have an output sized 5x7, you can use nn.AdaptiveAvgPool2d((5,7)).
If you want a global average pooling layer, you can use nn.AdaptiveAvgPool2d(1). In Keras you can just use GlobalAveragePooling2D.
For other output sizes in Keras, you need to use AveragePooling2D, but you can't specify the output shape directly. You need to calculate/define the pool_size, stride, and padding parameters depending on how you want the output shape. If you need help with the calculations, check this page of CS231n course.
It's possible to read dense data by this way:
# tf - tensorflow, np - numpy, sess - session
m = np.ones((2, 3))
placeholder = tf.placeholder(tf.int32, shape=m.shape)
sess.run(placeholder, feed_dict={placeholder: m})
How to read scipy sparse matrix (for example scipy.sparse.csr_matrix) into tf.placeholder or maybe tf.sparse_placeholder ?
I think that currently TF does not have a good way to read from sparse data. If you do not want to convert a your sparse matrix into a dense one, you can try to construct a sparse tensor..
Here is what official tutorial tells you:
SparseTensors don't play well with queues. If you use SparseTensors
you have to decode the string records using tf.parse_example after
batching (instead of using tf.parse_single_example before batching).
To feed SciPy sparse matrix to TF placeholder
Option 1: you need to use tf.sparse_placeholder. In Use coo_matrix in TensorFlow shows the way to feed data to a sparse_placeholder
Option 2: you need to convert sparse matrix to NumPy dense matrix and feed to tf.place_holder (of course, this way is impossible when the converted dense matrix is out of memory)
I try to translate a torch code to TensorFlow, but I cannot find the corresponding tensorflow function of SpatialFullConvolution which is able to apply transpose convoluation on vector (not only image).
How can I deal with it?
here is an example https://github.com/soumith/dcgan.torch/blob/master/main.lua
SpatialFullConvolution in torch is same as Transposed Convolution/Deconvolution/Fractionally strided convolution. You can find the Keras documentation here