Define the way to set the initial random weights of Keras layers.
These functions are used to set the initial weights and biases in a keras model.
Zeros() Ones() Constant(value = 0) RandomNormal(mean = 0, stddev = 0.05, seed = NULL) RandomUniform(minval = -0.05, maxval = 0.05, seed = NULL) TruncatedNormal(mean = 0, stddev = 0.05, seed = NULL) VarianceScaling(scale = 1, mode = "fan_in", distribution = "normal", seed = NULL) Orthogonal(gain = 1, seed = NULL) Identity(gain = 1) lecun_uniform(seed = NULL) glorot_normal(seed = NULL) glorot_uniform(seed = NULL) he_normal(seed = NULL) he_uniform(seed = NULL)
value |
constant value to start all weights at |
mean |
average of the Normal distribution to sample from |
stddev |
standard deviation of the Normal distribution to sample from |
seed |
Integer. Used to seed the random generator. |
minval |
Lower bound of the range of random values to generate. |
maxval |
Upper bound of the range of random values to generate. |
scale |
Scaling factor (positive float). |
mode |
One of "fan_in", "fan_out", "fan_avg". |
distribution |
distribution to use. One of 'normal' or 'uniform' |
gain |
Multiplicative factor to apply to the orthogonal matrix |
Taylor B. Arnold, taylor.arnold@acm.org
Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.
if(keras_available()) {
X_train <- matrix(rnorm(100 * 10), nrow = 100)
Y_train <- to_categorical(matrix(sample(0:2, 100, TRUE), ncol = 1), 3)
mod <- Sequential()
mod$add(Dense(units = 50, input_shape = dim(X_train)[2]))
mod$add(Activation("relu"))
mod$add(Dense(units = 3, kernel_initializer = Zeros(),
bias_initializer = Ones()))
mod$add(Dense(units = 3, kernel_initializer = Constant(),
bias_initializer = RandomNormal()))
mod$add(Dense(units = 3, kernel_initializer = RandomUniform(),
bias_initializer = TruncatedNormal()))
mod$add(Dense(units = 3, kernel_initializer = Orthogonal(),
bias_initializer = VarianceScaling()))
mod$add(Dense(units = 3, kernel_initializer = Identity(),
bias_initializer = lecun_uniform()))
mod$add(Dense(units = 3, kernel_initializer = glorot_normal(),
bias_initializer = glorot_uniform()))
mod$add(Dense(units = 3, kernel_initializer = he_normal(),
bias_initializer = he_uniform()))
mod$add(Activation("softmax"))
keras_compile(mod, loss = 'categorical_crossentropy', optimizer = RMSprop())
keras_fit(mod, X_train, Y_train, batch_size = 32, epochs = 5, verbose = 0)
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