Comments (4)
https://stackoverflow.com/questions/35543428/activation-function-after-pooling-layer-or-convolutional-layer
MaxPool(Relu(x)) = Relu(MaxPool(x))
https://www.tensorflow.org/federated/tutorials/tff_for_federated_learning_research_compression
model = tf.keras.models.Sequential([
tf.keras.layers.InputLayer(input_shape=(28, 28, 1)),
conv2d(filters=32),
max_pool(),
conv2d(filters=64),
max_pool(),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dense(10 if only_digits else 62),
tf.keras.layers.Softmax(),
])
The above model definition is from the Google TF team, which is also the original author of the FedAvg algorithm.
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There are Relu in your second link
https://www.tensorflow.org/federated/tutorials/tff_for_federated_learning_research_compression
from fedml.
oh, I see. Thanks. Could you please help to pull request this fix?
from fedml.
OK, I have pulled the request.
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