linear regression using TF(2)

batch,epoch,iteration batch_size, num_epochs

features = [tf.contrib.layers.real_valued_column("x", dimension=1)]
estimator = tf.contrib.learn.LinearRegressor(feature_columns=features)

x_train = np.array([1., 2., 3., 4.])
y_train = np.array([0., -1., -2., -3.])
x_eval = np.array([2., 5., 8., 1.])
y_eval = np.array([-1.01, -4.1, -7, 0.])

input_fn = tf.contrib.learn.io.numpy_input_fn({"x":x_train}, y_train,
                                              batch_size=4,
                                              num_epochs=1000)

eval_input_fn = tf.contrib.learn.io.numpy_input_fn(
    {"x":x_eval}, y_eval, batch_size=4, num_epochs=1000)

estimator.fit(input_fn=input_fn, steps=1000)
train_loss = estimator.evaluate(input_fn=input_fn)

• 这里看下log

# config 部分
{'_model_dir': None,
 '_save_checkpoints_secs': 600, 
'_num_ps_replicas': 0, 
'_keep_checkpoint_max': 5, 
'_tf_random_seed': None,
 '_task_type': None, 
'_environment': 'local',
 '_is_chief': True, 
'_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x125321510>, 
'_tf_config': gpu_options {per_process_gpu_memory_fraction: 1}
, '_num_worker_replicas': 0,
 '_task_id': 0,
 '_save_summary_steps': 100, 
'_save_checkpoints_steps': None,
 '_evaluation_master': '', '
_keep_checkpoint_every_n_hours': 10000,
 '_master': ''}

#warning 部分
model directory
attempt to expand dims
no longer supported

#checkpoints和loss的一些保存及打印
Create CheckpointSaverHook.
Saving checkpoints 
loss = 3.25, step = 1
global_step/sec: 661.358
Loss for final step: 6.06924e-10.

#evaluation信息和restore parameter
Starting evaluation at 2017-12-05-18:10:37
Restoring parameters from 
Finished evaluation at 2017-12-05-18:10:39
Saving dict for global step 1000: global_step = 1000, loss = 8.37181e-10
train loss: {'loss': 8.3718071e-10, 'global_step': 1000}
eval loss: {'loss': 0.0025272823, 'global_step': 1000}