f54c2b1361fb86f55a36064158c6baa658ffffb9,examples/mnist/run.py,,,#,162

Before Change

  // compute prediction
  w0, b0, w1, b1 = params
  layer0 = tfe.matmul(x, w0) + b0
  layer1 = tfe.sigmoid(layer0 * 0.1)  // input normalized to avoid large values
  logits = tfe.matmul(layer1, w1) + b1

  // send prediction output back to client
  prediction_op = tfe.define_output(prediction_client.player_name,
                                    logits,

After Change

  with tfe.protocol.SecureNN():
    batch_size = PredictionClient.BATCH_SIZE
    flat_dim = ModelOwner.IMG_ROWS * ModelOwner.IMG_COLS
    batch_input_shape = [batch_size, flat_dim]

    model = tfe.keras.Sequential()
    model.add(tfe.keras.layers.Dense(512, batch_input_shape=batch_input_shape))
    model.add(tfe.keras.layers.Activation("relu"))

Instances