Description Usage Arguments Author(s) References Examples
These functions are used to set the initial weights and biases in a keras model.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | 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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