Predict values from a keras model
Once compiled and trained, this function returns the predictions from a keras model. The function keras_predict returns raw predictions, keras_predict_classes gives class predictions, and keras_predict_proba gives class probabilities.
keras_predict(model, x, batch_size = 32, verbose = 1) keras_predict_classes(model, x, batch_size = 32, verbose = 1) keras_predict_proba(model, x, batch_size = 32, verbose = 1)
model |
a keras model object created with Sequential |
x |
input data |
batch_size |
integer. Number of samples per gradient update. |
verbose |
0 for no logging to stdout, 1 for progress bar logging, 2 for one log line per epoch. |
Taylor B. Arnold, taylor.arnold@acm.org
Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.
Other models: LoadSave,
Sequential, keras_compile,
keras_fit
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(Dropout(rate = 0.5))
mod$add(Activation("relu"))
mod$add(Dense(units = 3))
mod$add(ActivityRegularization(l1 = 1))
mod$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, validation_split = 0.2)
dim(keras_predict(mod, X_train))
mean(keras_predict(mod, X_train) == (apply(Y_train, 1, which.max) - 1))
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