小编use*_*662的帖子

我正在尝试将 LinearClassifier 与具有衰减学习率的 GradientDescentOptimizer 一起使用。

我的代码：

def main():
# load data
    features = np.load('data/feature_data.npz')
    tx = features['arr_0']
    y = features['arr_1']

## Prepare logistic regression
    n_point, n_feat = tx.shape

# Input functions
    def get_input_fn_from_numpy(tx, y, num_epochs=None, shuffle=True):
    # Preprocess data
        return tf.estimator.inputs.numpy_input_fn(
        x={"x":tx},
        y=y,
        num_epochs=num_epochs,
        shuffle=shuffle,
        batch_size=128
        )

    cols_label = "x"
    feature_cols = [tf.contrib.layers.real_valued_column(cols_label)]

    my_input_fn_train = get_input_fn_from_numpy(tx, y)

    model_dir = 'data/tmp/' + datetime.datetime.now().strftime("%m-%d_%H:%M:%S")
    global_step = tf.Variable(0, trainable=False)
    learning_rate=tf.train.exponential_decay(0.001*np.ones((20,1), dtype=np.float32), global_step, 10000, 0.95, staircase=False)
    regressor = tf.contrib.learn.LinearClassifier(feature_columns=feature_cols,
                                              model_dir=model_dir,
                                                  optimizer=tf.train.GradientDescentOptimizer(learning_rate=learning_rate))

    regressor.fit(input_fn=get_input_fn_from_numpy(tx_train, y_train), steps=100000) …