I have found a way to work around to solve the problem. For anyone who encounters the same issue, you can use the Lambda layer to wrap your tensorflow operations, this is what I did: from tensorflow.python.keras.layers import Lambda; def norm(fc2): fc2_norm = K.l2_normalize(fc2, axis = 3); illum_est = tf.reduce_sum(fc2_norm, axis = (1, 2)); illum_est … Read more
.data was an attribute of Variable (object representing Tensor with history tracking e.g. for automatic update), not Tensor. Actually, .data was giving access to the Variable‘s underlying Tensor. However, since PyTorch version 0.4.0, Variable and Tensor have been merged (into an updated Tensor structure), so .data disappeared along the previous Variable object (well Variable is … Read more
I made a diagram. The names follow the PyTorch docs, although I renamed num_layers to w. output comprises all the hidden states in the last layer (“last” depth-wise, not time-wise). (h_n, c_n) comprises the hidden states after the last timestep, t = n, so you could potentially feed them into another LSTM. The batch dimension … Read more
TensorFlow doesn’t have first-class Tensor objects, meaning that there are no notion of Tensor in the underlying graph that’s executed by the runtime. Instead the graph consists of op nodes connected to each other, representing operations. An operation allocates memory for its outputs, which are available on endpoints :0, :1, etc, and you can think … Read more
I have stumbled upon this problem too and below is what I figured out. When training RNN (LSTM or GRU or vanilla-RNN), it is difficult to batch the variable length sequences. For example: if the length of sequences in a size 8 batch is [4,6,8,5,4,3,7,8], you will pad all the sequences and that will result … Read more
Found this page on their github repo: Recommended approach for saving a model There are two main approaches for serializing and restoring a model. The first (recommended) saves and loads only the model parameters: torch.save(the_model.state_dict(), PATH) Then later: the_model = TheModelClass(*args, **kwargs) the_model.load_state_dict(torch.load(PATH)) The second saves and loads the entire model: torch.save(the_model, PATH) Then later: … Read more
I suspect the place where you copied the code from had eager execution enabled, i.e. had invoked tf.enable_eager_execution() at the start of the program. You could do the same. UPDATE: Note that eager execution is enabled by default in TensorFlow 2.0. So the answer above applies only to TensorFlow 1.x
The idea with tensordot is pretty simple – We input the arrays and the respective axes along which the sum-reductions are intended. The axes that take part in sum-reduction are removed in the output and all of the remaining axes from the input arrays are spread-out as different axes in the output keeping the order … Read more
Found this page on their github repo, I’ll just copy-paste the content here. Recommended approach for saving a model There are two main approaches for serializing and restoring a model. The first (recommended) saves and loads only the model parameters: torch.save(the_model.state_dict(), PATH) Then later: the_model = TheModelClass(*args, **kwargs) the_model.load_state_dict(torch.load(PATH)) The second saves and loads the … Read more
Units: The amount of “neurons”, or “cells”, or whatever the layer has inside it. It’s a property of each layer, and yes, it’s related to the output shape (as we will see later). In your picture, except for the input layer, which is conceptually different from other layers, you have: Hidden layer 1: 4 units … Read more