사용자 정의 학습: 자세히 둘러보기

pip install -q tfds-nightly

import os
import tensorflow as tf
import tensorflow_datasets as tfds
import matplotlib.pyplot as plt

print("TensorFlow version: {}".format(tf.__version__))
print("TensorFlow Datasets version: ",tfds.__version__)

2022-12-14 22:26:14.069508: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2022-12-14 22:26:14.069611: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2022-12-14 22:26:14.069620: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
TensorFlow version: 2.11.0
TensorFlow Datasets version:  4.7.0+nightly

ds_preview, info = tfds.load('penguins/simple', split='train', with_info=True)
df = tfds.as_dataframe(ds_preview.take(5), info)
print(df)
print(info.features)

body_mass_g  culmen_depth_mm  culmen_length_mm  flipper_length_mm  island  \
0       4200.0             13.9         45.500000              210.0       0   
1       4650.0             13.7         40.900002              214.0       0   
2       5300.0             14.2         51.299999              218.0       0   
3       5650.0             15.0         47.799999              215.0       0   
4       5050.0             15.8         46.299999              215.0       0   

   sex  species  
0    0        2  
1    0        2  
2    1        2  
3    1        2  
4    1        2  
FeaturesDict({
    'body_mass_g': float32,
    'culmen_depth_mm': float32,
    'culmen_length_mm': float32,
    'flipper_length_mm': float32,
    'island': ClassLabel(shape=(), dtype=int64, num_classes=3),
    'sex': ClassLabel(shape=(), dtype=int64, num_classes=3),
    'species': ClassLabel(shape=(), dtype=int64, num_classes=3),
})
2022-12-14 22:26:20.787197: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

class_names = ['Adélie', 'Chinstrap', 'Gentoo']

ds_split, info = tfds.load("penguins/processed", split=['train[:20%]', 'train[20%:]'], as_supervised=True, with_info=True)

ds_test = ds_split[0]
ds_train = ds_split[1]
assert isinstance(ds_test, tf.data.Dataset)

print(info.features)
df_test = tfds.as_dataframe(ds_test.take(5), info)
print("Test dataset sample: ")
print(df_test)

df_train = tfds.as_dataframe(ds_train.take(5), info)
print("Train dataset sample: ")
print(df_train)

ds_train_batch = ds_train.batch(32)

FeaturesDict({
    'features': Tensor(shape=(4,), dtype=float32),
    'species': ClassLabel(shape=(), dtype=int64, num_classes=3),
})
Test dataset sample: 
                                         features  species
0  [0.6545454, 0.22619048, 0.89830506, 0.6388889]        2
1        [0.36, 0.04761905, 0.6440678, 0.4027778]        2
2       [0.68, 0.30952382, 0.91525424, 0.6944444]        2
3   [0.6181818, 0.20238096, 0.8135593, 0.6805556]        2
4  [0.5527273, 0.26190478, 0.84745765, 0.7083333]        2
Train dataset sample: 
                                         features  species
0  [0.49818182, 0.6904762, 0.42372882, 0.4027778]        0
1      [0.48, 0.071428575, 0.6440678, 0.44444445]        2
2    [0.7236364, 0.9047619, 0.6440678, 0.5833333]        1
3  [0.34545454, 0.5833333, 0.33898306, 0.3472222]        0
4       [0.10909091, 0.75, 0.3559322, 0.41666666]        0
2022-12-14 22:26:21.858180: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
2022-12-14 22:26:22.013960: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

features, labels = next(iter(ds_train_batch))

print(features)
print(labels)

tf.Tensor(
[[0.49818182 0.6904762  0.42372882 0.4027778 ]
 [0.48       0.07142857 0.6440678  0.44444445]
 [0.7236364  0.9047619  0.6440678  0.5833333 ]
 [0.34545454 0.5833333  0.33898306 0.3472222 ]
 [0.10909091 0.75       0.3559322  0.41666666]
 [0.6690909  0.63095236 0.47457626 0.19444445]
 [0.8036364  0.9166667  0.4915254  0.44444445]
 [0.4909091  0.75       0.37288135 0.22916667]
 [0.33454546 0.85714287 0.37288135 0.2361111 ]
 [0.32       0.41666666 0.2542373  0.1388889 ]
 [0.41454545 0.5952381  0.5084746  0.19444445]
 [0.14909092 0.48809522 0.2542373  0.125     ]
 [0.23636363 0.4642857  0.27118644 0.05555556]
 [0.22181818 0.5952381  0.22033899 0.3472222 ]
 [0.24727273 0.5595238  0.15254237 0.25694445]
 [0.63272727 0.35714287 0.88135594 0.8194444 ]
 [0.47272727 0.15476191 0.6440678  0.4722222 ]
 [0.6036364  0.23809524 0.84745765 0.7361111 ]
 [0.26909092 0.5595238  0.27118644 0.16666667]
 [0.28       0.71428573 0.20338982 0.5416667 ]
 [0.10545454 0.5714286  0.33898306 0.2847222 ]
 [0.18545455 0.5952381  0.10169491 0.33333334]
 [0.47272727 0.16666667 0.7288136  0.6388889 ]
 [0.45090908 0.1904762  0.7118644  0.5972222 ]
 [0.49454546 0.5        0.3559322  0.25      ]
 [0.6363636  0.22619048 0.7457627  0.5694444 ]
 [0.08727273 0.5952381  0.2542373  0.05555556]
 [0.52       0.22619048 0.7457627  0.5555556 ]
 [0.5090909  0.23809524 0.7288136  0.6666667 ]
 [0.56       0.22619048 0.779661   0.625     ]
 [0.6363636  0.3452381  0.89830506 0.8333333 ]
 [0.15636364 0.47619048 0.20338982 0.04166667]], shape=(32, 4), dtype=float32)
tf.Tensor([0 2 1 0 0 1 1 1 0 1 1 0 0 0 0 2 2 2 0 0 0 0 2 2 1 2 0 2 2 2 2 0], shape=(32,), dtype=int64)
2022-12-14 22:26:22.169381: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

plt.scatter(features[:,0],
            features[:,2],
            c=labels,
            cmap='viridis')

plt.xlabel("Body Mass")
plt.ylabel("Culmen Length")
plt.show()

model = tf.keras.Sequential([
  tf.keras.layers.Dense(10, activation=tf.nn.relu, input_shape=(4,)),  # input shape required
  tf.keras.layers.Dense(10, activation=tf.nn.relu),
  tf.keras.layers.Dense(3)
])

predictions = model(features)
predictions[:5]

<tf.Tensor: shape=(5, 3), dtype=float32, numpy=
array([[-0.3334279 , -0.30740464,  0.23450832],
       [-0.13121836, -0.44709408,  0.23145097],
       [-0.45887685, -0.42732874,  0.33750302],
       [-0.26535368, -0.2485224 ,  0.18240781],
       [-0.24285898, -0.2467189 ,  0.15657528]], dtype=float32)>

tf.nn.softmax(predictions[:5])

<tf.Tensor: shape=(5, 3), dtype=float32, numpy=
array([[0.26378357, 0.27073818, 0.4654782 ],
       [0.315825  , 0.23028381, 0.45389122],
       [0.23531905, 0.24286126, 0.5218197 ],
       [0.27919087, 0.28392977, 0.43687937],
       [0.28676888, 0.28566414, 0.42756695]], dtype=float32)>

print("Prediction: {}".format(tf.math.argmax(predictions, axis=1)))
print("    Labels: {}".format(labels))

Prediction: [2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]
    Labels: [0 2 1 0 0 1 1 1 0 1 1 0 0 0 0 2 2 2 0 0 0 0 2 2 1 2 0 2 2 2 2 0]

loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

def loss(model, x, y, training):
  # training=training is needed only if there are layers with different
  # behavior during training versus inference (e.g. Dropout).
  y_ = model(x, training=training)

  return loss_object(y_true=y, y_pred=y_)

l = loss(model, features, labels, training=False)
print("Loss test: {}".format(l))

Loss test: 1.077925682067871

def grad(model, inputs, targets):
  with tf.GradientTape() as tape:
    loss_value = loss(model, inputs, targets, training=True)
  return loss_value, tape.gradient(loss_value, model.trainable_variables)

optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)

loss_value, grads = grad(model, features, labels)

print("Step: {}, Initial Loss: {}".format(optimizer.iterations.numpy(),
                                          loss_value.numpy()))

optimizer.apply_gradients(zip(grads, model.trainable_variables))

print("Step: {},         Loss: {}".format(optimizer.iterations.numpy(),
                                          loss(model, features, labels, training=True).numpy()))

Step: 0, Initial Loss: 1.077925682067871
WARNING:tensorflow:5 out of the last 5 calls to <function _BaseOptimizer._update_step_xla at 0x7f3f681ce670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:5 out of the last 5 calls to <function _BaseOptimizer._update_step_xla at 0x7f3f681ce670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:6 out of the last 6 calls to <function _BaseOptimizer._update_step_xla at 0x7f3f681ce670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:6 out of the last 6 calls to <function _BaseOptimizer._update_step_xla at 0x7f3f681ce670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
Step: 1,         Loss: 1.0767297744750977

## Note: Rerunning this cell uses the same model parameters

# Keep results for plotting
train_loss_results = []
train_accuracy_results = []

num_epochs = 201

for epoch in range(num_epochs):
  epoch_loss_avg = tf.keras.metrics.Mean()
  epoch_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()

  # Training loop - using batches of 32
  for x, y in ds_train_batch:
    # Optimize the model
    loss_value, grads = grad(model, x, y)
    optimizer.apply_gradients(zip(grads, model.trainable_variables))

    # Track progress
    epoch_loss_avg.update_state(loss_value)  # Add current batch loss
    # Compare predicted label to actual label
    # training=True is needed only if there are layers with different
    # behavior during training versus inference (e.g. Dropout).
    epoch_accuracy.update_state(y, model(x, training=True))

  # End epoch
  train_loss_results.append(epoch_loss_avg.result())
  train_accuracy_results.append(epoch_accuracy.result())

  if epoch % 50 == 0:
    print("Epoch {:03d}: Loss: {:.3f}, Accuracy: {:.3%}".format(epoch,
                                                                epoch_loss_avg.result(),
                                                                epoch_accuracy.result()))

Epoch 000: Loss: 1.071, Accuracy: 35.581%
Epoch 050: Loss: 0.688, Accuracy: 80.524%
Epoch 100: Loss: 0.437, Accuracy: 80.899%
Epoch 150: Loss: 0.319, Accuracy: 88.764%
Epoch 200: Loss: 0.231, Accuracy: 94.757%

fig, axes = plt.subplots(2, sharex=True, figsize=(12, 8))
fig.suptitle('Training Metrics')

axes[0].set_ylabel("Loss", fontsize=14)
axes[0].plot(train_loss_results)

axes[1].set_ylabel("Accuracy", fontsize=14)
axes[1].set_xlabel("Epoch", fontsize=14)
axes[1].plot(train_accuracy_results)
plt.show()

test_accuracy = tf.keras.metrics.Accuracy()
ds_test_batch = ds_test.batch(10)

for (x, y) in ds_test_batch:
  # training=False is needed only if there are layers with different
  # behavior during training versus inference (e.g. Dropout).
  logits = model(x, training=False)
  prediction = tf.math.argmax(logits, axis=1, output_type=tf.int64)
  test_accuracy(prediction, y)

print("Test set accuracy: {:.3%}".format(test_accuracy.result()))

Test set accuracy: 94.030%

tf.stack([y,prediction],axis=1)

<tf.Tensor: shape=(7, 2), dtype=int64, numpy=
array([[1, 1],
       [0, 0],
       [2, 2],
       [0, 0],
       [1, 0],
       [2, 2],
       [0, 0]])>

predict_dataset = tf.convert_to_tensor([
    [0.3, 0.8, 0.4, 0.5,],
    [0.4, 0.1, 0.8, 0.5,],
    [0.7, 0.9, 0.8, 0.4]
])

# training=False is needed only if there are layers with different
# behavior during training versus inference (e.g. Dropout).
predictions = model(predict_dataset, training=False)

for i, logits in enumerate(predictions):
  class_idx = tf.math.argmax(logits).numpy()
  p = tf.nn.softmax(logits)[class_idx]
  name = class_names[class_idx]
  print("Example {} prediction: {} ({:4.1f}%)".format(i, name, 100*p))

Example 0 prediction: Adélie (78.5%)
Example 1 prediction: Gentoo (98.3%)
Example 2 prediction: Chinstrap (76.2%)