線形回帰 解析

GradientDescentOptimizerのlearning_rateを変えてみる

    # 勾配降下法
    learning_rate = 0.01
    train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)

    ...

    # トレーニング回数
    training_step = 5000
    validation_step = 10

AdamOptimizer

    # Optimizer
    train_op = tf.train.AdamOptimizer().minimize(loss)
    ...

    # トレーニング回数
    training_step = 10000
    validation_step = 10

AdadeltaOptimizer

    # Optimizer
    learning_rate = 0.5
    train_op = tf.train.AdadeltaOptimizer(learning_rate).minimize(loss)
    ...
     # トレーニング回数
    training_step = 10000
    validation_step = 10

AdagradOptimizer

    # Optimizer
    learning_rate = 0.025
    train_op = tf.train.AdagradOptimizer(learning_rate).minimize(loss)
    ...
     # トレーニング回数
    training_step = 10000
    validation_step = 10

MomentumOptimizer

    # Optimizer       
    learning_rate = 0.01
    momentum_rate = 0.01
    train_op = tf.train.MomentumOptimizer(learning_rate, momentum_rate).minimize(loss)
    ...
     # トレーニング回数
    training_step = 10000
    validation_step = 10