e648c769cba51a96bc581be2e4d0e44bf958134d,census/tf-keras/trainer/model.py,,create_keras_model,#,54

Before Change

          activation=tf.nn.relu,
          kernel_initializer="uniform",
          input_shape=(input_dim,)))
  model.add(tf.keras.layers.Dense(75, activation=tf.nn.relu))
  model.add(tf.keras.layers.Dense(50, activation=tf.nn.relu))
  model.add(tf.keras.layers.Dense(25, activation=tf.nn.relu))
  // The single output node and Sigmoid activation makes this a Logistic
  // Regression.

After Change

      [
          l.Dense(100, activation=tf.nn.relu, kernel_initializer="uniform",
                  input_shape=(input_dim,)),
          l.Dense(75, activation=tf.nn.relu),
          l.Dense(50, activation=tf.nn.relu),
          l.Dense(25, activation=tf.nn.relu),
          l.Dense(1, activation=tf.nn.sigmoid)

Instances