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Ringkasan
Penaksir TensorFlow didukung di TensorFlow, dan dapat dibuat dari model tf.keras baru dan yang sudah ada. Tutorial ini berisi contoh lengkap dan minimal dari proses tersebut.
Mempersiapkan
import tensorflow as tf
import numpy as np
import tensorflow_datasets as tfds
Buat model Keras sederhana.
Di Keras, Anda merakit lapisan untuk membuat model . Model adalah (biasanya) grafik lapisan. Jenis model yang paling umum adalah tumpukan lapisan: model tf.keras.Sequential .
Untuk membangun jaringan yang sederhana dan terhubung penuh (yaitu multi-layer perceptron):
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(16, activation='relu', input_shape=(4,)),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(3)
])
Kompilasi model dan dapatkan ringkasannya.
model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
optimizer='adam')
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 16) 80
dropout (Dropout) (None, 16) 0
dense_1 (Dense) (None, 3) 51
=================================================================
Total params: 131
Trainable params: 131
Non-trainable params: 0
_________________________________________________________________
Buat fungsi input
Gunakan Datasets API untuk menskalakan ke set data besar atau pelatihan multi-perangkat.
Penaksir membutuhkan kontrol kapan dan bagaimana saluran input mereka dibangun. Untuk mengizinkan ini, mereka memerlukan "Fungsi input" atau input_fn . Estimator akan memanggil fungsi ini tanpa argumen. input_fn harus mengembalikan tf.data.Dataset .
def input_fn():
split = tfds.Split.TRAIN
dataset = tfds.load('iris', split=split, as_supervised=True)
dataset = dataset.map(lambda features, labels: ({'dense_input':features}, labels))
dataset = dataset.batch(32).repeat()
return dataset
Uji input_fn Anda
for features_batch, labels_batch in input_fn().take(1):
print(features_batch)
print(labels_batch)
{'dense_input': <tf.Tensor: shape=(32, 4), dtype=float32, numpy=
array([[5.1, 3.4, 1.5, 0.2],
[7.7, 3. , 6.1, 2.3],
[5.7, 2.8, 4.5, 1.3],
[6.8, 3.2, 5.9, 2.3],
[5.2, 3.4, 1.4, 0.2],
[5.6, 2.9, 3.6, 1.3],
[5.5, 2.6, 4.4, 1.2],
[5.5, 2.4, 3.7, 1. ],
[4.6, 3.4, 1.4, 0.3],
[7.7, 2.8, 6.7, 2. ],
[7. , 3.2, 4.7, 1.4],
[4.6, 3.2, 1.4, 0.2],
[6.5, 3. , 5.2, 2. ],
[5.5, 4.2, 1.4, 0.2],
[5.4, 3.9, 1.3, 0.4],
[5. , 3.5, 1.3, 0.3],
[5.1, 3.8, 1.5, 0.3],
[4.8, 3. , 1.4, 0.1],
[6.5, 3. , 5.8, 2.2],
[7.6, 3. , 6.6, 2.1],
[6.7, 3.3, 5.7, 2.1],
[7.9, 3.8, 6.4, 2. ],
[6.7, 3. , 5.2, 2.3],
[5.8, 4. , 1.2, 0.2],
[6.3, 2.5, 5. , 1.9],
[5. , 3. , 1.6, 0.2],
[6.9, 3.1, 5.1, 2.3],
[6.1, 3. , 4.6, 1.4],
[5.8, 2.7, 4.1, 1. ],
[5.2, 2.7, 3.9, 1.4],
[6.7, 3. , 5. , 1.7],
[5.7, 2.6, 3.5, 1. ]], dtype=float32)>}
tf.Tensor([0 2 1 2 0 1 1 1 0 2 1 0 2 0 0 0 0 0 2 2 2 2 2 0 2 0 2 1 1 1 1 1], shape=(32,), dtype=int64)
Buat Penaksir dari model tf.keras.
tf.keras.Model dapat dilatih dengan tf.estimator API dengan mengonversi model menjadi objek tf.estimator.Estimator dengan tf.keras.estimator.model_to_estimator .
import tempfile
model_dir = tempfile.mkdtemp()
keras_estimator = tf.keras.estimator.model_to_estimator(
keras_model=model, model_dir=model_dir)
INFO:tensorflow:Using default config.
INFO:tensorflow:Using default config.
INFO:tensorflow:Using the Keras model provided.
INFO:tensorflow:Using the Keras model provided.
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/keras/backend.py:450: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.
warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmp2jzrjbqb', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmp2jzrjbqb', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
Latih dan evaluasi estimator.
keras_estimator.train(input_fn=input_fn, steps=500)
eval_result = keras_estimator.evaluate(input_fn=input_fn, steps=10)
print('Eval result: {}'.format(eval_result))
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:397: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:397: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmp/tmp2jzrjbqb/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={})
INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmp/tmp2jzrjbqb/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={})
INFO:tensorflow:Warm-starting from: /tmp/tmp2jzrjbqb/keras/keras_model.ckpt
INFO:tensorflow:Warm-starting from: /tmp/tmp2jzrjbqb/keras/keras_model.ckpt
INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES.
INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES.
INFO:tensorflow:Warm-started 4 variables.
INFO:tensorflow:Warm-started 4 variables.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp2jzrjbqb/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp2jzrjbqb/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 3.2731433, step = 0
INFO:tensorflow:loss = 3.2731433, step = 0
INFO:tensorflow:global_step/sec: 19.6463
INFO:tensorflow:global_step/sec: 19.6463
INFO:tensorflow:loss = 1.012466, step = 100 (5.092 sec)
INFO:tensorflow:loss = 1.012466, step = 100 (5.092 sec)
INFO:tensorflow:global_step/sec: 19.705
INFO:tensorflow:global_step/sec: 19.705
INFO:tensorflow:loss = 0.9225232, step = 200 (5.075 sec)
INFO:tensorflow:loss = 0.9225232, step = 200 (5.075 sec)
INFO:tensorflow:global_step/sec: 19.9236
INFO:tensorflow:global_step/sec: 19.9236
INFO:tensorflow:loss = 0.8686823, step = 300 (5.019 sec)
INFO:tensorflow:loss = 0.8686823, step = 300 (5.019 sec)
INFO:tensorflow:global_step/sec: 19.8862
INFO:tensorflow:global_step/sec: 19.8862
INFO:tensorflow:loss = 0.6412657, step = 400 (5.029 sec)
INFO:tensorflow:loss = 0.6412657, step = 400 (5.029 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 500...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 500...
INFO:tensorflow:Saving checkpoints for 500 into /tmp/tmp2jzrjbqb/model.ckpt.
INFO:tensorflow:Saving checkpoints for 500 into /tmp/tmp2jzrjbqb/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 500...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 500...
INFO:tensorflow:Loss for final step: 0.65391386.
INFO:tensorflow:Loss for final step: 0.65391386.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/keras/engine/training_v1.py:2057: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
updates = self.state_updates
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2022-01-26T06:39:31
INFO:tensorflow:Starting evaluation at 2022-01-26T06:39:31
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmp2jzrjbqb/model.ckpt-500
INFO:tensorflow:Restoring parameters from /tmp/tmp2jzrjbqb/model.ckpt-500
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.63967s
INFO:tensorflow:Inference Time : 0.63967s
INFO:tensorflow:Finished evaluation at 2022-01-26-06:39:31
INFO:tensorflow:Finished evaluation at 2022-01-26-06:39:31
INFO:tensorflow:Saving dict for global step 500: global_step = 500, loss = 0.6503415
INFO:tensorflow:Saving dict for global step 500: global_step = 500, loss = 0.6503415
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 500: /tmp/tmp2jzrjbqb/model.ckpt-500
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 500: /tmp/tmp2jzrjbqb/model.ckpt-500
Eval result: {'loss': 0.6503415, 'global_step': 500}