How to use Convolution2D in keras?

What kinds of parameters should I use for convolution2D in keras?

    self.model.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=dataset.X_train.shape[1:]))
    self.model.add(Activation('relu'))
    self.model.add(Convolution2D(32, 3, 3))
    self.model.add(Activation('relu'))
    self.model.add(MaxPooling2D(pool_size=(2, 2)))
    self.model.add(Dropout(0.25))

Solution

To use Convolution2D in Keras, you typically need to specify a few key parameters:

Number of Filters (e.g., 32): This represents the number of output filters in the convolution.
Kernel Size (e.g., 3x3): The dimensions of the convolution window. In your code, it's specified as (3, 3).
Border Mode (e.g., 'same'): This controls how the convolution handles borders. 'same' means the output size is the same as the input size. This is achieved by padding the input as necessary.
Input Shape (e.g., input_shape=dataset.X_train.shape[1:]): This is required only for the first layer and specifies the shape of the input data.

In your example, you are creating a convolutional layer with 32 filters, each with a size of 3x3. The border_mode is set to 'same', ensuring the output volume is the same size as the input. After the convolutional layer, you're adding an activation layer with the ReLU function. Then, you repeat these steps (minus the input shape specification, as it's only needed for the first layer). Following these, you add a Max Pooling layer to reduce the spatial dimensions, and a Dropout layer to prevent overfitting.

MaxPooling2D:

pool_size=(2, 2): This parameter specifies the size of the pooling window. Here, a 2x2 window is used. What this means is that for each 2x2 area in the input feature map, the maximum value is taken and used to form a new, reduced feature map. Essentially, this operation reduces the spatial dimensions (height and width) of the input feature map by half, assuming the stride defaults to the pool size. Max pooling helps to reduce the computational load, memory usage, and also helps with reducing overfitting by providing an abstracted form of the representation.
Dropout:

0.25: This is the dropout rate. In this context, 0.25 means that during the training phase, each neuron (or connection) in the previous layer has a 25% chance of being temporarily "dropped out," i.e., ignored during this training phase. This is a regularization technique used to prevent overfitting. By randomly dropping out neurons from the network during training, it ensures that the network does not become too reliant on any one neuron and thus can generalize better to new data.

This structure is quite standard for convolutional neural networks in image processing tasks. Remember, the specific values for filters, kernel size, and other parameters can vary depending on the specific task and dataset.