conv-neural-network

How to tell Keras stop training based on loss value?

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I found the answer. I looked into Keras sources and find out code for EarlyStopping. I made my own callback, based on it: class EarlyStoppingByLossVal(Callback): def __init__(self, monitor=”val_loss”, value=0.00001, verbose=0): super(Callback, self).__init__() self.monitor = monitor self.value = value self.verbose = verbose def on_epoch_end(self, epoch, logs={}): current = logs.get(self.monitor) if current is None: warnings.warn(“Early stopping requires … Read more

Which parameters should be used for early stopping?

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Early stopping is basically stopping the training once your loss starts to increase (or in other words validation accuracy starts to decrease). According to documents it is used as follows; keras.callbacks.EarlyStopping(monitor=”val_loss”, min_delta=0, patience=0, verbose=0, mode=”auto”) Values depends on your implementation (problem, batch size etc…) but generally to prevent overfitting I would use; Monitor the validation … Read more

Tensorflow Strides Argument

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The pooling and convolutional ops slide a “window” across the input tensor. Using tf.nn.conv2d as an example: If the input tensor has 4 dimensions: [batch, height, width, channels], then the convolution operates on a 2D window on the height, width dimensions. strides determines how much the window shifts by in each of the dimensions. The … Read more

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

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I want to explain with picture from C3D. In a nutshell, convolutional direction & output shape is important! ↑↑↑↑↑ 1D Convolutions – Basic ↑↑↑↑↑ just 1-direction (time-axis) to calculate conv input = [W], filter = [k], output = [W] ex) input = [1,1,1,1,1], filter = [0.25,0.5,0.25], output = [1,1,1,1,1] output-shape is 1D array example) graph … Read more

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

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The reason for this apparent performance discrepancy between categorical & binary cross entropy is what user xtof54 has already reported in his answer below, i.e.: the accuracy computed with the Keras method evaluate is just plain wrong when using binary_crossentropy with more than 2 labels I would like to elaborate more on this, demonstrate the … Read more

Ordering of batch normalization and dropout?

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In the Ioffe and Szegedy 2015, the authors state that “we would like to ensure that for any parameter values, the network always produces activations with the desired distribution”. So the Batch Normalization Layer is actually inserted right after a Conv Layer/Fully Connected Layer, but before feeding into ReLu (or any other kinds of) activation. … Read more