I have seen multiple posts on reshaping numpy arrays as inputs to CNN's however, I haven't been able to successfully reshape my array as an input to my CNN!
I have a CNN that merges with another model further downstream. The input shape of the CNN is (4,4,1) -- it is bigger but i have purposefully made it smaller to establish he pipeline and get it running before i put in the proper size.
the format will be the same however, its a 1 channel n x n np.array. I am getting errors when reshaping which I will mention after the code. The input dimensions are put in to the model as follows:
cnn_branch_input = tf.keras.layers.Input(shape=(4,4,1))
cnn_branch_two = tf.keras.layers.Conv2D(etc....)(cnn_branch_input)
the np array (which is originally a pandas dataframe) characteristics and reshaping are as follows:
np.array(array).shape
(4,4)
input = np.array(array).reshape(-1,1,4,4)
input.shape
(1,1,4,4)
the input to my merged model is as follows:
model.fit([cnn_input,gnn_input, gnn_node_feat], y,
#sample_weight=train_mask,
#validation_data=validation_data,
batch_size=4,
shuffle=False)
this causes an error which makes sense to me:
ValueError: Data cardinality is ambiguous:
x sizes: 1, 4, 4 -- Please provide data which shares the same first dimension.
So now when reshaping to intentionally have a 4x4 plus 1 channel shape as follows:
input = np.array(array).reshape(-1,4,4,1)
input.shape
(1,4,4,1)
Two things, the array reshapes to 4, 1x1 arrays, so it seems the structure of the original array is lost, and I get the same error!!
Notice that in both reshape methods, the shape is either (1,4,4,1) or (1,1,4,4).. the -1 entry simply becomes a 1, making the CNN think the first element is shape 1. I thought the -1 would allow me to successfully add the sample dimension as 'any number of samples'.
Simply entering the original (4,4) array, I receive the error that the CNN received a 2 dim array while a 4 dimension array is required.
Im really confused as to how to correctly reshape this array! I would appreciate any help!
Related
When I implement target in in-batch multi-class classification on PyTorch (version 1.6), I have the following problem.
I got a variable D <class 'torch.Tensor'> （related to label description) of size as torch.Size([16, 128]), i.e. [data_size,token_id_size].
The original idea was to generate a target tensor of torch.Size([16]), each value is unique, corresponding to the rows in D, from 0 to 16 as [0,1,2,...,15], for in-batch multi-class classification.
This can be done using target = torch.LongTensor(torch.arange(16))
But there maybe repeated, non-unique rows in D, so I would like that the same, unique row in D has the its unique index in target. For example D has row0, row1, row8 the same token_ids or vector and the other rows are all different from each other, then target should be [0,0,2,3,4,5,6,0,8,9,10,11,12,13,14,15] or [0,0,1,2,3,4,5,0,6,7,8,9,10,11,12,13], wher the former has still indexes 0-15 (but no 1 and 7) and the latter has indexes of all in 0-13.
How can I implement this?
See answers of the simplified question (i) generate 1D tensor as unique index of rows of an 2D tensor and (ii) generate 1D tensor as unique index of rows of an 2D tensor (keeping the order and the original index), which address the problem of this question.
But these seem not useful to improve the contrastive multi-class classification.
Background info: I'm working on sequence-to-sequence models, and right now my model accepts variable-length input tensors (not lists) with input shapes corresponding to [batch size, sequence length]. However, in my implementation, sequence length is unspecified (set to None) to allow for variable length inputs. Specifically, input sequence batches are padded only to the length of the longest sequence in that batch. This has sped up my training time considerably, so I'd prefer to keep it this way, as opposed to going back to bucketed models and/or padded all sequences in the training data to the same length. I'm using TensorFlow 1.0.0.
Problem: I'm currently using the following to compute the loss (which runs just fine).
loss = tf.losses.sparse_softmax_cross_entropy(
weights=target_labels, # shape: [batch size, None]
logits=outputs[:, :-1, :], # shape: [batch size, None, vocab size]
weights=target_weights[:, :-1]) # shape: [batch size, None]
where vocab size is typically about 40,000. I'd like to use a sampled softmax, but I've ran into an issue that's due to the unspecified nature of the input shape. According to the documentation for tf.nn.sampled_softmax_loss, it requires the inputs to be fed separately for each timestep. However, I can't call, for example,
tf.unstack(target_labels, axis=1)
since the axis is unknown beforehand.Does anyone know how I might go about implementing this? One would assume that since both dynamic_rnn and tf.losses.sparse_softmax_cross_entropy seem to have no issue doing this, that a workaround could be implemented with the sampled softmax loss somehow. After digging around in the source code and even models repository, I've come up empty handed. Any help/suggestions would be greatly appreciated.
I have my input state with shape = (84,84,4)
state = Input(shape=(84,84,4), dtype="float")
So I want to pass this to some TimeDistributed layer with time steps size=1..5 (in range of 1 to 5) and I don't know exactly which it equals.
My next layer is something like this:
conv1 = TimeDistributed(Convolution2D(16, 8, 8, subsample=(4, 4), border_mode='valid',
activation='relu', dim_ordering='tf'))(state)
And I've got an error at this layer:
IndexError: tuple index out of range
I just want to pass an unknown time-series size to TimeDistributed and then to LSTM also.
So basically in Keras - you need to provide the sequence length because during computations Keras layers accepts as an input numpy array with a specified shape - what makes compulsory for all inputs (at least in one batch) to have a length fixed. But - you still can deal with varying input size by 0-padding (making all sequence equal size by adding all zero dummy timesteps at the beginning) and then masking what makes your network equivalent to a varying length input network.
You can give a variable sequence-length, like this :
classifier.add(TimeDistributed(Convolution2D(64,(3,3)),input_shape=(None,None,None,3)))
But now you will have to adjust the length of the vector when it flattens or un-rolls at the time prediction.
I am trying to parameterise a 1D conv net via Torch.
Let's say I have a Tensor called data that is of dimensions 10 x 512, in that there are 10 rows and 512 columns. As such, I want to implement a single 3-layer stack of a TemporalConvolution layer, followed by ReLU, followed by TemporalMaxPooling. My classification problem is binary, and there is a corresponding labels tensor, which is 10 x 1. Let us assume that there is already written a feval to iterate through each row in both data and labels.
As such, the problem is to construct a net that can map from 512 columns down to 1 column
Adapted from the documentation:
...
model = nn.Sequential()
model:add(nn.TemporalConvolution(inputFrameSize, outputFrameSize, kW, [dW]))
model:add(nn.ReLU())
model:add(nn.TemporalMaxPooling(kW2, [dW2])
...
criterion = nn.BCECriterion()
...
I have parameterised it as follows, but the following doesn't work : /
TemporalConvolution(512,1,3,1)
ReLU())
TemporalMaxPooling(3, 1)
It throws the error: 2D or 3D(batch mode) tensor expected. As a result I tried to reshape data before passing it to the net:
data = data:resize(1, 100, 512)
But this throws the error: invalid input frame size.
I can see that the error concerns the shape of the data coming into the conv net and of course the parameterisation too. I am further confused by this post here which seems to suggest that inputFrameSize of TemporalConvolution should be set to 10 not 512.
Any guidance would be appreciated, as to how to build a 1D conv net.
P.S. I have tested the script with a logisticRegression model, and that runs, so the issue is purely with the conv net architecture / the shape of the data coming into it.
I guess you misunderstand the meaning of inputFrameSize, which is not the seqlen of your input but n_channels (e.g. for 512*512 RGB images in 2d-convlution, the inputFrameSize should be 3 not 512).
I have trained an estimator, called clf, using fit method and save the model to disk. The next time to run the program , which will load clf from disk.
my problem is :
how to predict a sample which saved on disk? I mean, how to load it and predict?
how to get the sample label instead of label integer after predict?
how to predict a sample which saved on disk? I mean, how to load it and predict?
You have to use the same array representation for the new samples as the one used for the samples passed to fit method. If you want to predict a single sample, the input must be a 2D numpy array with shape (1, n_features).
The way to read your original file on the HDD and convert it to a numpy array representation suitable for classifier is a domain specific issue: it depends whether you are trying to classify text files, jpeg files, frames in a video file, rows in database, log lines for syslog monitored services...
how to get the sample label instead of label integer after predict?
Just keep a list of label names and ensure that the integer used as target values when fitting are in the range [0, n_classes). For instance ['spam', 'ham'], if you have predictions in the range [0, 1] then you can do:
new_samples = # 2D array with shape (n_samples, n_features)
label_names = ['ham', 'spam']
predictions = [label_names[pred] for pred in clf.predict(new_samples)]