Top "Conv-neural-network" questions

A convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks that has successfully been applied to analyzing visual imagery.

How to verify CuDNN installation?

I have searched many places but ALL I get is HOW to install it, not how to verify that it …

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Why binary_crossentropy and categorical_crossentropy give different performances for the same problem?

I'm trying to train a CNN to categorize text by topic. When I use binary cross-entropy I get ~80% accuracy, with …

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Intuitive understanding of 1D, 2D, and 3D convolutions in convolutional neural networks

Can anyone please clearly explain the difference between 1D, 2D, and 3D convolutions in convolutional neural networks (in deep learning) …

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Ordering of batch normalization and dropout?

The original question was in regard to TensorFlow implementations specifically. However, the answers are for implementations in general. This general …

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Keras ValueError: Input 0 is incompatible with layer conv2d_1: expected ndim=4, found ndim=5

I have checked all the solutions, but still, I am facing the same error. My training images shape is (26721, 32, 32, 1), which …

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Which parameters should be used for early stopping?

I'm training a neural network for my project using Keras. Keras has provided a function for early stopping. May I …

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How to tell Keras stop training based on loss value?

Currently I use the following code: callbacks = [ EarlyStopping(monitor='val_loss', patience=2, verbose=0), ModelCheckpoint(kfold_weights_path, monitor='val_loss', …

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Convert Keras model to C++

I am using Keras (with Theano) to train my CNN model. Does anyone has idea how can I use it …

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How to calculate the number of parameters for convolutional neural network?

I'm using Lasagne to create a CNN for the MNIST dataset. I'm following closely to this example: Convolutional Neural Networks …

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How do you use Keras LeakyReLU in Python?

I am trying to produce a CNN using Keras, and wrote the following code: batch_size = 64 epochs = 20 num_classes = 5 cnn_…

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