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.
Related
I am trying to convert my CNN written with tensorflow layers to use the keras api in tensorflow (I am using the keras api provided by TF 1.x), and am having issue writing a custom loss function, to train the model.
According to this guide, when defining a loss function it expects the arguments (y_true, y_pred)
https://www.tensorflow.org/guide/keras/train_and_evaluate#custom_losses
def basic_loss_function(y_true, y_pred):
return ...
However, in every example I have seen, y_true is somehow directly related to the model (in the simple case it is the output of the network). In my problem, this is not the case. How do implement this if my loss function depends on some training data that is unrelated to the tensors of the model?
To be concrete, here is my problem:
I am trying to learn an image embedding trained on pairs of images. My training data includes image pairs and annotations of matching points between the image pairs (image coordinates). The input feature is only the image pairs, and the network is trained in a siamese configuration.
I am able to implement this successfully with tensorflow layers and train it sucesfully with tensorflow estimators.
My current implementations builds a tf Dataset from a large database of tf Records, where the features is a dictionary containing the images and arrays of matching points. Before I could easily feed these arrays of image coordinates to the loss function, but here it is unclear how to do so.
There is a hack I often use that is to calculate the loss within the model, by means of Lambda layers. (When the loss is independent from the true data, for instance, and the model doesn't really have an output to be compared)
In a functional API model:
def loss_calc(x):
loss_input_1, loss_input_2 = x #arbirtray inputs, you choose
#according to what you gave to the Lambda layer
#here you use some external data that doesn't relate to the samples
externalData = K.constant(external_numpy_data)
#calculate the loss
return the loss
Using the outputs of the model itself (the tensor(s) that are used in your loss)
loss = Lambda(loss_calc)([model_output_1, model_output_2])
Create the model outputting the loss instead of the outputs:
model = Model(inputs, loss)
Create a dummy keras loss function for compilation:
def dummy_loss(y_true, y_pred):
return y_pred #where y_pred is the loss itself, the output of the model above
model.compile(loss = dummy_loss, ....)
Use any dummy array correctly sized regarding number of samples for training, it will be ignored:
model.fit(your_inputs, np.zeros((number_of_samples,)), ...)
Another way of doing it, is using a custom training loop.
This is much more work, though.
Although you're using TF1, you can still turn eager execution on at the very beginning of your code and do stuff like it's done in TF2. (tf.enable_eager_execution())
Follow the tutorial for custom training loops: https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough
Here, you calculate the gradients yourself, of any result regarding whatever you want. This means you don't need to follow Keras standards of training.
Finally, you can use the approach you suggested of model.add_loss.
In this case, you calculate the loss exaclty the same way I did in the first answer. And pass this loss tensor to add_loss.
You can probably compile a model with loss=None then (not sure), because you're going to use other losses, not the standard one.
In this case, your model's output will probably be None too, and you should fit with y=None.
If I insert the function, e.g., tf.fft(input, name=None), into a neural network, how does TensorFlow calculate the gradients in backpropagation?
I didn't find any documentation about this.
I am using TensorFlow 1.0.
If you're just inserting the tf.fft(...) function in the middle of a model I'm not certain tensorflow will even be able to handle a forward pass. If you read the docs on tf.signal.fft (https://www.tensorflow.org/api_docs/python/tf/signal/fft) or even just read the tf.fft function header, they both require inputs with dtype=tf.complex64 or dtype=tf.complex128. Perhaps tensorflow will cast float32 inputs to complex and then back again, allowing you to complete a forward pass, I'm not sure, but from what I can gather from reading tensorflow gradient documents casting values causes a disconnect between error gradient and Model parameters, meaning a backward pass won't work. You could try implementing a custom fft function which doesn't cast values and see if that works? It's not so easy though.
I’m transforming a TensorFlow model to Pytorch. And I’d like to initialize the mean and variance of BatchNorm2d using TensorFlow model.
I’m doing it in this way:
bn.running_mean = torch.nn.Parameter(torch.Tensor(TF_param))
And I get this error:
RuntimeError: the derivative for 'running_mean' is not implemented
But is works for bn.weight and bn.bias. Is there any way to initialize the mean and variance using my pre-trained Tensorflow model? Is there anything like moving_mean_initializer and moving_variance_initializer in Pytorch?
Thanks!
The running mean and variance of a batch norm layer are not nn.Parameters, but rather a buffer of the layer.
I think you can simply assign a torch.tensor, no need to wrap a nn.Parameter around it.
This is my third attempt to get a deep learning project off the ground. I'm working with protein sequences. First I tried TFLearn, then raw TensorFlow, and now I'm trying Keras.
The previous two attempts taught me a lot, and gave me some code and concepts that I can re-use. However there has always been an obstacle, and I've asked questions that the developers can't answer (in the case of TFLearn), or I've simply gotten bogged down (TensorFlow object introspection is tedious).
I have written this TensorFlow loss function, and I know it works:
def l2_angle_distance(pred, tgt):
with tf.name_scope("L2AngleDistance"):
# Scaling factor
count = tgt[...,0,0]
scale = tf.to_float(tf.count_nonzero(tf.is_finite(count)))
# Mask NaN in tgt
tgt = tf.where(tf.is_nan(tgt), pred, tgt)
# Calculate L1 losses
losses = tf.losses.cosine_distance(pred, tgt, -1, reduction=tf.losses.Reduction.NONE)
# Square the losses, then sum, to get L2 scalar loss.
# Divide the loss result by the scaling factor.
return tf.reduce_sum(losses * losses) / scale
My target values (tgt) can include NaN, because my protein sequences are passed in a 4D Tensor, despite the fact that the individual sequences differ in length. Before you ask, the data can't be resampled like an image. So I use NaN in the tgt Tensor to indicate "no prediction needed here." Before I calculate the L2 cosine loss, I replace every NaN with the matching values in the prediction (pred) so the loss for every NaN is always zero.
Now, how can I re-use this function in Keras? It appears that the Keras Lambda core layer is not a good choice, because a Lambda only takes a single argument, and a loss function needs two arguments.
Alternately, can I rewrite this function in Keras? I shouldn't ever need to use the Theano or CNTK backend, so it isn't necessary for me to rewrite my function in Keras. I'll use whatever works.
I just looked at the Keras losses.py file to get some clues. I imported keras.backend and had a look around. I also found https://keras.io/backend/. I don't seem to find wrappers for ANY of the TensorFlow function calls I happen to use: to_float(), count_nonzero(), is_finite(), where(), is_nan(), cosine_distance(), or reduce_sum().
Thanks for your suggestions!
I answered my own question. I'm posting the solution for anyone who may come across this same problem.
I tried using my TF loss function directly in Keras, as was independently suggested by Matias Valdenegro. I did not provoke any errors from Keras by doing so, however, the loss value went immediately to NaN.
Eventually I identified the problem. The calling convention for a Keras loss function is first y_true (which I called tgt), then y_pred (my pred). But the calling convention for a TensorFlow loss function is pred first, then tgt. So if you want to keep a Tensorflow-native version of the loss function around, this fix works:
def keras_l2_angle_distance(tgt, pred):
return l2_angle_distance(pred, tgt)
<snip>
model.compile(loss = keras_l2_angle_distance, optimizer = "something")
Maybe Theano or CNTK uses the same parameter order as Keras, I don't know. But I'm back in business.
You don't need to use keras.backend, as your loss is directly written in TensorFlow, then you can use it directly in Keras. The backend functions are an abstraction layer so you can code a loss/layer that will work with the multiple available backends in Keras.
You just have to put your loss in the model.compile call:
model.compile(loss = l2_angle_distance, optimizer = "something")
I defined a model of RNN in tensorflow, one of the gradients in compute_gradients is of type IndexedSlices while others are of type tensor. After I session.run(compute_gradients ...), the returned value type of IndexedSlices is IndexedSlicesValue, then I have two questions:
How could I average several IndexedSlicesValue values?
How can I serialize a IndexedSlicesValue and send it to another machine through socket?
Thank you very much!
IndexedSlices is really an encoding of a sparse tensor, using a pair of dense tensors. It probably comes from the gradient of a tf.gather operation. There is some API documentation about IndexedSlices here that may help: https://www.tensorflow.org/api_docs/python/tf/IndexedSlices
I don't know of much code to work with IndexedSlices directly; typically they are an internal detail used as part of gradient code. Depending on the data sizes, the easiest way to work with them might be to convert them into a dense Tensor and process/send that.