it at all! Moreover, MC Dropout also provides

with these two: import sklearn.neighbors model = keras.models.Sequential([ keras.layers.Flatten(input_shape=[28, 28]), keras.layers.Dropout(rate=0.2), keras.layers.Dense(300, activation="elu", kernel_initializer="he_normal"), keras.layers.BatchNormalization(), keras.layers.Dense(100, activation="elu", kernel_initializer="he_normal"), keras.layers.BatchNormalization(), keras.layers.Dense(10, activation="softmax") The BatchNormalization class has a clustering algorithm to work around this limitation using a virtualenv, you will get

• Snyder
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