I am trying to implement a text classification model using a CNN. As far as I know, for text data, we should use 1d Convolutions. I saw an example in pytorch using Conv2d but I want to know how can I apply Conv1d for text? Or, it is actually not possible?
Here is my model scenario:
Number of in-channels: 1, Number of out-channels: 128
Kernel size : 3 (only want to consider trigrams)
Batch size : 16
So, I will provide tensors of shape, <16, 1, 28, 300> where 28 is the length of a sentence. I want to use Conv1d which will give me 128 feature maps of length 26 (as I am considering trigrams).
I am not sure, how to define nn.Conv1d() for this setting. I can use Conv2d but want to know is it possible to achieve the same using Conv1d?
This example of Conv1d and Pool1d layers into an RNN resolved my issue.
So, I need to consider the embedding dimension as the number of in-channels while using nn.Conv1d as follows.
m = nn.Conv1d(200, 10, 2) # in-channels = 200, out-channels = 10
input = Variable(torch.randn(10, 200, 5)) # 200 = embedding dim, 5 = seq length
feature_maps = m(input)
print(feature_maps.size()) # feature_maps size = 10,10,4
Although I don't work with text data, the input tensor in its current form would only work using conv2d. One possible way to use conv1d would be to concatenate the embeddings in a tensor of shape e.g. <16,1,28*300>. You can reshape the input with view In pytorch.
Related
I apologize that this is probably a simple question that has been answered before, but I could not find the answer. I’m attempting to use a CNN to extract features and then input that into a FC network that outputs 2 variables. I’m attempting to use the functional linear layer as a way to dynamically handle the flattened features. The self.cnn is a Sequential container which last layer is the nn.Flatten(). When I print the size of x after the CNN I see it is 15x152064, so I’m unclear why the F.linear layer is failing to run with the error below. Any help would be appreciated.
RuntimeError: size mismatch, get 15, 15x152064,2
x = self.cnn(x)
batch_size, channels = x.size()
x = F.linear(x, torch.Tensor([256,channels]))
y_hat = self.FC(x)
torch.Tensor([256, channels]) does not create a tensor of size (256, channels) but the 1D tensor containing the values 256 and channels instead. I don't know how you want to initialize your weights, but there are a couple options :
# Identity transform:
x = F.linear(x, torch.ones(256,channels))
# Random transform :
x = F.linear(x, torch.randn(256,channels))
I'm building a many to many network in Keras, using an LSTM. I have sequences of varying length (labels always have the same length as the sequence they describe). To handle the varying length and after searching on other SO posts I've found padding + masking to be the best solution.
This is my model :
So I have n (874) samples of max_len (24) padded sequences with 25 features each. But how do I handle my labels ? Do I pad them too ?
If I pad them like in the same way as my X (with the same special value) I get this :
X_train shape : (873, 24, 25)
y_train shape : (873, 24)
All is fine except I get the following error :
ValueError: Can not squeeze dim[1], expected a dimension of 1, got 24 for '{{node binary_crossentropy/weighted_loss/Squeeze}} = Squeeze[T=DT_FLOAT, squeeze_dims=[-1]](Cast_1)' with input shapes: [1,24].
Searching up this error leads to post about removing retun_sequences=True from my LSTM layer, except I don't want that since each of my timesteps are labelled...
And if I don't pad them, they can't be converted to a tensor to be used by tensorflow.
Edit:
Explanatory illustration of the architecture I want to achieve, courtesy of this answer :https://stackoverflow.com/a/52092176/7732923
Problem found :
X_train shape : (873, 24, 25)
y_train shape : (873, 24)
y_train contained 873 samples of length 24, with one label for each timestep as I said, but, probably due to the possibility of wanting multi-label classification, each label for each timestep must be contained in a list, so the right shape for y_train must be :
y_train shape : (873, 24, 1)
So it was just about wrapping each label between [] during preprocessing, the architecture is sound and works (and now I'm left to determine how well but that's another beast ahah)
I have a series of processed audio files I am using as input into a CNN using Keras. Does the Keras 1D Convolutional layer support variable sequence lengths? The Keras documentation makes this unclear.
https://keras.io/layers/convolutional/
At the top of the documentation it mentions you can use (None, 128) for variable-length sequences of 128-dimensional vectors. Yet at the bottom it declares that the input shape must be a
3D tensor with shape: (batch_size, steps, input_dim)
Given the following example how should I input sequences of variable length into the network
Lets say I have two examples (a and b) containing X 1 dimensional vectors of length 100 that I want to feed into the 1DConv layer as input
a.shape = (100, 100)
b.shape = (200, 100)
Can I use an input shape of (2, None, 100)? Do I need to concatenate these tensors into c where
c.shape = (300, 100)
Then reshape it to be something
c_reshape.shape = (3, 100, 100)
Where 3 is the batch size, 100, is the number of steps, and the second 100 is the input size? The documentation on the input vector is not very clear.
Keras supports variable lengths by using None in the respective dimension when defining the model.
Notice that often input_shape refers to the shape without the batch size.
So, the 3D tensor with shape (batch_size, steps, input_dim) suits perfectly a model with input_shape=(steps, input_dim).
All you need to make this model accept variable lengths is use None in the steps dimension:
input_shape=(None, input_dim)
Numpy limitation
Now, there is a numpy limitation about variable lengths. You cannot create a numpy array with a shape that suits variable lengths.
A few solutions are available:
Pad your sequences with dummy values until they all reach the same size so you can put them into a numpy array of shape (batch_size, length, input_dim). Use Masking layers to disconsider the dummy values.
Train with separate numpy arrays of shape (1, length, input_dim), each array having its own length.
Group your images by sizes into smaller arrays.
Be careful with layers that don't support variable sizes
In convolutional models using variable sizes, you can't for instance, use Flatten, the result of the flatten would have a variable size if this were possible. And the following Dense layers would not be able to have a constant number of weights. This is impossible.
So, instead of Flatten, you should start using GlobalMaxPooling1D or GlobalAveragePooling1D layers.
I have Conv2D layer defines as:
Conv2D(96, kernel_size=(5, 5),
activation='relu',
input_shape=(image_rows, image_cols, 1),
kernel_initializer=initializers.glorot_normal(seed),
bias_initializer=initializers.glorot_uniform(seed),
padding='same',
name='conv_1')
This is the first layer in my network.
Input dimensions are 64 by 160, image is 1 channel.
I am trying to visualize weights from this convolutional layer but not sure how to get them.
Here is how I am doing this now:
1.Call
layer.get_weights()[0]
This returs an array of shape (5, 5, 1, 96). 1 is because images are 1-channel.
2.Take 5 by 5 filters by
layer.get_weights()[0][:,:,:,j][:,:,0]
Very ugly but I am not sure how to simplify this, any comments are very appreciated.
I am not sure in these 5 by 5 squares. Are they filters actually?
If not could anyone please tell how to correctly grab filters from the model?
I tried to display the weights like so only the first 25. I have the same question that you do is this the filter or something else. It doesn't seem to be the same filters that are derived from deep belief networks or stacked RBM's.
Here is the untrained visualized weights:
and here are the trained weights:
Strangely there is no change after training! If you compare them they are identical.
and then the DBN RBM filters layer 1 on top and layer 2 on bottom:
If i set kernel_intialization="ones" then I get filters that look good but the net loss never decreases though with many trial and error changes:
Here is the code to display the 2D Conv Weights / Filters.
ann = Sequential()
x = Conv2D(filters=64,kernel_size=(5,5),input_shape=(32,32,3))
ann.add(x)
ann.add(Activation("relu"))
...
x1w = x.get_weights()[0][:,:,0,:]
for i in range(1,26):
plt.subplot(5,5,i)
plt.imshow(x1w[:,:,i],interpolation="nearest",cmap="gray")
plt.show()
ann.fit(Xtrain, ytrain_indicator, epochs=5, batch_size=32)
x1w = x.get_weights()[0][:,:,0,:]
for i in range(1,26):
plt.subplot(5,5,i)
plt.imshow(x1w[:,:,i],interpolation="nearest",cmap="gray")
plt.show()
---------------------------UPDATE------------------------
So I tried it again with a learning rate of 0.01 instead of 1e-6 and used the images normalized between 0 and 1 instead of 0 and 255 by dividing the images by 255.0. Now the convolution filters are changing and the output of the first convolutional filter looks like so:
The trained filter you'll notice is changed (not by much) with a reasonable learning rate:
Here is image seven of the CIFAR-10 test set:
And here is the output of the first convolution layer:
And if I take the last convolution layer (no dense layers in between) and feed it to a classifier untrained it is similar to classifying raw images in terms of accuracy but if I train the convolution layers the last convolution layer output increases the accuracy of the classifier (random forest).
So I would conclude the convolution layers are indeed filters as well as weights.
In layer.get_weights()[0][:,:,:,:], the dimensions in [:,:,:,:] are x position of the weight, y position of the weight, the n th input to the corresponding conv layer (coming from the previous layer, note that if you try to obtain the weights of first conv layer then this number is 1 because only one input is driven to the first conv layer) and k th filter or kernel in the corresponding layer, respectively. So, the array shape returned by layer.get_weights()[0] can be interpreted as only one input is driven to the layer and 96 filters with 5x5 size are generated. If you want to reach one of the filters, you can type, lets say the 6th filter
print(layer.get_weights()[0][:,:,:,6].squeeze()).
However, if you need the filters of the 2nd conv layer (see model image link attached below), then notice for each of 32 input images or matrices you will have 64 filters. If you want to get the weights of any of them for example weights of the 4th filter generated for the 8th input image, then you should type
print(layer.get_weights()[0][:,:,8,4].squeeze()).
enter image description here
my question is quite closely related to this question but also goes beyond it.
I am trying to implement the following LSTM in Keras where
the number of timesteps be nb_tsteps=10
the number of input features is nb_feat=40
the number of LSTM cells at each time step is 120
the LSTM layer is followed by TimeDistributedDense layers
From the question referenced above I understand that I have to present the input data as
nb_samples, 10, 40
where I get nb_samples by rolling a window of length nb_tsteps=10 across the original timeseries of shape (5932720, 40). The code is hence
model = Sequential()
model.add(LSTM(120, input_shape=(X_train.shape[1], X_train.shape[2]),
return_sequences=True, consume_less='gpu'))
model.add(TimeDistributed(Dense(50, activation='relu')))
model.add(Dropout(0.2))
model.add(TimeDistributed(Dense(20, activation='relu')))
model.add(Dropout(0.2))
model.add(TimeDistributed(Dense(10, activation='relu')))
model.add(Dropout(0.2))
model.add(TimeDistributed(Dense(3, activation='relu')))
model.add(TimeDistributed(Dense(1, activation='sigmoid')))
Now to my question (assuming the above is correct so far):
The binary responses (0/1) are heavily imbalanced and I need to pass a class_weight dictionary like cw = {0: 1, 1: 25} to model.fit(). However I get an exception class_weight not supported for 3+ dimensional targets. This is because I present the response data as (nb_samples, 1, 1). If I reshape it into a 2D array (nb_samples, 1) I get the exception Error when checking model target: expected timedistributed_5 to have 3 dimensions, but got array with shape (5932720, 1).
Thanks a lot for any help!
I think you should use sample_weight with sample_weight_mode='temporal'.
From the Keras docs:
sample_weight: Numpy array of weights for the training samples, used
for scaling the loss function (during training only). You can either
pass a flat (1D) Numpy array with the same length as the input samples
(1:1 mapping between weights and samples), or in the case of temporal
data, you can pass a 2D array with shape (samples, sequence_length),
to apply a different weight to every timestep of every sample. In this
case you should make sure to specify sample_weight_mode="temporal" in
compile().
In your case you would need to supply a 2D array with the same shape as your labels.
If this is still an issue.. I think the TimeDistributed Layer expects and returns a 3D array (kind of similar to if you have return_sequences=True in the regular LSTM layer). Try adding a Flatten() layer or another LSTM layer at the end before the prediction layer.
d = TimeDistributed(Dense(10))(input_from_previous_layer)
lstm_out = Bidirectional(LSTM(10))(d)
output = Dense(1, activation='sigmoid')(lstm_out)
Using temporal is a workaround. Check out this stack. The issue is also documented on github.