As given in the documentation of PyTorch, the layer Conv2d uses a default dilation of 1. Does this mean that if I want to create a simple conv2d layer I would have to write
nn.conv2d(in_channels = 3, out_channels = 64, kernel_size = 3, dilation = 0)
instead of simply writing
nn.conv2d(in_channels = 3, out_channels = 64, kernel_size = 3)
Or is it the case that in PyTorch dilation = 1 means same as dilation = 0 as given here in the Dilated Convolution section?
From the calculation of H_out, W_out in the documentation of pytorch, we can know that dilation=n means to make a pixel (1x1) of kernel to be nxn, where the original kernel pixel is at the topleft, and the rest pixels are empty (or filled with 0).
Thus dilation=1 is equivalent to the standard convolution with no dilation.
Related
im a newbies and would like to understand, how is this being written in code?
The CNN has two convolutional layers; the
first layer has two kernels of size 3 and 4, with 50
feature maps each and the second layer has a kernel of size 2 with 100 feature maps.
Is it something like this?:
model.add(Conv1D(filters = 50, kernel_size = (3,3),(4,4), padding = 'same', activation='relu'))
model.add(MaxPooling1D(pool_size=(2,2))
model.add(Conv1D(filters = 100, kernel_size = 2, padding = 'same', activation='relu')
model.add(MaxPooling1D(pool_size=(2,2))
This seems to be one of the common questions on here (1, 2, 3), but I am still struggling to define the right shape for input to PyTorch conv1D.
I have text sequences of length 512 (number of tokens per sequence) with each token being represented by a vector of length 768 (embedding). The batch size I am using is 6.
So my input tensor to conv1D is of shape [6, 512, 768].
input = torch.randn(6, 512, 768)
Now, I want to convolve over the length of my sequence (512) with a kernel size of 2 using the conv1D layer from PyTorch.
Understanding 1:
I assumed that "in_channels" are the embedding dimension of the conv1D layer. If so, then a conv1D layer will be defined in this way where
in_channels = embedding dimension (768)
out_channels = 100 (arbitrary number)
kernel = 2
convolution_layer = nn.conv1D(768, 100, 2)
feature_map = convolution_layer(input)
But with this assumption, I get the following error:
RuntimeError: Given groups=1, weight of size 100 768 2, expected input `[4, 512, 768]` to have 768 channels, but got 512 channels instead
Understanding 2:
Then I assumed that "in_channels" is the sequence length of the input sequence. If so, then a conv1D layer will be defined in this way where
in_channels = sequence length (512)
out_channels = 100 (arbitrary number)
kernel = 2
convolution_layer = nn.conv1D(512, 100, 2)
feature_map = convolution_layer(input)
This works fine and I get an output feature map of dimension [batch_size, 100, 767]. However, I am confused. Shouldn't the convolutional layer convolve over the sequence length of 512 and output a feature map of dimension [batch_size, 100, 511]?
I will be really grateful for your help.
In pytorch your input shape of [6, 512, 768] should actually be [6, 768, 512] where the feature length is represented by the channel dimension and sequence length is the length dimension. Then you can define your conv1d with in/out channels of 768 and 100 respectively to get an output of [6, 100, 511].
Given an input of shape [6, 512, 768] you can convert it to the correct shape with Tensor.transpose.
input = input.transpose(1, 2).contiguous()
The .contiguous() ensures the memory of the tensor is stored contiguously which helps avoid potential issues during processing.
I found an answer to it (source).
So, usually, BERT outputs vectors of shape
[batch_size, sequence_length, embedding_dim].
where,
sequence_length = number of words or tokens in a sequence (max_length sequence BERT can handle is 512)
embedding_dim = the vector length of the vector describing each token (768 in case of BERT).
thus, input = torch.randn(batch_size, 512, 768)
Now, we want to convolve over the text sequence of length 512 using a kernel size of 2.
So, we define a PyTorch conv1D layer as follows,
convolution_layer = nn.conv1d(in_channels, out_channels, kernel_size)
where,
in_channels = embedding_dim
out_channels = arbitrary int
kernel_size = 2 (I want bigrams)
thus, convolution_layer = nn.conv1d(768, 100, 2)
Now we need a connecting link between the expected input by convolution_layer and the actual input.
For this, we require to
current input shape [batch_size, 512, 768]
expected input [batch_size, 768, 512]
To achieve this expected input shape, we need to use the transpose function from PyTorch.
input_transposed = input.transpose(1, 2)
I have a suggestion for you which may not be what you asked for but might help. Because your input is (6, 512, 768) you can use conv2d instead of 1d.
All you need to do is to add a dimension of 1 at index 1: input.unsqueeze(1) which works as your channel (consider it as a grayscale image)
def forward(self, x):
x = self.embedding(x) # [Batch, seq length, Embedding] = [5, 512, 768])
x = torch.unsqueeze(x, 1) # [5, 1, 512, 768]) # like a grayscale image
and also for your conv2d layer, you can define like this:
window_size=3 # for trigrams
EMBEDDING_SIZE = 768
NUM_FILTERS = 10 # or whatever you want
self.conv = nn.Conv2d(in_channels = 1,
out_channels = NUM_FILTERS,
kernel_size = [window_size, EMBEDDING_SIZE],
padding=(window_size - 1, 0))```
what's the idea behind when using the following convolutional layers?
especially for nn.Conv2d(16, 16, 3, padding = 1)
self.conv1 = nn.Conv2d(3, 16, 3, padding = 1 )
self.conv2 = nn.Conv2d(16, 16, 3, padding = 1)
self.conv3 = nn.Conv2d(16, 32, 3, padding = 1)
self.pool = nn.MaxPool2d(2, 2)
x = F.relu(self.conv1(x))
x = self.pool(F.relu(self.conv2(x)))
x = F.relu(self.conv3(x))
I thought Conv2d always uses a bigger size like
from (16,32) to (32,64) for example.
Is nn.Conv2d(16, 16, 3, padding = 1) merely for reducing the size?
The model architecture all depends on finally what works best for your application, and it's always going to vary.
You are right in saying that usually, you want to make your tensors deeper (in the dimension of your channels) in order to extract richer features, but there is no hard and fast rule about that. Having said that, sometimes you don't want to make your tensors too big, since more the number of channels more the number of trainable parameters making it difficult for your model to train. This again brings me back to the very first line I said - "It all depends".
And as for the line:
nn.Conv2d(16, 16, 3, padding = 1) # stride = 1 by default.
This will keep the size of the tensor the same as the input in all 3 dimensions (height, width, and number of channels).
I will also add the formula to calculate size of output tensor in a convolution for reference.
output_size = ( (input_size - filter_size + 2*padding) / stride ) + 1
If I have a layer with 32 convolution 5x5 rgb kernels in it, I would expect the shape to be (32, 5, 5, 3) being (count, h, w, rgb) but instead it is
(5, 5, 3, 32). This messes up iteration since
for kern in kernels:
Does not work correctly. I get a series of (5 ,3, 32) ndarrays. I do not get each of the 5x5 rgb kernels.
Am I just doing this wrong?
Strange that the kernel is stored in the shape (h, w, channels, filters), as the implementation suggests otherwise:
kernel_shape = self.kernel_size + (self.filters, input_dim)
self.kernel = self.add_weight(shape=kernel_shape, ...)
...
However, if this is what you are seeing, and if you need to iterate over each filter, why not just move the axis with np.moveaxis:
kernel = np.moveaxis(kernel, -1,0)
to get the desired (kernels, h, w, channels).
I'm a bit new to Keras and deep learning. I'm currently trying to replicate this paper but when I'm compiling the first model (without the LSTMs) I get the following error:
"ValueError: Error when checking target: expected dense_3 to have shape (None, 120, 40) but got array with shape (8, 40, 1)"
The description of the model is this:
Input (length T is appliance specific window size)
Parallel 1D convolution with filter size 3, 5, and 7
respectively, stride=1, number of filters=32,
activation type=linear, border mode=same
Merge layer which concatenates the output of
parallel 1D convolutions
Dense layer, output_dim=128, activation type=ReLU
Dense layer, output_dim=128, activation type=ReLU
Dense layer, output_dim=T , activation type=linear
My code is this:
from keras import layers, Input
from keras.models import Model
# the window sizes (seq_length?) are 40, 1075, 465, 72 and 1246 for the kettle, dish washer,
# fridge, microwave, oven and washing machine, respectively.
def ae_net(T):
input_layer = Input(shape= (T,))
branch_a = layers.Conv1D(32, 3, activation= 'linear', padding='same', strides=1)(input_layer)
branch_b = layers.Conv1D(32, 5, activation= 'linear', padding='same', strides=1)(input_layer)
branch_c = layers.Conv1D(32, 7, activation= 'linear', padding='same', strides=1)(input_layer)
merge_layer = layers.concatenate([branch_a, branch_b, branch_c], axis=1)
dense_1 = layers.Dense(128, activation='relu')(merge_layer)
dense_2 =layers.Dense(128, activation='relu')(dense_1)
output_dense = layers.Dense(T, activation='linear')(dense_2)
model = Model(input_layer, output_dense)
return model
model = ae_net(40)
model.compile(loss= 'mean_absolute_error', optimizer='rmsprop')
model.fit(X, y, batch_size= 8)
where X and y are numpy arrays of 8 sequences of a length of 40 values. So X.shape and y.shape are (8, 40, 1). It's actually one batch of data. The thing is I cannot understand how the output would be of shape (None, 120, 40) and what these sizes would mean.
As you noted, your shapes contain batch_size, length and channels: (8,40,1)
Your three convolutions are, each one, creating a tensor like (8,40,32).
Your concatenation in the axis=1 creates a tensor like (8,120,32), where 120 = 3*40.
Now, the dense layers only work on the last dimension (the channels in this case), leaving the length (now 120) untouched.
Solution
Now, it seems you do want to keep the length at the end. So you won't need any flatten or reshape layers. But you will need to keep the length 40, though.
You're probably doing the concatenation in the wrong axis. Instead of the length axis (1), you should concatenate in the channels axis (2 or -1).
So, this should be your concatenate layer:
merge_layer = layers.Concatenate()([branch_a, branch_b, branch_c])
#or layers.Concatenate(axis=-1)([branch_a, branch_b, branch_c])
This will output (8, 40, 96), and the dense layers will transform the 96 in something else.