MaxPooling | R Documentation |
Max pooling operations
MaxPooling1D(pool_size = 2, strides = NULL, padding = "valid", input_shape = NULL) MaxPooling2D(pool_size = c(2, 2), strides = NULL, padding = "valid", data_format = NULL, input_shape = NULL) MaxPooling3D(pool_size = c(2, 2, 2), strides = NULL, padding = "valid", data_format = NULL, input_shape = NULL)
pool_size |
Integer or triplet of integers; size(s) of the max pooling windows. |
strides |
Integer, triplet of integers, or None. Factor(s) by which to downscale. E.g. 2 will halve the input. If NULL, it will default to pool_size. |
padding |
One of "valid" or "same" (case-insensitive). |
input_shape |
only need when first layer of a model; sets the input shape of the data |
data_format |
A string, one of channels_last (default) or channels_first |
Taylor B. Arnold, taylor.arnold@acm.org
Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.
Other layers: Activation
,
ActivityRegularization
,
AdvancedActivation
,
BatchNormalization
, Conv
,
Dense
, Dropout
,
Embedding
, Flatten
,
GaussianNoise
, LayerWrapper
,
LocallyConnected
, Masking
,
Permute
, RNN
,
RepeatVector
, Reshape
,
Sequential
if(keras_available()) { X_train <- array(rnorm(100 * 28 * 28), dim = c(100, 28, 28, 1)) Y_train <- to_categorical(matrix(sample(0:2, 100, TRUE), ncol = 1), 3) mod <- Sequential() mod$add(Conv2D(filters = 2, kernel_size = c(2, 2), input_shape = c(28, 28, 1))) mod$add(Activation("relu")) mod$add(MaxPooling2D(pool_size=c(2, 2))) mod$add(LocallyConnected2D(filters = 2, kernel_size = c(2, 2))) mod$add(Activation("relu")) mod$add(MaxPooling2D(pool_size=c(2, 2))) mod$add(Dropout(0.25)) mod$add(Flatten()) mod$add(Dropout(0.5)) mod$add(Dense(3, activation='softmax')) keras_compile(mod, loss='categorical_crossentropy', optimizer=RMSprop()) keras_fit(mod, X_train, Y_train, verbose = 0) }
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