I am getting this code while analyzing through lstm model. Any help will be much appreciated :)
The tuple index out of range happens when you try to take the second dimension of the X_test.shape (and I guess X_test is 1-dimensional).
As you know, the shape will be a tuple (m, n). But if the numpy array is 1-dimensional the shape will be (m,). So there isnt a second dimension. Therefore X_test.shape[1] will fail.
Related
I have 3 classifiers that run over 288 samples. All of them are sklearn.neural_network.MLPClassifier structures. Here is the code i am using:
list_of_clfs = [MLPClassifier(...), MLPClassifier(...), MLPClassifier(...)]
probas_list = []
for clf in list_of_clfs:
probas_list.append(clf.predict_proba(X_test))
Each predict_proba(X_test) will return a 2D array with shape (n_samples, n_classes). Then, i am creating a 3D array that will contain all predict_proba() in one single place:
proba = np.array(probas_list) #this should return a (3, n_samples, n_classes) array
This should work fine, but i get an error:
ValueError: could not broadcast input array from shape (288,4) into shape (288)
I don't know why, but this works with dummy examples but not with my dataset.
update: it seems like one of the predict_proba() calls is returning an array of shape (288, 2) but my problem has 4 classes. All classifiers are being tested on the same dataset, so i don't know what this comes from.
Thanks in advance
I'm looking at LSTM neural networks. I saw code like this below:
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
This code is meant to change a 2d array into a 3d array but the syntax looks off to me or at least I don't understand it. For example I would assume this code below as a 3d syntax
np.reshape(rows , columns, dimensions)
Could someone elaborate what the syntax is and what it is trying to do.
Function numpy.reshape gives a new shape to an array without changing its data. It is a numpy package function. First of all, it needs to know what to reshape, which is the first argument of this function (in your case, you want to reshape X_train).
Then it needs to know what is the shape of your new matrix. This argument needs to be a tuple. For 2D reshape you can pass (W,H), for three dimensional you can pass (W,H,D), for four dimensional you can pass (W,H,D,T) and so on.
However, you can also call reshape a Numpy matrix by X_train.reshape((W,H,D)). In this case, since reshape function is a method of X_train object, then you do not have to pass it and only pass the new shape.
It is also worth mentioning that the total number of element in a matrix with the new shape, should match your original matrix. For example, your 2D X_train has X_train.shape[0] x X_train.shape[1] elements. This value should be equal to W x H x D.
I am using tf.nn.sparse_softmax_cross_entropy_with_logits and when I pass through the labels and logits I get the following error
tensorflow.python.framework.errors_impl.InvalidArgumentError: labels
must be 1-D, but got shape [50,1]
I don't understnad how having a shape [50,1] is not the same as being 1D
I don't understand how having a shape [50,1] is not the same as being 1D.
While you can reshape a [50, 1] 2D matrix into a [50] 1D matrix just with a simple squeeze, Tensorflow will never do that automatically.
The only heuristic the tf.nn.sparse_softmax_cross_entropy_with_logits uses to check if the input shape is correct is to check the number of dimensions it has. If it's not 1D, it fails without trying other heuristics like checking if the input could be squeezed. This is a security feature.
I'm new to Keras and am trying to test out a model I've just trained.
I'm using Tensorflow backend and Python 3.
However, the shape my input has and the shape Keras says it has in an error are completely different. Here's my code:
testnote = np.zeros((3,))
testnote[0] = 70
testnote[1] = 70
print(testnote.shape)
pred = model.predict(testnote)
print(pred)
My consistent output is "(3,)" for the shape of testnote and then an error for my predict line: "ValueError: Error when checking input: expected dense_1_input to have shape (3,) but got array with shape (1,)"
How is it that Keras reads testnote as having shape (1,) when I've just confirmed that the shape is (3,)? Is it using some sort of different standard for what "shape" means? I've tried reshaping and adding brackets and a bunch of other things, but I don't really know what the problem is.
For additional context, the model takes in an array with 3 scalar input (representing pitch, velocity, and instrument class) and outputs an array with 1025 scalar outputs. I am carefully not using the word "dimension" since I think this is where I'm getting confused, and technically both are only 1 dimension. I'm sure there are many problems with my model which I will have to fix following this. However, I'd like to just get this prediction function working so I can understand what my output looks like.
Thanks in advance for any help.
A Keras Model implicitly expects that your data (passed as a np array) has a dimension for the batch size. Currently, your model is interpreting testnote as being 3 examples of shape 1. Try adding the batch dimension to 'testnote' as follows:
testnote = testnote.reshape(1,-1)
This will reshape testnote to shape (1, 3), so that you explicitly define the batch size to be 1.
I created a CNN whith Python and Keras which compresses 2D input of various length into a single output. All images have a height of 80 pixels, but different lenght, e.g. shape (80, lenght_of_image_i, 2), where 2 is the number of color channels.
I have 5000 images, the shape of the training data array X in numpy is (5000, 1) and the array has dtype object. This is because storing content with different shape is not possible in a single numpy array. Each object in the list has shape (80, lenght_of_image_i, 2).
With this said, when I call the model.fit(X,y) function of the sequential model, I get the following error:
ValueError: Error when checking input: expected conv2d_1_input to have 4
dimensions, but got array with shape (5000, 1)
Converting the numpy array to Python list of numpy arrays also doesn't work:
AttributeError: 'list' object has no attribute 'ndim'
Zero padding or transformations of my data to get all of my images to the same shape is not an option.
My Question now is: How can I call the model.fit(X,y) function when my data has not a fixed shape?
Thank you in advance!
Edit: Note that I do not have a problem with the architecture of my network (since I am not using dense layers). My problem is that I cannot call the fit function, due to problems with the shape of the numpy array.
My model is a replicate of this network: http://machine-listening.eecs.qmul.ac.uk/wp-content/uploads/sites/26/2017/01/sparrow.pdf
You need to pass "numpy arrays" to fit, of type "float". That is the only possibility.
So, you will probably have to group batches of images with the same length, or train each sample individually:
for image, output in zip(images,outputs):
model.train_on_batch(image.reshape((1,80,-1,2), outputs.reshape((1,)+outputs.shape, ....)