I am confused about LSTM input/output dimensions, specifically in keras library. How do keras return 2D output while its input is 3D? I know it can return 3D output using “return_sequence = Trure,” but if return_sequence = False, how can it deal with 3D and produces 2D output?
For example, if input data of shape (32, 16, 20), 32 batch size, 16 timestep, 20 features, and output of shape (32, 100), 32 batch size, 100 hidden states; how keras processes input of 3d and returns output 2d.
Additionally, how can concatenate input and hidden state if they don’t have the exact dimensions?
I found the answer to my question in the link below:
https://mmuratarat.github.io/2019-01-19/dimensions-of-lstm
it's very helpful!
Related
I would like to train a CNN using a 2D numpy array as input, but I am receiving this error: ValueError: Error when checking input: expected conv2d_input to have 4 dimensions, but got array with shape (21, 21).
My input is indeed a 21x21 numpy array of floats. The first layer of the network is defined as Conv2D(32, (3, 3), input_shape=(21, 21, 1)) to match the shape of the input array.
I have found some similar questions but none pertaining to a 2D input array, they mostly deal with images. According to the documentation, Conv2D is expecting an input of a 4D tensor containing (samples, channels, rows, cols), but I cannot find any documentation explaining the meaning of these values. Similar questions pertaining to image inputs suggest reshaping the input array using np.ndarray.reshape(), but when trying to do that I receive an input error.
How can I train a CNN on such an input array? Should input_shape be a different size tuple?
Your current numpy array has dimensions (21, 21). However, TensorFlow expects input tensors to have dimensions in the format (batch_size, height, width, channels) or BHWC implying that you need to convert your numpy input array to 4 dimensions (from the current 2 dimensions). One way to do so is as follows:
input = np.expand_dims(input, axis=0)
input = np.expand_dims(input, axis=-1)
Now, the numpy input array has dimensions: (1, 21, 21, 1) which can be passed to a TF Conv2D operation.
Hope this helps! :)
I trained a CNN model with (5x128x128x3) size of input shape
and I got trained weight of (5x128x128x3)
by the way, I wanna use this weight for training (7x128x128x3) size of input data
So, this is my question
should I use only same shape of input?
I wonder if I can use another size (in this case, 7x128x128x3) of input for transfer learning
ValueError: Error when checking input: expected input_1 to have shape (5, 128, 128, 3) but got arry with shape (7, 128, 128, 3)```
Let's break down the dimensions (5x128x128x3):
The first dimension is the batch size (which was 5 when the original model was trained). This is irrelevant and you can set it to None as pointed out in the comments to feed arbitrary sized batches to the model.
The second to third dimensions (128x128) are the width and height of the image and you may be able to change these, but it's hard to say for sure without knowing the model architecture and which layer output you're using for transfer learning. The reason you can change these is that 2d convolutional filters are repeated across the 2d dimensions (width and height) of the image, so they will remain valid for different widths and heights (assuming compatible padding). But if you change the 2d dimensions too much, it is possible that the receptive fields of the layers are changed in a way that hurts transfer learning performance. Eg. if the 7th conv layer in the network for 128x128 input can see the entire input image in each activation (a receptive field of 128x128), then if you double the width and height, it won't anymore and the layer may not recognize certain global features.
The fourth dimension is the number of channels in the input images and you can't change this, as the filters in the first layer will have 3 weights across the depth dimension.
I'm running a classification and predition neural network algorithme using pre-trained model with keras.
Now I know the shape of the input for keras is (224,224,3) but my input has this shape (180, 200, 20) and I get the following error:
ValueError: Dimension 0 in both shapes must be equal, but are 3 and 64. Shapes are [3,3,20,64] and [64,3,3,3]. for 'Assign_32' (op: 'Assign') with input shapes: [3,3,20,64], [64,3,3,3].
and here is the code:
from keras import applications
from keras.layers import Input
input_tensor = Input(shape = (180, 200, 20))
vgg_model = applications.VGG16(weights = 'imagenet', include_top = False, input_tensor = input_tensor)
vgg_model.summary()
Any idea how to get around this? Thank you
From Documentation:
input_shape: optional shape tuple, only to be specified if include_top
is False (otherwise the input shape has to be (224, 224, 3) (with
'channels_last' data format) or (3, 224, 224) (with 'channels_first'
data format). It should have exactly 3 inputs channels, and width and
height should be no smaller than 32. E.g. (200, 200, 3) would be one
valid value.
You can try to create a vgg16 from scratch from this link. VGG16 model for Keras
You need to resize your input image
from keras.preprocessing import image
img = image.load_img("image1.jpeg",target_size=(224,224))
If you want to learn to do transfer learning from scratch in keras you can read this article. This article has step by step implementation.
https://medium.com/#1297rohit/transfer-learning-from-scratch-using-keras-339834b153b9
In your case, since you are not dealing with images of the right size (or number of channels) you may want to cut out large parts of the vgg network to still save the information contained in the middle layers, but I am not sure how efficient it would be.
You would need to remove the first convolution layer, and all the dense layers at the end, replacing them with your own layers. You would certainly need to retrain the whole network, so rather than transfer learning you would be doing very smart initialization.
I got a 3 dimensional array and would like to use it as an input for a sequential model in Keras. The shape of the input array is (32, 32, 4). I want to get an array with the same shape as output. How should i make a feed forward neuronal network with one input, one output and one hidden layer, to make it work with such an array as input?
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.