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The error pretty much explains it - you can't use it until you fit the model. If this code itself is generating the error, then maybe TimeDistributed is not propagating the input_shape parameter and needs it set directly, or you need a standalone Input layer.


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In transfer learning there are two parameters which influence the basic setup to go for: Size of new dataset Similarity of new dataset to dataset of pre-trained model When your dataset is small the problem is that high capacity pre-trained models can easily overfit if you re-train too many layers. And since you re-trained multiple layers this could be an ...


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It is possible to build that kind of a CNN. It is important to maintain uniform distribution for both the classes ('cat' and 'not cat'). That is you should have an almost equal number of samples for each of these classes to avoid biasing your model to the 'non-cat' class just because it has huge number of examples. The number of non-cat examples can be ...


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In the convolution layer of the convolutional neural network (CNN), each output value depends on a small number of input values, known as the sparsity of connections. In neural network usage, "dense" connections connect all inputs. By contrast, a CNN is "sparse" because only the local "patch" of pixels is connected, instead ...


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Andrew Ng is making this point in comparison to a simple Neural network. Let's say you have a 10x10 image, In a dense neural network, - We will connect every 100 neurons to the 100 in the next layer.(Dense) - Over that, each all will have a distinct weight (No sharing) So, total parm = 10K In a Convolution Neural Network, the approach is as shown in this ...


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In fact these involve different aspects of parameters in a CNN. Parameter sharing, means one parameter may be shared by more than one input/connection. So this reduces total amount of independent parameters. Parameters shared are non-zero. Sparsity of connections means that some parameters are simply missing (ie are zero), nothing to do with sharing same ...


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As @shepan6 has mentioned, this could be because of your class balance in the validation set. You should print out the confusion matrix on both the training and the validation set. You might find one high error class to be underrepresented in your validation set. You might also find that your algorithm gets two classes confused frequently. For example, with ...


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X[train].shape[0] - This is the number of instances. Let's say it is M X[train].shape[1] - This is the shape of each instance. Each instance is (1 x N) Since input instances are of 1-D, the input data become m x N. Had it been 2-D, it would have been m x Nx x Ny And one more question, I know that CNN required fixed input size. But I split my data into ...


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I know this is an old question, but I guess it needs a more detailed answer than the ones given before. The way a max pooling layer changes the size of the receptive field depends both on the strides and on the size of the max pooling filter. The receptive field is doubled if the max pooling layer has a pool size of (2,2) and also a strides of (2,2).


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RNNs and CNNs are not mutually exclusive! It might seem that they are used to handle different problems, but it is important to note that some types of data can be processed by either architecture. For instance, RNNs uses the sequences as the input. It should be mentioned that sequences are not just limited to text or music. Sequences can also be videos, ...


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When I think about RNNs applied to Computer Vision, two main research areas of Deep Learning come up to my mind: Image Captioning: Neural Networks trained to produce descriptions of images. In that case, you have a Conv ecoder that processed pixel data, and an RNN decoder that produces a description. Video processing (I don't have a better term). Anything ...


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The fourth dimension is because it is referring to either the full dataset(train/test) Or an individual batch. 600 - Number of images in the dataset or batch 64 x 64 - Size of each image 3 - Number of channels


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600 can be the number of images of shape 64x64x3 and not only one image.


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I think you can. each of your classes need to have a sigmoid output which makes each prediction independent of other classes. there you can use binary cross entropy to calculate the overall error/loss.


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You can optimize with non-gradient based methods. The field is called derivative-free optimization. Local Search is one common approach for derivative-free optimization of neural networks.


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Kaggle's Dataset is really noisy and it is really tough to get very good result with simple models. You have to do lots of preproccesing ,Data Augmentations and larger network for many more epoch to get a good result. Things you can do : Go to notebook section of your competetion and look out for some preprocessing and augmentations and apply them. Use some ...


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Early stopping is not directly affected by imbalanced data. Also comput_class_weight effects the training, not the evaluation. As far as picking a metric for evaluating imbalanced data, it depends on the specific problem. The most common choices are F-score, precision, and recall. It appears that you are using scikit-learn which offers a weighted option, ...


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The inputs of a CNN must have the same shape during prediction as when it was trained. So if you have a CNN trained on 50 time-steps windows, then you can make predictions on a stream of input by updating the data in this window continuously. Each new time step you push the most recent row onto the end, and drop the earliest row. Of course it is possible to ...


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Interesting question. You are right in assuming that some 1s may be confused for 7s, same with 8s and 3s for instance. Generally creating different classes as you suggest doesn't happen, simply because it would require more annotation. There are multiple ways to handle this. stacked models Anything labelled as a 1 or a 7 would be given to a model fine-...


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73 millions trainable parms - When using Transfer learning we first freeze the base model - Train it till you reach good accuracy - Then unfreeze it and train for just few epochs. Keep LR small Other probable issues - - If your labels are not One-Hot coded, please use sparse_categorical_crossentropy - Add validation_split in fit method - Suggest you add a ...


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If your dataset is too small, it won't apply to other new data easily. In this case, you should either: try to increase your training dataset Find new images and classify them to increase the training data size, the model will improve as you add new images, but this can be time consuming use transfer learning Find a model that someone else built on a ...


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It looks like the new data has a different distribution from the training data. It looks like the training data is just a single fruit, with white background, and the new image you've passed is a picture of bananas with blue background. The model has probably learned something like: if blue image, then blueberries, and for this reason it classifies the blue ...


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It has no value if it is same for all the training set. Let's say, you are using a global health dataset for Life expectancy then country code can be a useful feature. It might contain hidden information. But if you are doing the same analysis for one country e.g. India, keeping a feature country which has only one value e.g. India, will be of no use. It ...


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You are describing a problem of supervised learning with multiple inputs. That is not an uncommon task and you can find many tutorials about multiple inputs for neural networks out there. Using Tensorflow, I personally recommend Keras Functional API for this task, since it gives you more control on the layers while keeping the high-level simplicity.


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Changing the batch size will not change the overall training time too much. Since with any batch size you are passing almost 80K images. One(and the best) approach will be to use transfer learning. If you have a compelling reason to do full training, you will need a GPU powered bigger hardware. Google Colab can be an option. There are many other options ...


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When you use keras fit, pass the value for x as a generator function which will provide (perhaps using yield) the batch of data (x, y) tuple. Also in the generator function, you can use checkpoint. https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit


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You have the idea of augmentation wrong I suppose. Image augmentation is used to introduce variations in your existing image dataset by using different operations like rotation, slicing, mirroring etc to make the model more robust. But using image augmentation on unbalanced data would keep the resultant data unbalanced as long as all the operations are ...


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welcome to the community I guess there's a confusion in your understanding of the kernel we use in case of rgb data. We normally use a kernel of equal number of channels as the input coming in (in this case as you mentioned it's RGB, so my number of channels for convolution operation would be 3). So instead of using a 3 X 3 Kernel, we use a 3 X 3 X 3 kernel. ...


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Yes, the filter will learn as if they are spatially co-located. The main purpose of convolutions is to detect local features, where the notion of locality comes from the positions over which the filter is applied. Some neural network building blocks that you could use are: Position-wise dense layers: this applies a linear transformation to each of the ...


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It's because of the approach applied - CNN - We use 2-D Convolution on the image, Hence we need the image in 2-D. In this case, we use the fully connected neural network at the end. hence flattening is done at the end. CNN is used to reduce the dimension of the Image without losing the key information. A Simple neural network will become too big to train ...


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In the first example there are 60000 images of 2828 which is a 2d grayscale image. But in order to use CNN your images must be 3 dimensinal with height, width and channel as a new dimension. So you have to resize your every 28 * 28 image into 2828*1 image before you can send it into your CNN layers.


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Typically in a Conv1D layer for a time series, the features can be different measurements taken or recorded at the same time period. So they can be related to each other. For example, if you are trying to predict time=4 below, the question is whether there is a relationship between your features, meas1 and meas2? If so, you want to keep the features together ...


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If you have already extracted a feature vector $X$ from your images. Then indeed you can use an artificial neural network (ANN) to classify these given you have labelled instances. You can do this in python using multiple different libraries such as scikit-learn, Keras, or tensorflow quite easily. Scikit-learn is the easiest to implement but the least ...


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Denoising an image is typically done using Encoder-Decoder models called Denoising Autoencoders, and there's a formal reason for it. Let's take a look at these architectures: We have an Encoder that generates a compressed representation of your input data, and a Decoder that restores it to normal. The reason why this architecture is so useful is that in ...


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Few points to recollect: 1. Bounding box is a label. 2. Grids are useful for predicting midpoints. Though he mentions grids are useful for predicting, the main goal lies in predicting the object itself. The grid suggests if the object is present or not(in other words to locate bx,by). The ground truth for the bounding box is w.r.t. the entire image. So the ...


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There isn't enough information here to give a confident answer. Can you provide some of the below? What is the dataset? Depending on what the images depict, certain augmentations might not make sense. What is the total number of original images? What is the total number of augmented images? If you're using multiple augmentations, how many of each type are ...


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I would recommend to try a pretrained CNN to extract features, then do a simple classifier on top of that. OpenL3 for example is very easy to use, and performs pretty well on a range of tasks. The classifier could be for example Logistic Regression, or Random Forest.


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