Tensorflow2 Keras 调整单位和激活函数

Question

我正在尝试设置一个 Keras 调谐器来同时调整层数和激活函数。网络尝试将 2D 函数变形为另一个 2D 函数。我不断收到错误：

ValueError: Unknown activation function: sigmoidtanh

我的代码如下：

python

def euclidean_distance_loss(y_true, y_pred):
return tf.reduce_sum(tf.math.squared_difference(y_pred, y_true), axis=1)

def model_builder(hp):
model = tf.keras.Sequential()
tf.keras.layers.InputLayer(input_shape=(2,)),

#===Tune number of layers===#
for i in range(hp.Int('num_layers', 2, 5)):
    #===Tune number of units and activation in each layer===#
    hp_units = hp.Int('units_' + str(i), min_value=12, max_value=512, step=10)
    activation = hp.Choice('activation_' + str(i), ['linear', 'relu', 'selu', 'sigmoid', 'tanh'])
    model.add(tf.keras.layers.Dense(units=hp_units, activation=activation))

#===Add Final Layer===#
model.add(tf.keras.layers.Dense(units=2, activation='linear'))

#===Compile===#
model.compile(optimizer='adam', loss=euclidean_distance_loss, metrics=['MeanSquaredError'])

return model

b_tuner = BayesianOptimization( model_builder,
                            objective = 'val_mean_squared_error',
                            max_trials = 10,
                            executions_per_trial = 15,
                            directory = 'output',
                            project_name = 'b_tune_nn' )


b_tuner.search(x_train_points, y_train_points, epochs=25, validation_split=0.2)

现在如果我写：

    model.add(tf.keras.layers.Dense(units=hp_units, activation='tanh'))

一切正常。我在指定模型时做错了什么？

如果您需要更多上下文，请告诉我。

Answer 1

适合我的完整代码：

import kerastuner as kt
import tensorflow as tf
from kerastuner import BayesianOptimization
import numpy as np

x_train = np.random.randn(1000,20)
y_train = np.random.randn(1000,2)

def euclidean_distance_loss(y_true, y_pred):
  return tf.reduce_sum(tf.math.squared_difference(y_pred, y_true), axis=1)

def model_builder(hp):
  model = tf.keras.Sequential()
  tf.keras.layers.InputLayer(input_shape=(2,)),

  #===Tune number of layers===#
  for i in range(hp.Int('num_layers', 2, 5)):
    #===Tune number of units and activation in each layer===#
    hp_units = hp.Int('units_' + str(i), min_value=12, max_value=512, step=10)
    activation = hp.Choice('activation_' + str(i), ['linear', 'relu', 'selu', 'sigmoid', 'tanh'])
    model.add(tf.keras.layers.Dense(units=hp_units, activation=activation))
  #===Add Final Layer===#
  model.add(tf.keras.layers.Dense(units=2, activation='linear'))

  #===Compile===#
  model.compile(optimizer='adam', loss=euclidean_distance_loss, metrics=['MeanSquaredError'])

  return model


b_tuner = BayesianOptimization( model_builder,
                            objective = 'val_mean_squared_error',
                            max_trials = 10,
                            executions_per_trial = 15,
                            directory = 'output',
                            project_name = 'b_tune_nn' )


b_tuner.search(x_train, y_train, epochs=25, validation_split=0.2)

编辑：

pip uninstall keras-tuner
pip install -U keras-tuner # get the latest version